# pylint: disable=too-many-lines,too-many-statements
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
from io import IOBase
import sys
from typing import Any, Callable, Dict, IO, Optional, TypeVar, Union, cast, overload

from azure.core.exceptions import (
    ClientAuthenticationError,
    HttpResponseError,
    ResourceExistsError,
    ResourceNotFoundError,
    ResourceNotModifiedError,
    map_error,
)
from azure.core.pipeline import PipelineResponse
from azure.core.polling import LROPoller, NoPolling, PollingMethod
from azure.core.polling.base_polling import LROBasePolling
from azure.core.rest import HttpRequest, HttpResponse
from azure.core.tracing.decorator import distributed_trace
from azure.core.utils import case_insensitive_dict

from .._serialization import Serializer
from .._vendor import ConversationAnalysisClientMixinABC

if sys.version_info >= (3, 9):
    from collections.abc import MutableMapping
else:
    from typing import MutableMapping  # type: ignore  # pylint: disable=ungrouped-imports
JSON = MutableMapping[str, Any]  # pylint: disable=unsubscriptable-object
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]

_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False


def build_conversation_analysis_analyze_conversation_request(  # pylint: disable=name-too-long
    **kwargs: Any,
) -> HttpRequest:
    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
    api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
    accept = _headers.pop("Accept", "application/json")

    # Construct URL
    _url = "/:analyze-conversations"

    # Construct parameters
    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")

    # Construct headers
    if content_type is not None:
        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")

    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)


def build_conversation_analysis_conversation_analysis_request(  # pylint: disable=name-too-long
    **kwargs: Any,
) -> HttpRequest:
    _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
    _params = case_insensitive_dict(kwargs.pop("params", {}) or {})

    content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
    api_version: str = kwargs.pop("api_version", _params.pop("api-version", "2023-04-01"))
    accept = _headers.pop("Accept", "application/json")

    # Construct URL
    _url = "/analyze-conversations/jobs"

    # Construct parameters
    _params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")

    # Construct headers
    if content_type is not None:
        _headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
    _headers["Accept"] = _SERIALIZER.header("accept", accept, "str")

    return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)


class ConversationAnalysisClientOperationsMixin(ConversationAnalysisClientMixinABC):  # pylint: disable=name-too-long
    @overload
    def analyze_conversation(self, task: JSON, *, content_type: str = "application/json", **kwargs: Any) -> JSON:
        # pylint: disable=line-too-long
        """Analyzes the input conversation utterance.

        See
        https://learn.microsoft.com/rest/api/language/2023-04-01/conversation-analysis-runtime/analyze-conversation
        for more information.

        :param task: A single conversational task to execute. Required.
        :type task: JSON
        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
         Default value is "application/json".
        :paramtype content_type: str
        :return: JSON object
        :rtype: JSON
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python

                # The input is polymorphic. The following are possible polymorphic inputs based off
                  discriminator "kind":

                # JSON input template for discriminator value "Conversation":
                analyze_conversation_task = {
                    "analysisInput": {
                        "conversationItem": {
                            "id": "str",  # The ID of a conversation item. Required.
                            "participantId": "str",  # The participant ID of a
                              conversation item. Required.
                            "language": "str",  # Optional. The override language of a
                              conversation item in BCP 47 language representation.
                            "modality": "str",  # Optional. Enumeration of supported
                              conversational modalities. Known values are: "transcript" and "text".
                            "role": "str"  # Optional. Role of the participant. Known
                              values are: "agent", "customer", and "generic".
                        }
                    },
                    "kind": "Conversation",
                    "parameters": {
                        "deploymentName": "str",  # The name of the deployment to use.
                          Required.
                        "projectName": "str",  # The name of the project to use. Required.
                        "directTarget": "str",  # Optional. The name of a target project to
                          forward the request to.
                        "isLoggingEnabled": bool,  # Optional. If true, the service will keep
                          the query for further review.
                        "stringIndexType": "TextElements_v8",  # Optional. Default value is
                          "TextElements_v8". Specifies the method used to interpret string offsets. Set
                          to "UnicodeCodePoint" for Python strings. Known values are:
                          "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit".
                        "targetProjectParameters": {
                            "str": analysis_parameters
                        },
                        "verbose": bool  # Optional. If true, the service will return more
                          detailed information in the response.
                    }
                }

                # JSON input template you can fill out and use as your body input.
                task = analyze_conversation_task
                # The response is polymorphic. The following are possible polymorphic responses based
                  off discriminator "kind":

                # JSON input template for discriminator value "ConversationResult":
                analyze_conversation_task_result = {
                    "kind": "ConversationResult",
                    "result": {
                        "prediction": base_prediction,
                        "query": "str",  # The conversation utterance given by the caller.
                          Required.
                        "detectedLanguage": "str"  # Optional. The system detected language
                          for the query in BCP 47 language representation..
                    }
                }

                # JSON input template for discriminator value "Conversation":
                base_prediction = {
                    "entities": [
                        {
                            "category": "str",  # The entity category. Required.
                            "confidenceScore": 0.0,  # The entity confidence score.
                              Required.
                            "length": 0,  # The length of the text. Required.
                            "offset": 0,  # The starting index of this entity in the
                              query. Required.
                            "text": "str",  # The predicted entity text. Required.
                            "extraInformation": [
                                base_extra_information
                            ],
                            "resolutions": [
                                base_resolution
                            ]
                        }
                    ],
                    "intents": [
                        {
                            "category": "str",  # A predicted class. Required.
                            "confidenceScore": 0.0  # The confidence score of the class
                              from 0.0 to 1.0. Required.
                        }
                    ],
                    "projectKind": "Conversation",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # JSON input template for discriminator value "Orchestration":
                base_prediction = {
                    "intents": {
                        "str": target_intent_result
                    },
                    "projectKind": "Orchestration",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # response body for status code(s): 200
                response == analyze_conversation_task_result
        """

    @overload
    def analyze_conversation(self, task: IO[bytes], *, content_type: str = "application/json", **kwargs: Any) -> JSON:
        # pylint: disable=line-too-long
        """Analyzes the input conversation utterance.

        See
        https://learn.microsoft.com/rest/api/language/2023-04-01/conversation-analysis-runtime/analyze-conversation
        for more information.

        :param task: A single conversational task to execute. Required.
        :type task: IO[bytes]
        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
         Default value is "application/json".
        :paramtype content_type: str
        :return: JSON object
        :rtype: JSON
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python
                # The response is polymorphic. The following are possible polymorphic responses based
                  off discriminator "kind":

                # JSON input template for discriminator value "ConversationResult":
                analyze_conversation_task_result = {
                    "kind": "ConversationResult",
                    "result": {
                        "prediction": base_prediction,
                        "query": "str",  # The conversation utterance given by the caller.
                          Required.
                        "detectedLanguage": "str"  # Optional. The system detected language
                          for the query in BCP 47 language representation..
                    }
                }

                # JSON input template for discriminator value "Conversation":
                base_prediction = {
                    "entities": [
                        {
                            "category": "str",  # The entity category. Required.
                            "confidenceScore": 0.0,  # The entity confidence score.
                              Required.
                            "length": 0,  # The length of the text. Required.
                            "offset": 0,  # The starting index of this entity in the
                              query. Required.
                            "text": "str",  # The predicted entity text. Required.
                            "extraInformation": [
                                base_extra_information
                            ],
                            "resolutions": [
                                base_resolution
                            ]
                        }
                    ],
                    "intents": [
                        {
                            "category": "str",  # A predicted class. Required.
                            "confidenceScore": 0.0  # The confidence score of the class
                              from 0.0 to 1.0. Required.
                        }
                    ],
                    "projectKind": "Conversation",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # JSON input template for discriminator value "Orchestration":
                base_prediction = {
                    "intents": {
                        "str": target_intent_result
                    },
                    "projectKind": "Orchestration",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # response body for status code(s): 200
                response == analyze_conversation_task_result
        """

    @distributed_trace
    def analyze_conversation(self, task: Union[JSON, IO[bytes]], **kwargs: Any) -> JSON:
        # pylint: disable=line-too-long
        """Analyzes the input conversation utterance.

        See
        https://learn.microsoft.com/rest/api/language/2023-04-01/conversation-analysis-runtime/analyze-conversation
        for more information.

        :param task: A single conversational task to execute. Is either a JSON type or a IO[bytes]
         type. Required.
        :type task: JSON or IO[bytes]
        :return: JSON object
        :rtype: JSON
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python

                # The input is polymorphic. The following are possible polymorphic inputs based off
                  discriminator "kind":

                # JSON input template for discriminator value "Conversation":
                analyze_conversation_task = {
                    "analysisInput": {
                        "conversationItem": {
                            "id": "str",  # The ID of a conversation item. Required.
                            "participantId": "str",  # The participant ID of a
                              conversation item. Required.
                            "language": "str",  # Optional. The override language of a
                              conversation item in BCP 47 language representation.
                            "modality": "str",  # Optional. Enumeration of supported
                              conversational modalities. Known values are: "transcript" and "text".
                            "role": "str"  # Optional. Role of the participant. Known
                              values are: "agent", "customer", and "generic".
                        }
                    },
                    "kind": "Conversation",
                    "parameters": {
                        "deploymentName": "str",  # The name of the deployment to use.
                          Required.
                        "projectName": "str",  # The name of the project to use. Required.
                        "directTarget": "str",  # Optional. The name of a target project to
                          forward the request to.
                        "isLoggingEnabled": bool,  # Optional. If true, the service will keep
                          the query for further review.
                        "stringIndexType": "TextElements_v8",  # Optional. Default value is
                          "TextElements_v8". Specifies the method used to interpret string offsets. Set
                          to "UnicodeCodePoint" for Python strings. Known values are:
                          "TextElements_v8", "UnicodeCodePoint", and "Utf16CodeUnit".
                        "targetProjectParameters": {
                            "str": analysis_parameters
                        },
                        "verbose": bool  # Optional. If true, the service will return more
                          detailed information in the response.
                    }
                }

                # JSON input template you can fill out and use as your body input.
                task = analyze_conversation_task
                # The response is polymorphic. The following are possible polymorphic responses based
                  off discriminator "kind":

                # JSON input template for discriminator value "ConversationResult":
                analyze_conversation_task_result = {
                    "kind": "ConversationResult",
                    "result": {
                        "prediction": base_prediction,
                        "query": "str",  # The conversation utterance given by the caller.
                          Required.
                        "detectedLanguage": "str"  # Optional. The system detected language
                          for the query in BCP 47 language representation..
                    }
                }

                # JSON input template for discriminator value "Conversation":
                base_prediction = {
                    "entities": [
                        {
                            "category": "str",  # The entity category. Required.
                            "confidenceScore": 0.0,  # The entity confidence score.
                              Required.
                            "length": 0,  # The length of the text. Required.
                            "offset": 0,  # The starting index of this entity in the
                              query. Required.
                            "text": "str",  # The predicted entity text. Required.
                            "extraInformation": [
                                base_extra_information
                            ],
                            "resolutions": [
                                base_resolution
                            ]
                        }
                    ],
                    "intents": [
                        {
                            "category": "str",  # A predicted class. Required.
                            "confidenceScore": 0.0  # The confidence score of the class
                              from 0.0 to 1.0. Required.
                        }
                    ],
                    "projectKind": "Conversation",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # JSON input template for discriminator value "Orchestration":
                base_prediction = {
                    "intents": {
                        "str": target_intent_result
                    },
                    "projectKind": "Orchestration",
                    "topIntent": "str"  # Optional. The intent with the highest score.
                }

                # response body for status code(s): 200
                response == analyze_conversation_task_result
        """
        error_map = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = kwargs.pop("params", {}) or {}

        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
        cls: ClsType[JSON] = kwargs.pop("cls", None)

        content_type = content_type or "application/json"
        _json = None
        _content = None
        if isinstance(task, (IOBase, bytes)):
            _content = task
        else:
            _json = task

        _request = build_conversation_analysis_analyze_conversation_request(
            content_type=content_type,
            api_version=self._config.api_version,
            json=_json,
            content=_content,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _stream = False
        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200]:
            if _stream:
                response.read()  # Load the body in memory and close the socket
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        if response.content:
            deserialized = response.json()
        else:
            deserialized = None

        if cls:
            return cls(pipeline_response, cast(JSON, deserialized), {})  # type: ignore

        return cast(JSON, deserialized)  # type: ignore

    def _conversation_analysis_initial(self, task: Union[JSON, IO[bytes]], **kwargs: Any) -> Optional[JSON]:
        error_map = {
            401: ClientAuthenticationError,
            404: ResourceNotFoundError,
            409: ResourceExistsError,
            304: ResourceNotModifiedError,
        }
        error_map.update(kwargs.pop("error_map", {}) or {})

        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = kwargs.pop("params", {}) or {}

        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
        cls: ClsType[Optional[JSON]] = kwargs.pop("cls", None)

        content_type = content_type or "application/json"
        _json = None
        _content = None
        if isinstance(task, (IOBase, bytes)):
            _content = task
        else:
            _json = task

        _request = build_conversation_analysis_conversation_analysis_request(
            content_type=content_type,
            api_version=self._config.api_version,
            json=_json,
            content=_content,
            headers=_headers,
            params=_params,
        )
        path_format_arguments = {
            "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }
        _request.url = self._client.format_url(_request.url, **path_format_arguments)

        _stream = False
        pipeline_response: PipelineResponse = self._client._pipeline.run(  # pylint: disable=protected-access
            _request, stream=_stream, **kwargs
        )

        response = pipeline_response.http_response

        if response.status_code not in [200, 202]:
            if _stream:
                response.read()  # Load the body in memory and close the socket
            map_error(status_code=response.status_code, response=response, error_map=error_map)
            raise HttpResponseError(response=response)

        deserialized = None
        response_headers = {}
        if response.status_code == 200:
            if response.content:
                deserialized = response.json()
            else:
                deserialized = None

        if response.status_code == 202:
            response_headers["Operation-Location"] = self._deserialize(
                "str", response.headers.get("Operation-Location")
            )

        if cls:
            return cls(pipeline_response, deserialized, response_headers)  # type: ignore

        return deserialized  # type: ignore

    @overload
    def begin_conversation_analysis(
        self, task: JSON, *, content_type: str = "application/json", **kwargs: Any
    ) -> LROPoller[JSON]:
        # pylint: disable=line-too-long
        """Submit analysis job for conversations.

        Submit a collection of conversations for analysis. Specify one or more unique tasks to be
        executed.

        See https://learn.microsoft.com/rest/api/language/2023-04-01/analyze-conversation/submit-job
        for more information.

        :param task: Collection of conversations to analyze and one or more tasks to execute. Required.
        :type task: JSON
        :keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
         Default value is "application/json".
        :paramtype content_type: str
        :return: An instance of LROPoller that returns JSON object
        :rtype: ~azure.core.polling.LROPoller[JSON]
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python

                # JSON input template you can fill out and use as your body input.
                task = {
                    "analysisInput": {
                        "conversations": [
                            conversation
                        ]
                    },
                    "tasks": [
                        analyze_conversation_lro_task
                    ],
                    "displayName": "str"  # Optional. Display name for the analysis job.
                }

                # response body for status code(s): 200
                response == {
                    "createdDateTime": "2020-02-20 00:00:00",  # Required.
                    "jobId": "str",  # Required.
                    "lastUpdatedDateTime": "2020-02-20 00:00:00",  # Required.
                    "status": "str",  # The status of the task at the mentioned last update time.
                      Required. Known values are: "notStarted", "running", "succeeded", "failed",
                      "cancelled", "cancelling", and "partiallyCompleted".
                    "tasks": {
                        "completed": 0,  # Count of tasks that finished successfully.
                          Required.
                        "failed": 0,  # Count of tasks that failed. Required.
                        "inProgress": 0,  # Count of tasks that are currently in progress.
                          Required.
                        "total": 0,  # Total count of tasks submitted as part of the job.
                          Required.
                        "items": [
                            analyze_conversation_job_result
                        ]
                    },
                    "displayName": "str",  # Optional.
                    "errors": [
                        {
                            "code": "str",  # One of a server-defined set of error codes.
                              Required. Known values are: "InvalidRequest", "InvalidArgument",
                              "Unauthorized", "Forbidden", "NotFound", "ProjectNotFound",
                              "OperationNotFound", "AzureCognitiveSearchNotFound",
                              "AzureCognitiveSearchIndexNotFound", "TooManyRequests",
                              "AzureCognitiveSearchThrottling",
                              "AzureCognitiveSearchIndexLimitReached", "InternalServerError",
                              "ServiceUnavailable", "Timeout", "QuotaExceeded", "Conflict", and
                              "Warning".
                            "message": "str",  # A human-readable representation of the
                              error. Required.
                            "details": [
                                ...
                            ],
                            "innererror": {
                                "code": "str",  # One of a server-defined set of
                                  error codes. Required. Known values are: "InvalidRequest",
                                  "InvalidParameterValue", "KnowledgeBaseNotFound",
                                  "AzureCognitiveSearchNotFound", "AzureCognitiveSearchThrottling",
                                  "ExtractionFailure", "InvalidRequestBodyFormat", "EmptyRequest",
                                  "MissingInputDocuments", "InvalidDocument", "ModelVersionIncorrect",
                                  "InvalidDocumentBatch", "UnsupportedLanguageCode", and
                                  "InvalidCountryHint".
                                "message": "str",  # Error message. Required.
                                "details": {
                                    "str": "str"  # Optional. Error details.
                                },
                                "innererror": ...,
                                "target": "str"  # Optional. Error target.
                            },
                            "target": "str"  # Optional. The target of the error.
                        }
                    ],
                    "expirationDateTime": "2020-02-20 00:00:00",  # Optional.
                    "nextLink": "str",  # Optional.
                    "statistics": {
                        "conversationsCount": 0,  # Number of conversations submitted in the
                          request. Required.
                        "documentsCount": 0,  # Number of documents submitted in the request.
                          Required.
                        "erroneousConversationsCount": 0,  # Number of invalid documents.
                          This includes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "erroneousDocumentsCount": 0,  # Number of invalid documents. This
                          includes empty, over-size limit or non-supported languages documents.
                          Required.
                        "transactionsCount": 0,  # Number of transactions for the request.
                          Required.
                        "validConversationsCount": 0,  # Number of conversation documents.
                          This excludes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "validDocumentsCount": 0  # Number of valid documents. This excludes
                          empty, over-size limit or non-supported languages documents. Required.
                    }
                }
        """

    @overload
    def begin_conversation_analysis(
        self, task: IO[bytes], *, content_type: str = "application/json", **kwargs: Any
    ) -> LROPoller[JSON]:
        # pylint: disable=line-too-long
        """Submit analysis job for conversations.

        Submit a collection of conversations for analysis. Specify one or more unique tasks to be
        executed.

        See https://learn.microsoft.com/rest/api/language/2023-04-01/analyze-conversation/submit-job
        for more information.

        :param task: Collection of conversations to analyze and one or more tasks to execute. Required.
        :type task: IO[bytes]
        :keyword content_type: Body Parameter content-type. Content type parameter for binary body.
         Default value is "application/json".
        :paramtype content_type: str
        :return: An instance of LROPoller that returns JSON object
        :rtype: ~azure.core.polling.LROPoller[JSON]
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python

                # response body for status code(s): 200
                response == {
                    "createdDateTime": "2020-02-20 00:00:00",  # Required.
                    "jobId": "str",  # Required.
                    "lastUpdatedDateTime": "2020-02-20 00:00:00",  # Required.
                    "status": "str",  # The status of the task at the mentioned last update time.
                      Required. Known values are: "notStarted", "running", "succeeded", "failed",
                      "cancelled", "cancelling", and "partiallyCompleted".
                    "tasks": {
                        "completed": 0,  # Count of tasks that finished successfully.
                          Required.
                        "failed": 0,  # Count of tasks that failed. Required.
                        "inProgress": 0,  # Count of tasks that are currently in progress.
                          Required.
                        "total": 0,  # Total count of tasks submitted as part of the job.
                          Required.
                        "items": [
                            analyze_conversation_job_result
                        ]
                    },
                    "displayName": "str",  # Optional.
                    "errors": [
                        {
                            "code": "str",  # One of a server-defined set of error codes.
                              Required. Known values are: "InvalidRequest", "InvalidArgument",
                              "Unauthorized", "Forbidden", "NotFound", "ProjectNotFound",
                              "OperationNotFound", "AzureCognitiveSearchNotFound",
                              "AzureCognitiveSearchIndexNotFound", "TooManyRequests",
                              "AzureCognitiveSearchThrottling",
                              "AzureCognitiveSearchIndexLimitReached", "InternalServerError",
                              "ServiceUnavailable", "Timeout", "QuotaExceeded", "Conflict", and
                              "Warning".
                            "message": "str",  # A human-readable representation of the
                              error. Required.
                            "details": [
                                ...
                            ],
                            "innererror": {
                                "code": "str",  # One of a server-defined set of
                                  error codes. Required. Known values are: "InvalidRequest",
                                  "InvalidParameterValue", "KnowledgeBaseNotFound",
                                  "AzureCognitiveSearchNotFound", "AzureCognitiveSearchThrottling",
                                  "ExtractionFailure", "InvalidRequestBodyFormat", "EmptyRequest",
                                  "MissingInputDocuments", "InvalidDocument", "ModelVersionIncorrect",
                                  "InvalidDocumentBatch", "UnsupportedLanguageCode", and
                                  "InvalidCountryHint".
                                "message": "str",  # Error message. Required.
                                "details": {
                                    "str": "str"  # Optional. Error details.
                                },
                                "innererror": ...,
                                "target": "str"  # Optional. Error target.
                            },
                            "target": "str"  # Optional. The target of the error.
                        }
                    ],
                    "expirationDateTime": "2020-02-20 00:00:00",  # Optional.
                    "nextLink": "str",  # Optional.
                    "statistics": {
                        "conversationsCount": 0,  # Number of conversations submitted in the
                          request. Required.
                        "documentsCount": 0,  # Number of documents submitted in the request.
                          Required.
                        "erroneousConversationsCount": 0,  # Number of invalid documents.
                          This includes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "erroneousDocumentsCount": 0,  # Number of invalid documents. This
                          includes empty, over-size limit or non-supported languages documents.
                          Required.
                        "transactionsCount": 0,  # Number of transactions for the request.
                          Required.
                        "validConversationsCount": 0,  # Number of conversation documents.
                          This excludes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "validDocumentsCount": 0  # Number of valid documents. This excludes
                          empty, over-size limit or non-supported languages documents. Required.
                    }
                }
        """

    @distributed_trace
    def begin_conversation_analysis(self, task: Union[JSON, IO[bytes]], **kwargs: Any) -> LROPoller[JSON]:
        # pylint: disable=line-too-long
        """Submit analysis job for conversations.

        Submit a collection of conversations for analysis. Specify one or more unique tasks to be
        executed.

        See https://learn.microsoft.com/rest/api/language/2023-04-01/analyze-conversation/submit-job
        for more information.

        :param task: Collection of conversations to analyze and one or more tasks to execute. Is either
         a JSON type or a IO[bytes] type. Required.
        :type task: JSON or IO[bytes]
        :return: An instance of LROPoller that returns JSON object
        :rtype: ~azure.core.polling.LROPoller[JSON]
        :raises ~azure.core.exceptions.HttpResponseError:

        Example:
            .. code-block:: python

                # JSON input template you can fill out and use as your body input.
                task = {
                    "analysisInput": {
                        "conversations": [
                            conversation
                        ]
                    },
                    "tasks": [
                        analyze_conversation_lro_task
                    ],
                    "displayName": "str"  # Optional. Display name for the analysis job.
                }

                # response body for status code(s): 200
                response == {
                    "createdDateTime": "2020-02-20 00:00:00",  # Required.
                    "jobId": "str",  # Required.
                    "lastUpdatedDateTime": "2020-02-20 00:00:00",  # Required.
                    "status": "str",  # The status of the task at the mentioned last update time.
                      Required. Known values are: "notStarted", "running", "succeeded", "failed",
                      "cancelled", "cancelling", and "partiallyCompleted".
                    "tasks": {
                        "completed": 0,  # Count of tasks that finished successfully.
                          Required.
                        "failed": 0,  # Count of tasks that failed. Required.
                        "inProgress": 0,  # Count of tasks that are currently in progress.
                          Required.
                        "total": 0,  # Total count of tasks submitted as part of the job.
                          Required.
                        "items": [
                            analyze_conversation_job_result
                        ]
                    },
                    "displayName": "str",  # Optional.
                    "errors": [
                        {
                            "code": "str",  # One of a server-defined set of error codes.
                              Required. Known values are: "InvalidRequest", "InvalidArgument",
                              "Unauthorized", "Forbidden", "NotFound", "ProjectNotFound",
                              "OperationNotFound", "AzureCognitiveSearchNotFound",
                              "AzureCognitiveSearchIndexNotFound", "TooManyRequests",
                              "AzureCognitiveSearchThrottling",
                              "AzureCognitiveSearchIndexLimitReached", "InternalServerError",
                              "ServiceUnavailable", "Timeout", "QuotaExceeded", "Conflict", and
                              "Warning".
                            "message": "str",  # A human-readable representation of the
                              error. Required.
                            "details": [
                                ...
                            ],
                            "innererror": {
                                "code": "str",  # One of a server-defined set of
                                  error codes. Required. Known values are: "InvalidRequest",
                                  "InvalidParameterValue", "KnowledgeBaseNotFound",
                                  "AzureCognitiveSearchNotFound", "AzureCognitiveSearchThrottling",
                                  "ExtractionFailure", "InvalidRequestBodyFormat", "EmptyRequest",
                                  "MissingInputDocuments", "InvalidDocument", "ModelVersionIncorrect",
                                  "InvalidDocumentBatch", "UnsupportedLanguageCode", and
                                  "InvalidCountryHint".
                                "message": "str",  # Error message. Required.
                                "details": {
                                    "str": "str"  # Optional. Error details.
                                },
                                "innererror": ...,
                                "target": "str"  # Optional. Error target.
                            },
                            "target": "str"  # Optional. The target of the error.
                        }
                    ],
                    "expirationDateTime": "2020-02-20 00:00:00",  # Optional.
                    "nextLink": "str",  # Optional.
                    "statistics": {
                        "conversationsCount": 0,  # Number of conversations submitted in the
                          request. Required.
                        "documentsCount": 0,  # Number of documents submitted in the request.
                          Required.
                        "erroneousConversationsCount": 0,  # Number of invalid documents.
                          This includes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "erroneousDocumentsCount": 0,  # Number of invalid documents. This
                          includes empty, over-size limit or non-supported languages documents.
                          Required.
                        "transactionsCount": 0,  # Number of transactions for the request.
                          Required.
                        "validConversationsCount": 0,  # Number of conversation documents.
                          This excludes documents that are empty, over the size limit, or in
                          unsupported languages. Required.
                        "validDocumentsCount": 0  # Number of valid documents. This excludes
                          empty, over-size limit or non-supported languages documents. Required.
                    }
                }
        """
        _headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
        _params = kwargs.pop("params", {}) or {}

        content_type: Optional[str] = kwargs.pop("content_type", _headers.pop("Content-Type", None))
        cls: ClsType[JSON] = kwargs.pop("cls", None)
        polling: Union[bool, PollingMethod] = kwargs.pop("polling", True)
        lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
        cont_token: Optional[str] = kwargs.pop("continuation_token", None)
        if cont_token is None:
            raw_result = self._conversation_analysis_initial(
                task=task, content_type=content_type, cls=lambda x, y, z: x, headers=_headers, params=_params, **kwargs
            )
        kwargs.pop("error_map", None)

        def get_long_running_output(pipeline_response):
            response = pipeline_response.http_response
            if response.content:
                deserialized = response.json()
            else:
                deserialized = None
            if cls:
                return cls(pipeline_response, deserialized, {})  # type: ignore
            return deserialized

        path_format_arguments = {
            "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True),
        }

        if polling is True:
            polling_method: PollingMethod = cast(
                PollingMethod, LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs)
            )
        elif polling is False:
            polling_method = cast(PollingMethod, NoPolling())
        else:
            polling_method = polling
        if cont_token:
            return LROPoller[JSON].from_continuation_token(
                polling_method=polling_method,
                continuation_token=cont_token,
                client=self._client,
                deserialization_callback=get_long_running_output,
            )
        return LROPoller[JSON](self._client, raw_result, get_long_running_output, polling_method)  # type: ignore