# pylint: disable=too-many-lines
# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
# --------------------------------------------------------------------------

from typing import Any, List, Optional, MutableMapping, Dict, Callable
from enum import Enum
from typing_extensions import Self
from azure.core import CaseInsensitiveEnumMeta
from .._generated._utils import serialization as _serialization
from .._generated.models import (
    LexicalAnalyzer,
    LexicalTokenizer,
    AnalyzeRequest,
    CustomAnalyzer as _CustomAnalyzer,
    EntityRecognitionSkillV3 as _EntityRecognitionSkillV3,
    PatternAnalyzer as _PatternAnalyzer,
    PatternTokenizer as _PatternTokenizer,
    SearchResourceEncryptionKey as _SearchResourceEncryptionKey,
    SearchIndexerDataSource as _SearchIndexerDataSource,
    SearchIndexerSkill,
    SearchIndexerSkillset as _SearchIndexerSkillset,
    SentimentSkillV3 as _SentimentSkillV3,
    SynonymMap as _SynonymMap,
    DataSourceCredentials,
    AzureActiveDirectoryApplicationCredentials,
    CognitiveServicesAccount,
    SearchIndexerKnowledgeStore,
    SearchIndexerIndexProjection,
    SearchIndexerDataContainer,
    DataChangeDetectionPolicy,
    DataDeletionDetectionPolicy,
    SearchIndexer as _SearchIndexer,
    IndexingSchedule,
    IndexingParameters,
    FieldMapping,
)

DELIMITER = "|"


class SearchIndexerSkillset(_serialization.Model):
    """A list of skills.

    All required parameters must be populated in order to send to Azure.

    :ivar name: The name of the skillset. Required.
    :vartype name: str
    :ivar description: The description of the skillset.
    :vartype description: str
    :ivar skills: A list of skills in the skillset. Required.
    :vartype skills: list[~azure.search.documents.indexes.models.SearchIndexerSkill]
    :ivar cognitive_services_account: Details about the Azure AI service to be used when running
     skills.
    :vartype cognitive_services_account: ~azure.search.documents.indexes.models.CognitiveServicesAccount
    :ivar knowledge_store: Definition of additional projections to Azure blob, table, or files, of
     enriched data.
    :vartype knowledge_store: ~azure.search.documents.indexes.models.SearchIndexerKnowledgeStore
    :ivar index_projection: Definition of additional projections to secondary search index(es).
    :vartype index_projection: ~azure.search.documents.indexes.models.SearchIndexerIndexProjection
    :ivar e_tag: The ETag of the skillset.
    :vartype e_tag: str
    :ivar encryption_key: A description of an encryption key that you create in Azure Key Vault.
     This key is used to provide an additional level of encryption-at-rest for your skillset
     definition when you want full assurance that no one, not even Microsoft, can decrypt your
     skillset definition. Once you have encrypted your skillset definition, it will always remain
     encrypted. The search service will ignore attempts to set this property to null. You can change
     this property as needed if you want to rotate your encryption key; Your skillset definition
     will be unaffected. Encryption with customer-managed keys is not available for free search
     services, and is only available for paid services created on or after January 1, 2019.
    :vartype encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
    """

    def __init__(
        self,
        *,
        name: str,
        skills: List["SearchIndexerSkill"],
        description: Optional[str] = None,
        cognitive_services_account: Optional["CognitiveServicesAccount"] = None,
        knowledge_store: Optional["SearchIndexerKnowledgeStore"] = None,
        index_projection: Optional["SearchIndexerIndexProjection"] = None,
        e_tag: Optional[str] = None,
        encryption_key: Optional["SearchResourceEncryptionKey"] = None,
        **kwargs: Any
    ) -> None:
        super().__init__(**kwargs)
        self.name = name
        self.description = description
        self.skills = skills
        self.cognitive_services_account = cognitive_services_account
        self.knowledge_store = knowledge_store
        self.index_projection = index_projection
        self.e_tag = e_tag
        self.encryption_key = encryption_key

    def _to_generated(self):
        generated_skills = []
        for skill in self.skills:
            if hasattr(skill, "_to_generated"):
                generated_skills.append(
                    skill._to_generated()  # pylint:disable=protected-access
                )
            else:
                generated_skills.append(skill)
        assert len(generated_skills) == len(self.skills)
        encryption_key = getattr(self, "encryption_key", None)
        return _SearchIndexerSkillset(
            name=getattr(self, "name", None),
            description=getattr(self, "description", None),
            skills=generated_skills,
            cognitive_services_account=getattr(
                self, "cognitive_services_account", None
            ),
            knowledge_store=getattr(self, "knowledge_store", None),
            index_projection=getattr(self, "index_projection", None),
            e_tag=getattr(self, "e_tag", None),
            encryption_key=(
                encryption_key._to_generated()  # pylint:disable=protected-access
                if encryption_key
                else None
            ),
        )

    @classmethod
    def _from_generated(cls, skillset) -> Optional[Self]:
        if not skillset:
            return None
        custom_skills = []
        for skill in skillset.skills:
            skill_cls = type(skill)
            if skill_cls in [_EntityRecognitionSkillV3]:
                custom_skills.append(
                    EntityRecognitionSkill._from_generated(  # pylint:disable=protected-access
                        skill
                    )
                )
            elif skill_cls in [_SentimentSkillV3]:
                custom_skills.append(
                    SentimentSkill._from_generated(  # pylint:disable=protected-access
                        skill
                    )
                )
            else:
                custom_skills.append(skill)
        assert len(skillset.skills) == len(custom_skills)
        kwargs = skillset.as_dict()
        # pylint:disable=protected-access
        kwargs["encryption_key"] = SearchResourceEncryptionKey._from_generated(
            skillset.encryption_key
        )
        kwargs["skills"] = custom_skills
        return cls(**kwargs)

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a SearchIndexerSkillset instance.

        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchIndexerSkillset instance
        :rtype: SearchIndexerSkillset
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchIndexerSkillset.deserialize(data, content_type=content_type)
        )

    def as_dict(
        self,
        keep_readonly: bool = True,
        key_transformer: Callable[
            [str, Dict[str, Any], Any], Any
        ] = _serialization.attribute_transformer,
        **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return a dict that can be serialized using json.dump.

        :param bool keep_readonly: If you want to serialize the readonly attributes
        :param Callable key_transformer: A callable that will transform the key of the dict
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().as_dict(  # type: ignore
            keep_readonly=keep_readonly, key_transformer=key_transformer, **kwargs
        )

    @classmethod
    def from_dict(  # type: ignore
        cls,
        data: Any,
        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
        content_type: Optional[str] = None,
    ) -> Optional[Self]:
        """Parse a dict using given key extractor return a model.

        By default consider key
        extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor
        and last_rest_key_case_insensitive_extractor)

        :param dict data: A dict using RestAPI structure
        :param Callable key_extractors: A callable that will extract a key from a dict
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchIndexerSkillset instance
        :rtype: SearchIndexerSkillset
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(  # type: ignore
            _SearchIndexerSkillset.from_dict(
                data, content_type=content_type, key_extractors=key_extractors
            )
        )


class EntityRecognitionSkillVersion(str, Enum, metaclass=CaseInsensitiveEnumMeta):
    """Specifies the Entity Recognition skill version to use."""

    V1 = "#Microsoft.Skills.Text.EntityRecognitionSkill"
    #: Use Entity Recognition skill V3.
    V3 = "#Microsoft.Skills.Text.V3.EntityRecognitionSkill"
    #: Use latest version of Entity Recognition skill.
    LATEST = V3


class EntityRecognitionSkill(SearchIndexerSkill):
    """Using the Text Analytics API, extracts entities of different types from text.

    All required parameters must be populated in order to send to Azure.

    :ivar odata_type: Required. Identifies the concrete type of the skill.Constant filled by
     server.
    :vartype odata_type: str
    :ivar name: The name of the skill which uniquely identifies it within the skillset. A skill
     with no name defined will be given a default name of its 1-based index in the skills array,
     prefixed with the character '#'.
    :vartype name: str
    :ivar description: The description of the skill which describes the inputs, outputs, and usage
     of the skill.
    :vartype description: str
    :ivar context: Represents the level at which operations take place, such as the document root
     or document content (for example, /document or /document/content). The default is /document.
    :vartype context: str
    :ivar inputs: Required. Inputs of the skills could be a column in the source data set, or the
     output of an upstream skill.
    :vartype inputs: list[~azure.search.documents.indexes.models.InputFieldMappingEntry]
    :ivar outputs: Required. The output of a skill is either a field in a search index, or a value
     that can be consumed as an input by another skill.
    :vartype outputs: list[~azure.search.documents.indexes.models.OutputFieldMappingEntry]
    :ivar categories: A list of entity categories that should be extracted.
    :vartype categories: list[str or ~azure.search.documents.indexes.models.EntityCategory]
    :ivar default_language_code: A value indicating which language code to use. Default is en.
     Possible values include: "ar", "cs", "zh-Hans", "zh-Hant", "da", "nl", "en", "fi", "fr", "de",
     "el", "hu", "it", "ja", "ko", "no", "pl", "pt-PT", "pt-BR", "ru", "es", "sv", "tr".
    :vartype default_language_code: str or
     ~azure.search.documents.indexes.models.EntityRecognitionSkillLanguage
    :ivar include_typeless_entities: Determines whether or not to include entities which are well
     known but don't conform to a pre-defined type. If this configuration is not set (default), set
     to null or set to false, entities which don't conform to one of the pre-defined types will not
     be surfaced. Only valid for skill version 1.
    :vartype include_typeless_entities: bool
    :ivar minimum_precision: A value between 0 and 1 that be used to only include entities whose
     confidence score is greater than the value specified. If not set (default), or if explicitly
     set to null, all entities will be included.
    :vartype minimum_precision: float
    :ivar model_version: The version of the model to use when calling the Text Analytics service.
     It will default to the latest available when not specified. We recommend you do not specify
     this value unless absolutely necessary. Only valid from skill version 3.
    :vartype model_version: str
    :ivar skill_version: The version of the skill to use when calling the Text Analytics service.
     It will default to V1 when not specified.
    :vartype skill_version: ~azure.search.documents.indexes.models.EntityRecognitionSkillVersion
    """

    _validation = {
        "odata_type": {"required": True},
        "inputs": {"required": True},
        "outputs": {"required": True},
        "minimum_precision": {"maximum": 1, "minimum": 0},
    }

    _attribute_map = {
        "odata_type": {"key": "@odata\\.type", "type": "str"},
        "name": {"key": "name", "type": "str"},
        "description": {"key": "description", "type": "str"},
        "context": {"key": "context", "type": "str"},
        "inputs": {"key": "inputs", "type": "[InputFieldMappingEntry]"},
        "outputs": {"key": "outputs", "type": "[OutputFieldMappingEntry]"},
        "categories": {"key": "categories", "type": "[str]"},
        "default_language_code": {"key": "defaultLanguageCode", "type": "str"},
        "include_typeless_entities": {"key": "includeTypelessEntities", "type": "bool"},
        "minimum_precision": {"key": "minimumPrecision", "type": "float"},
        "model_version": {"key": "modelVersion", "type": "str"},
        "skill_version": {"key": "skillVersion", "type": "str"},
    }

    def __init__(self, **kwargs):
        # pop skill_version from kwargs to avoid warning in msrest
        skill_version = kwargs.pop("skill_version", EntityRecognitionSkillVersion.V3)

        super(EntityRecognitionSkill, self).__init__(**kwargs)
        self.skill_version = skill_version
        self.odata_type = self.skill_version
        self.categories = kwargs.get("categories", None)
        self.default_language_code = kwargs.get("default_language_code", None)
        self.minimum_precision = kwargs.get("minimum_precision", None)
        self.include_typeless_entities = kwargs.get("include_typeless_entities", None)
        self.model_version = kwargs.get("model_version", None)

    def _to_generated(self):
        if self.skill_version in [
            EntityRecognitionSkillVersion.V3,
            EntityRecognitionSkillVersion.LATEST,
        ]:
            return _EntityRecognitionSkillV3(
                inputs=self.inputs,
                outputs=self.outputs,
                name=self.name,
                description=self.description,
                context=self.context,
                odata_type=self.odata_type,
                categories=self.categories,
                default_language_code=self.default_language_code,
                minimum_precision=self.minimum_precision,
                model_version=self.model_version,
            )
        return None

    @classmethod
    def _from_generated(cls, skill):
        if not skill:
            return None
        kwargs = skill.as_dict()
        if isinstance(skill, _EntityRecognitionSkillV3):
            return EntityRecognitionSkill(
                skill_version=EntityRecognitionSkillVersion.V3, **kwargs
            )
        return None


class SentimentSkillVersion(str, Enum, metaclass=CaseInsensitiveEnumMeta):
    """Specifies the Sentiment Skill version to use."""

    V1 = "#Microsoft.Skills.Text.SentimentSkill"
    #: Use Sentiment skill V3.
    V3 = "#Microsoft.Skills.Text.V3.SentimentSkill"
    #: Use latest version of Sentiment skill.
    LATEST = V3


class SentimentSkill(SearchIndexerSkill):
    """V1: Text analytics positive-negative sentiment analysis, scored as a floating point value in a range of zero
    to 1.
    V3: Using the Text Analytics API, evaluates unstructured text and for each record, provides sentiment labels
    (such as "negative", "neutral" and "positive") based on the highest confidence score found by the service at
    a sentence and document-level.

    All required parameters must be populated in order to send to Azure.

    :ivar odata_type: Required. Identifies the concrete type of the skill.Constant filled by
     server.
    :vartype odata_type: str
    :ivar name: The name of the skill which uniquely identifies it within the skillset. A skill
     with no name defined will be given a default name of its 1-based index in the skills array,
     prefixed with the character '#'.
    :vartype name: str
    :ivar description: The description of the skill which describes the inputs, outputs, and usage
     of the skill.
    :vartype description: str
    :ivar context: Represents the level at which operations take place, such as the document root
     or document content (for example, /document or /document/content). The default is /document.
    :vartype context: str
    :ivar inputs: Required. Inputs of the skills could be a column in the source data set, or the
     output of an upstream skill.
    :vartype inputs: list[~azure.search.documents.indexes.models.InputFieldMappingEntry]
    :ivar outputs: Required. The output of a skill is either a field in a search index, or a value
     that can be consumed as an input by another skill.
    :vartype outputs: list[~azure.search.documents.indexes.models.OutputFieldMappingEntry]
    :ivar default_language_code: A value indicating which language code to use. Default is en.
     Possible values include: "da", "nl", "en", "fi", "fr", "de", "el", "it", "no", "pl", "pt-PT",
     "ru", "es", "sv", "tr".
    :vartype default_language_code: str or
     ~azure.search.documents.indexes.models.SentimentSkillLanguage
    :ivar include_opinion_mining: If set to true, the skill output will include information from
     Text Analytics for opinion mining, namely targets (nouns or verbs) and their associated
     assessment (adjective) in the text. Default is false.
    :vartype include_opinion_mining: bool
    :ivar model_version: The version of the model to use when calling the Text Analytics service.
     It will default to the latest available when not specified. We recommend you do not specify
     this value unless absolutely necessary.
    :vartype model_version: str
    :ivar skill_version: The version of the skill to use when calling the Text Analytics service.
     It will default to V1 when not specified.
    :vartype skill_version: ~azure.search.documents.indexes.models.SentimentSkillVersion
    """

    _validation = {
        "odata_type": {"required": True},
        "inputs": {"required": True},
        "outputs": {"required": True},
    }

    _attribute_map = {
        "odata_type": {"key": "@odata\\.type", "type": "str"},
        "name": {"key": "name", "type": "str"},
        "description": {"key": "description", "type": "str"},
        "context": {"key": "context", "type": "str"},
        "inputs": {"key": "inputs", "type": "[InputFieldMappingEntry]"},
        "outputs": {"key": "outputs", "type": "[OutputFieldMappingEntry]"},
        "default_language_code": {"key": "defaultLanguageCode", "type": "str"},
        "include_opinion_mining": {"key": "includeOpinionMining", "type": "bool"},
        "model_version": {"key": "modelVersion", "type": "str"},
        "skill_version": {"key": "skillVersion", "type": "str"},
    }

    def __init__(self, **kwargs):
        # pop skill_version from kwargs to avoid warning in msrest
        skill_version = kwargs.pop("skill_version", SentimentSkillVersion.V3)

        super(SentimentSkill, self).__init__(**kwargs)
        self.skill_version = skill_version
        self.odata_type = self.skill_version
        self.default_language_code = kwargs.get("default_language_code", None)
        self.include_opinion_mining = kwargs.get("include_opinion_mining", None)
        self.model_version = kwargs.get("model_version", None)

    def _to_generated(self):
        if self.skill_version in [
            SentimentSkillVersion.V3,
            SentimentSkillVersion.LATEST,
        ]:
            return _SentimentSkillV3(
                inputs=self.inputs,
                outputs=self.outputs,
                name=self.name,
                description=self.description,
                context=self.context,
                odata_type=self.odata_type,
                default_language_code=self.default_language_code,
                include_opinion_mining=self.include_opinion_mining,
                model_version=self.model_version,
            )
        return None

    @classmethod
    def _from_generated(cls, skill):
        if not skill:
            return None
        kwargs = skill.as_dict()
        if isinstance(skill, _SentimentSkillV3):
            return SentimentSkill(skill_version=SentimentSkillVersion.V3, **kwargs)
        return None


class AnalyzeTextOptions(_serialization.Model):
    """Specifies some text and analysis components used to break that text into tokens.

    All required parameters must be populated in order to send to Azure.

    :ivar text: Required. The text to break into tokens.
    :vartype text: str
    :ivar analyzer_name: The name of the analyzer to use to break the given text. If this parameter is
     not specified, you must specify a tokenizer instead. The tokenizer and analyzer parameters are
     mutually exclusive. Possible values include: "ar.microsoft", "ar.lucene", "hy.lucene",
     "bn.microsoft", "eu.lucene", "bg.microsoft", "bg.lucene", "ca.microsoft", "ca.lucene", "zh-
     Hans.microsoft", "zh-Hans.lucene", "zh-Hant.microsoft", "zh-Hant.lucene", "hr.microsoft",
     "cs.microsoft", "cs.lucene", "da.microsoft", "da.lucene", "nl.microsoft", "nl.lucene",
     "en.microsoft", "en.lucene", "et.microsoft", "fi.microsoft", "fi.lucene", "fr.microsoft",
     "fr.lucene", "gl.lucene", "de.microsoft", "de.lucene", "el.microsoft", "el.lucene",
     "gu.microsoft", "he.microsoft", "hi.microsoft", "hi.lucene", "hu.microsoft", "hu.lucene",
     "is.microsoft", "id.microsoft", "id.lucene", "ga.lucene", "it.microsoft", "it.lucene",
     "ja.microsoft", "ja.lucene", "kn.microsoft", "ko.microsoft", "ko.lucene", "lv.microsoft",
     "lv.lucene", "lt.microsoft", "ml.microsoft", "ms.microsoft", "mr.microsoft", "nb.microsoft",
     "no.lucene", "fa.lucene", "pl.microsoft", "pl.lucene", "pt-BR.microsoft", "pt-BR.lucene", "pt-
     PT.microsoft", "pt-PT.lucene", "pa.microsoft", "ro.microsoft", "ro.lucene", "ru.microsoft",
     "ru.lucene", "sr-cyrillic.microsoft", "sr-latin.microsoft", "sk.microsoft", "sl.microsoft",
     "es.microsoft", "es.lucene", "sv.microsoft", "sv.lucene", "ta.microsoft", "te.microsoft",
     "th.microsoft", "th.lucene", "tr.microsoft", "tr.lucene", "uk.microsoft", "ur.microsoft",
     "vi.microsoft", "standard.lucene", "standardasciifolding.lucene", "keyword", "pattern",
     "simple", "stop", "whitespace".
    :vartype analyzer_name: str or ~azure.search.documents.indexes.models.LexicalAnalyzerName
    :ivar tokenizer_name: The name of the tokenizer to use to break the given text. If this parameter
     is not specified, you must specify an analyzer instead. The tokenizer and analyzer parameters
     are mutually exclusive. Possible values include: "classic", "edgeNGram", "keyword_v2",
     "letter", "lowercase", "microsoft_language_tokenizer", "microsoft_language_stemming_tokenizer",
     "nGram", "path_hierarchy_v2", "pattern", "standard_v2", "uax_url_email", "whitespace".
    :vartype tokenizer_name: str or ~azure.search.documents.indexes.models.LexicalTokenizerName
    :ivar normalizer_name: The name of the normalizer to use to normalize the given text. Known values
     are: "asciifolding", "elision", "lowercase", "standard", and "uppercase".
    :vartype normalizer_name: str or ~azure.search.documents.indexes.models.LexicalNormalizerName
    :ivar token_filters: An optional list of token filters to use when breaking the given text.
     This parameter can only be set when using the tokenizer parameter.
    :vartype token_filters: list[str or ~azure.search.documents.indexes.models.TokenFilterName]
    :ivar char_filters: An optional list of character filters to use when breaking the given text.
     This parameter can only be set when using the tokenizer parameter.
    :vartype char_filters: list[str]
    """

    def __init__(
        self,
        *,
        text: str,
        analyzer_name: Optional[str] = None,
        tokenizer_name: Optional[str] = None,
        normalizer_name: Optional[str] = None,
        token_filters: Optional[List[str]] = None,
        char_filters: Optional[List[str]] = None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.text = text
        self.analyzer_name = analyzer_name
        self.tokenizer_name = tokenizer_name
        self.normalizer_name = normalizer_name
        self.token_filters = token_filters
        self.char_filters = char_filters

    def _to_analyze_request(self):
        return AnalyzeRequest(
            text=self.text,
            analyzer=self.analyzer_name,
            tokenizer=self.tokenizer_name,
            normalizer=self.normalizer_name,
            token_filters=self.token_filters,
            char_filters=self.char_filters,
        )

    @classmethod
    def _from_analyze_request(cls, analyze_request) -> Self:
        return cls(
            text=analyze_request.text,
            analyzer_name=analyze_request.analyzer,
            tokenizer_name=analyze_request.tokenizer,
            normalizer_name=analyze_request.normalizer,
            token_filters=analyze_request.token_filters,
            char_filters=analyze_request.char_filters,
        )

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_analyze_request().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a AnalyzeTextOptions instance.

        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A AnalyzeTextOptions instance
        :rtype: AnalyzeTextOptions
        :raises: DeserializationError if something went wrong
        """
        return cls._from_analyze_request(
            AnalyzeRequest.deserialize(data, content_type=content_type)
        )

    def as_dict(
        self,
        keep_readonly: bool = True,
        key_transformer: Callable[
            [str, Dict[str, Any], Any], Any
        ] = _serialization.attribute_transformer,
        **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return a dict that can be serialized using json.dump.

        :param bool keep_readonly: If you want to serialize the readonly attributes
        :param Callable key_transformer: A callable that will transform the key of the dict
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_analyze_request().as_dict(  # type: ignore
            keep_readonly=keep_readonly, key_transformer=key_transformer, **kwargs
        )

    @classmethod
    def from_dict(  # type: ignore
        cls,
        data: Any,
        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
        content_type: Optional[str] = None,
    ) -> Optional[Self]:
        """Parse a dict using given key extractor return a model.

        By default consider key
        extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor
        and last_rest_key_case_insensitive_extractor)

        :param dict data: A dict using RestAPI structure
        :param Callable key_extractors: A callable that will extract a key from a dict
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A AnalyzeTextOptions instance
        :rtype: AnalyzeTextOptions
        :raises: DeserializationError if something went wrong
        """
        return cls._from_analyze_request(
            AnalyzeRequest.from_dict(
                data, content_type=content_type, key_extractors=key_extractors
            )
        )


class CustomAnalyzer(LexicalAnalyzer):
    """Allows you to take control over the process of converting text into indexable/searchable tokens.
    It's a user-defined configuration consisting of a single predefined tokenizer and one or more filters.
    The tokenizer is responsible for breaking text into tokens, and the filters for modifying tokens
    emitted by the tokenizer.

    All required parameters must be populated in order to send to Azure.

    :ivar odata_type: Required. Identifies the concrete type of the analyzer.Constant filled by
     server.
    :vartype odata_type: str
    :ivar name: Required. The name of the analyzer. It must only contain letters, digits, spaces,
     dashes or underscores, can only start and end with alphanumeric characters, and is limited to
     128 characters.
    :vartype name: str
    :ivar tokenizer_name: Required. The name of the tokenizer to use to divide continuous text into a
     sequence of tokens, such as breaking a sentence into words. Possible values include: "classic",
     "edgeNGram", "keyword_v2", "letter", "lowercase", "microsoft_language_tokenizer",
     "microsoft_language_stemming_tokenizer", "nGram", "path_hierarchy_v2", "pattern",
     "standard_v2", "uax_url_email", "whitespace".
    :vartype tokenizer_name: str or ~azure.search.documents.indexes.models.LexicalTokenizerName
    :ivar token_filters: A list of token filters used to filter out or modify the tokens generated
     by a tokenizer. For example, you can specify a lowercase filter that converts all characters to
     lowercase. The filters are run in the order in which they are listed.
    :vartype token_filters: list[str or ~azure.search.documents.indexes.models.TokenFilterName]
    :ivar char_filters: A list of character filters used to prepare input text before it is
     processed by the tokenizer. For instance, they can replace certain characters or symbols. The
     filters are run in the order in which they are listed.
    :vartype char_filters: list[str]
    """

    _validation = {
        "odata_type": {"required": True},
        "name": {"required": True},
        "tokenizer_name": {"required": True},
    }

    _attribute_map = {
        "odata_type": {"key": "@odata\\.type", "type": "str"},
        "name": {"key": "name", "type": "str"},
        "tokenizer_name": {"key": "tokenizerName", "type": "str"},
        "token_filters": {"key": "tokenFilters", "type": "[str]"},
        "char_filters": {"key": "charFilters", "type": "[str]"},
    }

    def __init__(self, **kwargs):
        super(CustomAnalyzer, self).__init__(**kwargs)
        self.odata_type = "#Microsoft.Azure.Search.CustomAnalyzer"
        self.tokenizer_name = kwargs["tokenizer_name"]
        self.token_filters = kwargs.get("token_filters", None)
        self.char_filters = kwargs.get("char_filters", None)

    def _to_generated(self):
        return _CustomAnalyzer(
            name=self.name,
            odata_type=self.odata_type,
            tokenizer=self.tokenizer_name,
            token_filters=self.token_filters,
            char_filters=self.char_filters,
        )

    @classmethod
    def _from_generated(cls, custom_analyzer):
        if not custom_analyzer:
            return None
        return cls(
            name=custom_analyzer.name,
            odata_type=custom_analyzer.odata_type,
            tokenizer_name=custom_analyzer.tokenizer,
            token_filters=custom_analyzer.token_filters,
            char_filters=custom_analyzer.char_filters,
        )


class PatternAnalyzer(LexicalAnalyzer):
    """Flexibly separates text into terms via a regular expression.
    This analyzer is implemented using Apache Lucene.

    All required parameters must be populated in order to send to Azure.

    :ivar name: Required. The name of the analyzer. It must only contain letters, digits, spaces,
     dashes or underscores, can only start and end with alphanumeric characters, and is limited to
     128 characters.
    :vartype name: str
    :ivar lower_case_terms: A value indicating whether terms should be lower-cased. Default is
     true.
    :vartype lower_case_terms: bool
    :ivar pattern: A regular expression to match token separators. Default is an
     expression that matches one or more white space characters.
    :vartype pattern: str
    :ivar flags: List of regular expression flags. Possible values of each flag include: 'CANON_EQ',
     'CASE_INSENSITIVE', 'COMMENTS', 'DOTALL', 'LITERAL', 'MULTILINE', 'UNICODE_CASE', 'UNIX_LINES'.
    :vartype flags: list[str] or list[~azure.search.documents.indexes.models.RegexFlags]
    :ivar stopwords: A list of stopwords.
    :vartype stopwords: list[str]
    """

    _validation = {"odata_type": {"required": True}, "name": {"required": True}}

    _attribute_map = {
        "odata_type": {"key": "@odata\\.type", "type": "str"},
        "name": {"key": "name", "type": "str"},
        "lower_case_terms": {"key": "lowercase", "type": "bool"},
        "pattern": {"key": "pattern", "type": "str"},
        "flags": {"key": "flags", "type": "[str]"},
        "stopwords": {"key": "stopwords", "type": "[str]"},
    }

    def __init__(self, **kwargs):
        super(PatternAnalyzer, self).__init__(**kwargs)
        self.odata_type = "#Microsoft.Azure.Search.PatternAnalyzer"
        self.lower_case_terms = kwargs.get("lower_case_terms", True)
        self.pattern = kwargs.get("pattern", r"\W+")
        self.flags = kwargs.get("flags", None)
        self.stopwords = kwargs.get("stopwords", None)

    def _to_generated(self):
        if not self.flags:
            flags = None
        else:
            flags = DELIMITER.join(self.flags)
        return _PatternAnalyzer(
            name=self.name,
            lower_case_terms=self.lower_case_terms,
            pattern=self.pattern,
            flags=flags,
            stopwords=self.stopwords,
        )

    @classmethod
    def _from_generated(cls, pattern_analyzer):
        if not pattern_analyzer:
            return None
        if not pattern_analyzer.flags:
            flags = None
        else:
            flags = pattern_analyzer.flags.split(DELIMITER)
        return cls(
            name=pattern_analyzer.name,
            lower_case_terms=pattern_analyzer.lower_case_terms,
            pattern=pattern_analyzer.pattern,
            flags=flags,
            stopwords=pattern_analyzer.stopwords,
        )


class PatternTokenizer(LexicalTokenizer):
    """Tokenizer that uses regex pattern matching to construct distinct tokens.
    This tokenizer is implemented using Apache Lucene.

    All required parameters must be populated in order to send to Azure.

    :ivar name: Required. The name of the tokenizer. It must only contain letters, digits, spaces,
     dashes or underscores, can only start and end with alphanumeric characters, and is limited to
     128 characters.
    :vartype name: str
    :ivar pattern: A regular expression to match token separators. Default is an
     expression that matches one or more white space characters.
    :vartype pattern: str
    :ivar flags: List of regular expression flags. Possible values of each flag include: 'CANON_EQ',
     'CASE_INSENSITIVE', 'COMMENTS', 'DOTALL', 'LITERAL', 'MULTILINE', 'UNICODE_CASE', 'UNIX_LINES'.
    :vartype flags: list[str] or list[~azure.search.documents.indexes.models.RegexFlags]
    :ivar group: The zero-based ordinal of the matching group in the regular expression to
     extract into tokens. Use -1 if you want to use the entire pattern to split the input into
     tokens, irrespective of matching groups. Default is -1.
    :vartype group: int
    """

    _validation = {"odata_type": {"required": True}, "name": {"required": True}}

    _attribute_map = {
        "odata_type": {"key": "@odata\\.type", "type": "str"},
        "name": {"key": "name", "type": "str"},
        "pattern": {"key": "pattern", "type": "str"},
        "flags": {"key": "flags", "type": "[str]"},
        "group": {"key": "group", "type": "int"},
    }

    def __init__(self, **kwargs):
        super(PatternTokenizer, self).__init__(**kwargs)
        self.odata_type = "#Microsoft.Azure.Search.PatternTokenizer"
        self.pattern = kwargs.get("pattern", r"\W+")
        self.flags = kwargs.get("flags", None)
        self.group = kwargs.get("group", -1)

    def _to_generated(self):
        if not self.flags:
            flags = None
        else:
            flags = DELIMITER.join(self.flags)
        return _PatternTokenizer(
            name=self.name,
            pattern=self.pattern,
            flags=flags,
            group=self.group,
        )

    @classmethod
    def _from_generated(cls, pattern_tokenizer):
        if not pattern_tokenizer:
            return None
        if not pattern_tokenizer.flags:
            flags = None
        else:
            flags = pattern_tokenizer.flags.split(DELIMITER)
        return cls(
            name=pattern_tokenizer.name,
            pattern=pattern_tokenizer.pattern,
            flags=flags,
            group=pattern_tokenizer.group,
        )


class SearchResourceEncryptionKey(_serialization.Model):
    """A customer-managed encryption key in Azure Key Vault. Keys that you create and manage can be
    used to encrypt or decrypt data-at-rest in Azure Cognitive Search, such as indexes and synonym maps.

    All required parameters must be populated in order to send to Azure.

    :ivar key_name: Required. The name of your Azure Key Vault key to be used to encrypt your data
     at rest.
    :vartype key_name: str
    :ivar key_version: Required. The version of your Azure Key Vault key to be used to encrypt
     your data at rest.
    :vartype key_version: str
    :ivar vault_uri: Required. The URI of your Azure Key Vault, also referred to as DNS name, that
     contains the key to be used to encrypt your data at rest. An example URI might be https://my-
     keyvault-name.vault.azure.net.
    :vartype vault_uri: str
    :ivar application_id: Required. An AAD Application ID that was granted the required access
     permissions to the Azure Key Vault that is to be used when encrypting your data at rest. The
     Application ID should not be confused with the Object ID for your AAD Application.
    :vartype application_id: str
    :ivar application_secret: The authentication key of the specified AAD application.
    :vartype application_secret: str
    """

    def __init__(
        self,
        *,
        key_name: str,
        key_version: str,
        vault_uri: str,
        application_id: Optional[str] = None,
        application_secret: Optional[str] = None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.key_name = key_name
        self.key_version = key_version
        self.vault_uri = vault_uri
        self.application_id = application_id
        self.application_secret = application_secret

    def _to_generated(self):
        if self.application_id and self.application_secret:
            access_credentials = AzureActiveDirectoryApplicationCredentials(
                application_id=self.application_id,
                application_secret=self.application_secret,
            )
        else:
            access_credentials = None
        return _SearchResourceEncryptionKey(
            key_name=self.key_name,
            key_version=self.key_version,
            vault_uri=self.vault_uri,
            access_credentials=access_credentials,
        )

    @classmethod
    def _from_generated(cls, search_resource_encryption_key) -> Optional[Self]:
        if not search_resource_encryption_key:
            return None
        if search_resource_encryption_key.access_credentials:
            application_id = (
                search_resource_encryption_key.access_credentials.application_id
            )
            application_secret = (
                search_resource_encryption_key.access_credentials.application_secret
            )
        else:
            application_id = None
            application_secret = None
        return cls(
            key_name=search_resource_encryption_key.key_name,
            key_version=search_resource_encryption_key.key_version,
            vault_uri=search_resource_encryption_key.vault_uri,
            application_id=application_id,
            application_secret=application_secret,
        )

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a SearchResourceEncryptionKey instance.

        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchResourceEncryptionKey instance
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchResourceEncryptionKey.deserialize(data, content_type=content_type)
        )

    def as_dict(
        self,
        keep_readonly: bool = True,
        key_transformer: Callable[
            [str, Dict[str, Any], Any], Any
        ] = _serialization.attribute_transformer,
        **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return a dict that can be serialized using json.dump.

        :param bool keep_readonly: If you want to serialize the readonly attributes
        :param Callable key_transformer: A callable that will transform the key of the dict
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().as_dict(  # type: ignore
            keep_readonly=keep_readonly, key_transformer=key_transformer, **kwargs
        )

    @classmethod
    def from_dict(  # type: ignore
        cls,
        data: Any,
        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
        content_type: Optional[str] = None,
    ) -> Optional[Self]:
        """Parse a dict using given key extractor return a model.

        By default consider key
        extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor
        and last_rest_key_case_insensitive_extractor)

        :param dict data: A dict using RestAPI structure
        :param Callable key_extractors: A callable that will extract a key from a dict
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchResourceEncryptionKey instance
        :rtype: SearchResourceEncryptionKey
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchResourceEncryptionKey.from_dict(
                data, content_type=content_type, key_extractors=key_extractors
            )
        )


class SynonymMap(_serialization.Model):
    """Represents a synonym map definition.

    Variables are only populated by the server, and will be ignored when sending a request.

    All required parameters must be populated in order to send to Azure.

    :ivar name: Required. The name of the synonym map.
    :vartype name: str
    :ivar format: Required. The format of the synonym map. Only the 'solr' format is currently
     supported. Default value: "solr".
    :vartype format: str
    :ivar synonyms: Required. A series of synonym rules in the specified synonym map format. The
     rules must be separated by newlines.
    :vartype synonyms: list[str]
    :ivar encryption_key: A description of an encryption key that you create in Azure Key Vault.
     This key is used to provide an additional level of encryption-at-rest for your data when you
     want full assurance that no one, not even Microsoft, can decrypt your data in Azure Cognitive
     Search. Once you have encrypted your data, it will always remain encrypted. Azure Cognitive
     Search will ignore attempts to set this property to null. You can change this property as
     needed if you want to rotate your encryption key; Your data will be unaffected. Encryption with
     customer-managed keys is not available for free search services, and is only available for paid
     services created on or after January 1, 2019.
    :vartype encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
    :ivar e_tag: The ETag of the synonym map.
    :vartype e_tag: str
    """

    format = "solr"

    def __init__(
        self,
        *,
        name: str,
        synonyms: List[str],
        encryption_key: Optional[SearchResourceEncryptionKey] = None,
        e_tag: Optional[str] = None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.name = name
        self.synonyms = synonyms
        self.encryption_key = encryption_key
        self.e_tag = e_tag

    def _to_generated(self):
        return _SynonymMap(
            name=self.name,
            synonyms="\n".join(self.synonyms),
            encryption_key=(
                self.encryption_key._to_generated()  # pylint:disable=protected-access
                if self.encryption_key
                else None
            ),
            e_tag=self.e_tag,
        )

    @classmethod
    def _from_generated(cls, synonym_map) -> Optional[Self]:
        if not synonym_map:
            return None
        return cls(
            name=synonym_map.name,
            synonyms=synonym_map.synonyms.split("\n"),
            # pylint:disable=protected-access
            encryption_key=SearchResourceEncryptionKey._from_generated(
                synonym_map.encryption_key
            ),
            e_tag=synonym_map.e_tag,
        )

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a SynonymMap instance.

        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SynonymMap instance
        :rtype: SynonymMap
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SynonymMap.deserialize(data, content_type=content_type)
        )

    def as_dict(
        self,
        keep_readonly: bool = True,
        key_transformer: Callable[
            [str, Dict[str, Any], Any], Any
        ] = _serialization.attribute_transformer,
        **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return a dict that can be serialized using json.dump.

        :param bool keep_readonly: If you want to serialize the readonly attributes
        :param Callable key_transformer: A callable that will transform the key of the dict
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().as_dict(  # type: ignore
            keep_readonly=keep_readonly, key_transformer=key_transformer, **kwargs
        )

    @classmethod
    def from_dict(  # type: ignore
        cls,
        data: Any,
        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
        content_type: Optional[str] = None,
    ) -> Optional[Self]:
        """Parse a dict using given key extractor return a model.

        By default consider key
        extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor
        and last_rest_key_case_insensitive_extractor)

        :param dict data: A dict using RestAPI structure
        :param Callable key_extractors: A callable that will extract a key from a dict
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SynonymMap instance
        :rtype: SynonymMap
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SynonymMap.from_dict(
                data, content_type=content_type, key_extractors=key_extractors
            )
        )


class SearchIndexerDataSourceConnection(_serialization.Model):
    """Represents a datasource connection definition, which can be used to configure an indexer.

    All required parameters must be populated in order to send to Azure.

    :ivar name: Required. The name of the datasource connection.
    :vartype name: str
    :ivar description: The description of the datasource connection.
    :vartype description: str
    :ivar type: Required. The type of the datasource connection. Possible values include: "azuresql",
     "cosmosdb", "azureblob", "azuretable", "mysql", "adlsgen2".
    :vartype type: str or ~azure.search.documents.indexes.models.SearchIndexerDataSourceType
    :ivar connection_string: The connection string for the datasource connection.
    :vartype connection_string: str
    :ivar container: Required. The data container for the datasource connection.
    :vartype container: ~azure.search.documents.indexes.models.SearchIndexerDataContainer
    :ivar data_change_detection_policy: The data change detection policy for the datasource connection.
    :vartype data_change_detection_policy: ~azure.search.documents.models.DataChangeDetectionPolicy
    :ivar data_deletion_detection_policy: The data deletion detection policy for the datasource connection.
    :vartype data_deletion_detection_policy:
     ~azure.search.documents.models.DataDeletionDetectionPolicy
    :ivar e_tag: The ETag of the data source.
    :vartype e_tag: str
    :ivar encryption_key: A description of an encryption key that you create in Azure Key Vault.
     This key is used to provide an additional level of encryption-at-rest for your datasource
     definition when you want full assurance that no one, not even Microsoft, can decrypt your data
     source definition in Azure Cognitive Search. Once you have encrypted your data source
     definition, it will always remain encrypted. Azure Cognitive Search will ignore attempts to set
     this property to null. You can change this property as needed if you want to rotate your
     encryption key; Your datasource definition will be unaffected. Encryption with customer-managed
     keys is not available for free search services, and is only available for paid services created
     on or after January 1, 2019.
    :vartype encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
    """

    def __init__(
        self,
        *,
        name: str,
        description: Optional[str] = None,
        type: str,
        connection_string: str,
        container: SearchIndexerDataContainer,
        data_change_detection_policy: Optional[DataChangeDetectionPolicy] = None,
        data_deletion_detection_policy: Optional[DataDeletionDetectionPolicy] = None,
        e_tag: Optional[str] = None,
        encryption_key: Optional[SearchResourceEncryptionKey] = None,
        **kwargs
    ):
        super().__init__(**kwargs)
        self.name = name
        self.description = description
        self.type = type
        self.connection_string = connection_string
        self.container = container
        self.data_change_detection_policy = data_change_detection_policy
        self.data_deletion_detection_policy = data_deletion_detection_policy
        self.e_tag = e_tag
        self.encryption_key = encryption_key

    def _to_generated(self):
        if self.connection_string is None or self.connection_string == "":
            connection_string = "<unchanged>"
        else:
            connection_string = self.connection_string
        credentials = DataSourceCredentials(connection_string=connection_string)
        return _SearchIndexerDataSource(
            name=self.name,
            description=self.description,
            type=self.type,
            credentials=credentials,
            container=self.container,
            data_change_detection_policy=self.data_change_detection_policy,
            data_deletion_detection_policy=self.data_deletion_detection_policy,
            e_tag=self.e_tag,
            encryption_key=(
                self.encryption_key._to_generated()  # pylint: disable=protected-access
                if self.encryption_key
                else None
            ),
        )

    @classmethod
    def _from_generated(cls, search_indexer_data_source) -> Optional[Self]:
        if not search_indexer_data_source:
            return None
        connection_string = (
            search_indexer_data_source.credentials.connection_string
            if search_indexer_data_source.credentials
            else None
        )
        return cls(
            name=search_indexer_data_source.name,
            description=search_indexer_data_source.description,
            type=search_indexer_data_source.type,
            connection_string=connection_string,
            container=search_indexer_data_source.container,
            data_change_detection_policy=search_indexer_data_source.data_change_detection_policy,
            data_deletion_detection_policy=search_indexer_data_source.data_deletion_detection_policy,
            e_tag=search_indexer_data_source.e_tag,
            encryption_key=(
                SearchResourceEncryptionKey._from_generated(  # pylint: disable=protected-access
                    search_indexer_data_source.encryption_key
                )
                if search_indexer_data_source.encryption_key
                else None
            ),
        )

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a SearchIndexerDataSourceConnection instance.

        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchIndexerDataSourceConnection instance
        :rtype: SearchIndexerDataSourceConnection
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchIndexerDataSource.deserialize(data, content_type=content_type)
        )

    def as_dict(
        self,
        keep_readonly: bool = True,
        key_transformer: Callable[
            [str, Dict[str, Any], Any], Any
        ] = _serialization.attribute_transformer,
        **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return a dict that can be serialized using json.dump.

        :param bool keep_readonly: If you want to serialize the readonly attributes
        :param Callable key_transformer: A callable that will transform the key of the dict
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().as_dict(  # type: ignore
            keep_readonly=keep_readonly, key_transformer=key_transformer, **kwargs
        )

    @classmethod
    def from_dict(  # type: ignore
        cls,
        data: Any,
        key_extractors: Optional[Callable[[str, Dict[str, Any], Any], Any]] = None,
        content_type: Optional[str] = None,
    ) -> Optional[Self]:
        """Parse a dict using given key extractor return a model.

        By default consider key
        extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor
        and last_rest_key_case_insensitive_extractor)

        :param dict data: A dict using RestAPI structure
        :param Callable key_extractors: A callable that will extract a key from a dict
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchIndexerDataSourceConnection instance
        :rtype: SearchIndexerDataSourceConnection
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchIndexerDataSource.from_dict(
                data, content_type=content_type, key_extractors=key_extractors
            )
        )


def pack_analyzer(analyzer):
    if not analyzer:
        return None
    if isinstance(analyzer, (PatternAnalyzer, CustomAnalyzer)):
        return analyzer._to_generated()  # pylint:disable=protected-access
    return analyzer


def unpack_analyzer(analyzer):
    if not analyzer:
        return None
    if isinstance(analyzer, _PatternAnalyzer):
        return PatternAnalyzer._from_generated(  # pylint:disable=protected-access
            analyzer
        )
    if isinstance(analyzer, _CustomAnalyzer):
        return CustomAnalyzer._from_generated(  # pylint:disable=protected-access
            analyzer
        )
    return analyzer


class SearchIndexer(
    _serialization.Model
):  # pylint: disable=too-many-instance-attributes
    """Represents an indexer.

    All required parameters must be populated in order to send to server.

    :ivar name: The name of the indexer. Required.
    :vartype name: str
    :ivar description: The description of the indexer.
    :vartype description: str
    :ivar data_source_name: The name of the datasource from which this indexer reads data.
     Required.
    :vartype data_source_name: str
    :ivar skillset_name: The name of the skillset executing with this indexer.
    :vartype skillset_name: str
    :ivar target_index_name: The name of the index to which this indexer writes data. Required.
    :vartype target_index_name: str
    :ivar schedule: The schedule for this indexer.
    :vartype schedule: ~azure.search.documents.indexes.models.IndexingSchedule
    :ivar parameters: Parameters for indexer execution.
    :vartype parameters: ~azure.search.documents.indexes.models.IndexingParameters
    :ivar field_mappings: Defines mappings between fields in the data source and corresponding
     target fields in the index.
    :vartype field_mappings: list[~azure.search.documents.indexes.models.FieldMapping]
    :ivar output_field_mappings: Output field mappings are applied after enrichment and immediately
     before indexing.
    :vartype output_field_mappings: list[~azure.search.documents.indexes.models.FieldMapping]
    :ivar is_disabled: A value indicating whether the indexer is disabled. Default is false.
    :vartype is_disabled: bool
    :ivar e_tag: The ETag of the indexer.
    :vartype e_tag: str
    :ivar encryption_key: A description of an encryption key that you create in Azure Key Vault.
     This key is used to provide an additional level of encryption-at-rest for your indexer
     definition (as well as indexer execution status) when you want full assurance that no one, not
     even Microsoft, can decrypt them. Once you have encrypted your indexer definition, it will
     always remain encrypted. The search service will ignore attempts to set this property to null.
     You can change this property as needed if you want to rotate your encryption key; Your indexer
     definition (and indexer execution status) will be unaffected. Encryption with customer-managed
     keys is not available for free search services, and is only available for paid services created
     on or after January 1, 2019.
    :vartype encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
    """

    def __init__(
        self,
        *,
        name: str,
        data_source_name: str,
        target_index_name: str,
        description: Optional[str] = None,
        skillset_name: Optional[str] = None,
        schedule: Optional[IndexingSchedule] = None,
        parameters: Optional[IndexingParameters] = None,
        field_mappings: Optional[List[FieldMapping]] = None,
        output_field_mappings: Optional[List[FieldMapping]] = None,
        is_disabled: bool = False,
        e_tag: Optional[str] = None,
        encryption_key: Optional[SearchResourceEncryptionKey] = None,
        **kwargs: Any
    ) -> None:
        """
        :keyword name: The name of the indexer. Required.
        :paramtype name: str
        :keyword description: The description of the indexer.
        :paramtype description: str
        :keyword data_source_name: The name of the datasource from which this indexer reads data.
         Required.
        :paramtype data_source_name: str
        :keyword skillset_name: The name of the skillset executing with this indexer.
        :paramtype skillset_name: str
        :keyword target_index_name: The name of the index to which this indexer writes data. Required.
        :paramtype target_index_name: str
        :keyword schedule: The schedule for this indexer.
        :paramtype schedule: ~azure.search.documents.indexes.models.IndexingSchedule
        :keyword parameters: Parameters for indexer execution.
        :paramtype parameters: ~azure.search.documents.indexes.models.IndexingParameters
        :keyword field_mappings: Defines mappings between fields in the data source and corresponding
         target fields in the index.
        :paramtype field_mappings: list[~azure.search.documents.indexes.models.FieldMapping]
        :keyword output_field_mappings: Output field mappings are applied after enrichment and
         immediately before indexing.
        :paramtype output_field_mappings: list[~azure.search.documents.indexes.models.FieldMapping]
        :keyword is_disabled: A value indicating whether the indexer is disabled. Default is false.
        :paramtype is_disabled: bool
        :keyword e_tag: The ETag of the indexer.
        :paramtype e_tag: str
        :keyword encryption_key: A description of an encryption key that you create in Azure Key Vault.
         This key is used to provide an additional level of encryption-at-rest for your indexer
         definition (as well as indexer execution status) when you want full assurance that no one, not
         even Microsoft, can decrypt them. Once you have encrypted your indexer definition, it will
         always remain encrypted. The search service will ignore attempts to set this property to null.
         You can change this property as needed if you want to rotate your encryption key; Your indexer
         definition (and indexer execution status) will be unaffected. Encryption with customer-managed
         keys is not available for free search services, and is only available for paid services created
         on or after January 1, 2019.
        :paramtype encryption_key: ~azure.search.documents.indexes.models.SearchResourceEncryptionKey
        """
        super().__init__(**kwargs)
        self.name = name
        self.description = description
        self.data_source_name = data_source_name
        self.skillset_name = skillset_name
        self.target_index_name = target_index_name
        self.schedule = schedule
        self.parameters = parameters
        self.field_mappings = field_mappings
        self.output_field_mappings = output_field_mappings
        self.is_disabled = is_disabled
        self.e_tag = e_tag
        self.encryption_key = encryption_key

    def _to_generated(self):
        return _SearchIndexer(
            name=self.name,
            description=self.description,
            data_source_name=self.data_source_name,
            skillset_name=self.skillset_name,
            target_index_name=self.target_index_name,
            schedule=self.schedule,
            parameters=self.parameters,
            field_mappings=self.field_mappings,
            output_field_mappings=self.output_field_mappings,
            is_disabled=self.is_disabled,
            e_tag=self.e_tag,
            encryption_key=(
                self.encryption_key._to_generated()  # pylint:disable=protected-access
                if self.encryption_key
                else None
            ),
        )

    @classmethod
    def _from_generated(cls, search_indexer) -> Optional[Self]:
        if not search_indexer:
            return None
        return cls(
            name=search_indexer.name,
            description=search_indexer.description,
            data_source_name=search_indexer.data_source_name,
            skillset_name=search_indexer.skillset_name,
            target_index_name=search_indexer.target_index_name,
            schedule=search_indexer.schedule,
            parameters=search_indexer.parameters,
            field_mappings=search_indexer.field_mappings,
            output_field_mappings=search_indexer.output_field_mappings,
            is_disabled=search_indexer.is_disabled,
            e_tag=search_indexer.e_tag,
            # pylint:disable=protected-access
            encryption_key=SearchResourceEncryptionKey._from_generated(
                search_indexer.encryption_key
            ),
        )

    def serialize(
        self, keep_readonly: bool = False, **kwargs: Any
    ) -> MutableMapping[str, Any]:
        """Return the JSON that would be sent to server from this model.
        :param bool keep_readonly: If you want to serialize the readonly attributes
        :returns: A dict JSON compatible object
        :rtype: dict
        """
        return self._to_generated().serialize(keep_readonly=keep_readonly, **kwargs)  # type: ignore

    @classmethod
    def deserialize(cls, data: Any, content_type: Optional[str] = None) -> Optional[Self]:  # type: ignore
        """Parse a str using the RestAPI syntax and return a SearchIndexer instance.
        :param str data: A str using RestAPI structure. JSON by default.
        :param str content_type: JSON by default, set application/xml if XML.
        :returns: A SearchIndexer instance
        :rtype: SearchIndexer
        :raises: DeserializationError if something went wrong
        """
        return cls._from_generated(
            _SearchIndexer.deserialize(data, content_type=content_type)
        )
