# pylint: disable=line-too-long,useless-suppression # 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. # -------------------------------------------------------------------------- """Customize generated code here. Follow our quickstart for examples: https://aka.ms/azsdk/python/dpcodegen/python/customize """ import json from typing import Any, Callable, Dict, IO, Mapping, Optional, TypeVar, Union, cast, overload, Generic, TYPE_CHECKING from collections.abc import MutableMapping # pylint:disable=import-error from urllib.parse import urlparse from azure.core.exceptions import HttpResponseError 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 azure.core.credentials import AzureKeyCredential from azure.core.paging import ItemPaged from ._client import TextAnalysisClient as AnalysisTextClientGenerated from . import models as _models from .models import AnalyzeTextOperationState, TextActions # convenience re-exports if present from ._utils.serialization import Serializer if TYPE_CHECKING: from azure.core.credentials import TokenCredential JSON = MutableMapping[str, Any] _Unset: Any = object() T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]] _SERIALIZER = Serializer() _SERIALIZER.client_side_validation = False def _parse_operation_id(op_loc: Optional[str]) -> Optional[str]: """Extract the operation ID from an Operation-Location URL. :param op_loc: The ``Operation-Location`` header value or URL to parse. If ``None`` or malformed, no ID can be extracted. :type op_loc: Optional[str] :return: The trailing path segment as the operation ID, or ``None`` if not found. :rtype: Optional[str] """ if not op_loc: return None path = urlparse(op_loc).path.rstrip("/") if "/" not in path: return None return path.rsplit("/", 1)[-1] PollingReturnType_co = TypeVar("PollingReturnType_co", covariant=True) class AnalyzeTextLROPoller(LROPoller[PollingReturnType_co], Generic[PollingReturnType_co]): """Custom poller that returns PollingReturnType_co and exposes operation metadata.""" def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) self._last_state: Optional[AnalyzeTextOperationState] = None # set by deserializer # internal: called by the deserializer to update details() def _record_state_for_details(self, state: AnalyzeTextOperationState) -> None: self._last_state = state @property def details(self) -> Mapping[str, Any]: """Metadata associated with the long-running operation. :return: A mapping with keys like ``operation_id`` and, when available, ``status``, ``job_id``, ``display_name``, ``created_at``, ``last_updated_at``, ``expires_on``, ``statistics``, ``errors``, and ``next_link``. :rtype: Mapping[str, Any] """ try: headers = getattr(self.polling_method(), "_initial_response").http_response.headers # type: ignore[attr-defined] op_loc = headers.get("Operation-Location") or headers.get("operation-location") except (AttributeError, TypeError): op_loc = None info: Dict[str, Any] = {"operation_id": _parse_operation_id(op_loc)} if self._last_state is not None: s = self._last_state info.update( { "status": s.status, "job_id": s.job_id, "display_name": s.display_name, "created_at": s.created_at, "last_updated_at": s.last_updated_at, "expires_on": s.expires_on, "statistics": s.statistics, "errors": s.errors, "next_link": s.next_link, } ) return info @classmethod def from_continuation_token( cls, polling_method: PollingMethod[PollingReturnType_co], continuation_token: str, **kwargs: Any, ) -> "AnalyzeTextLROPoller[PollingReturnType_co]": client, initial_response, deserialization_callback = polling_method.from_continuation_token( continuation_token, **kwargs ) return cls(client, initial_response, deserialization_callback, polling_method) class TextAnalysisClient(AnalysisTextClientGenerated): def __init__( self, endpoint: str, credential: Union[AzureKeyCredential, "TokenCredential"], *, api_version: Optional[str] = None, **kwargs: Any, ) -> None: """Create a TextAnalysisClient. :param endpoint: Supported Cognitive Services endpoint. :type endpoint: str :param credential: Key or token credential. :type credential: ~azure.core.credentials.AzureKeyCredential or ~azure.core.credentials.TokenCredential :keyword api_version: The API version to use for this operation. Default value is "2025-05-15-preview". Note that overriding this default value may result in unsupported behavior. :paramtype api_version: str` """ if api_version is not None: kwargs["api_version"] = api_version super().__init__(endpoint=endpoint, credential=credential, **kwargs) @overload def begin_analyze_text_job( self, *, text_input: _models.MultiLanguageTextInput, actions: list[_models.AnalyzeTextOperationAction], content_type: str = "application/json", display_name: Optional[str] = None, default_language: Optional[str] = None, cancel_after: Optional[float] = None, **kwargs: Any, ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]: """Submit a collection of text documents for analysis. Specify one or more unique tasks to be executed as a long-running operation. :keyword text_input: Contains the input to be analyzed. Required. :paramtype text_input: ~azure.ai.textanalytics.models.MultiLanguageTextInput :keyword actions: List of tasks to be performed as part of the LRO. Required. :paramtype actions: list[~azure.ai.textanalytics.models.AnalyzeTextOperationAction] :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. Default value is "application/json". :paramtype content_type: str :keyword display_name: Name for the task. Default value is None. :paramtype display_name: str :keyword default_language: Default language to use for records requesting automatic language detection. Default value is None. :paramtype default_language: str :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not completed. Default value is None. :paramtype cancel_after: float :return: A poller whose ``result()`` yields ``ItemPaged[TextActions]`` and exposes metadata via ``.details``. :rtype: ~azure.ai.textanalytics.AnalyzeTextLROPoller[ ~azure.core.paging.ItemPaged[~azure.ai.textanalytics.models.TextActions]] :raises ~azure.core.exceptions.HttpResponseError: """ @overload def begin_analyze_text_job( self, body: JSON, *, content_type: str = "application/json", **kwargs: Any ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]: """Submit a collection of text documents for analysis. Specify one or more unique tasks to be executed as a long-running operation. :param body: Required. :type body: JSON :keyword content_type: Body Parameter content-type. Content type parameter for JSON body. Default value is "application/json". :paramtype content_type: str :return: A poller whose ``result()`` yields ``ItemPaged[TextActions]`` and exposes metadata via ``.details``. :rtype: ~azure.ai.textanalytics.AnalyzeTextLROPoller[ ~azure.core.paging.ItemPaged[~azure.ai.textanalytics.models.TextActions]] :raises ~azure.core.exceptions.HttpResponseError: """ @overload def begin_analyze_text_job( self, body: IO[bytes], *, content_type: str = "application/json", **kwargs: Any ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]: """Submit a collection of text documents for analysis. Specify one or more unique tasks to be executed as a long-running operation. :param body: Required. :type body: 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: A poller whose ``result()`` yields ``ItemPaged[TextActions]`` and exposes metadata via ``.details``. :rtype: ~azure.ai.textanalytics.AnalyzeTextLROPoller[ ~azure.core.paging.ItemPaged[~azure.ai.textanalytics.models.TextActions]] :raises ~azure.core.exceptions.HttpResponseError: """ @distributed_trace def begin_analyze_text_job( # type: ignore[override] self, body: Union[JSON, IO[bytes]] = _Unset, *, text_input: _models.MultiLanguageTextInput = _Unset, actions: list[_models.AnalyzeTextOperationAction] = _Unset, display_name: Optional[str] = None, default_language: Optional[str] = None, cancel_after: Optional[float] = None, **kwargs: Any, ) -> AnalyzeTextLROPoller[ItemPaged["TextActions"]]: """Submit a collection of text documents for analysis. Specify one or more unique tasks to be executed as a long-running operation. :param body: Is either a JSON type or a IO[bytes] type. Required. :type body: JSON or IO[bytes] :keyword text_input: Contains the input to be analyzed. Required. :paramtype text_input: ~azure.ai.textanalytics.models.MultiLanguageTextInput :keyword actions: List of tasks to be performed as part of the LRO. Required. :paramtype actions: list[~azure.ai.textanalytics.models.AnalyzeTextOperationAction] :keyword display_name: Name for the task. Default value is None. :paramtype display_name: str :keyword default_language: Default language to use for records requesting automatic language detection. Default value is None. :paramtype default_language: str :keyword cancel_after: Optional duration in seconds after which the job will be canceled if not completed. Default value is None. :paramtype cancel_after: float :return: A poller whose ``result()`` yields ``ItemPaged[TextActions]`` and exposes metadata via ``.details``. :rtype: ~azure.ai.textanalytics.AnalyzeTextLROPoller[ ~azure.core.paging.ItemPaged[~azure.ai.textanalytics.models.TextActions]] :raises ~azure.core.exceptions.HttpResponseError: """ _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)) polling: Union[bool, PollingMethod[ItemPaged["TextActions"]]] = kwargs.pop("polling", True) lro_delay = kwargs.pop("polling_interval", self._config.polling_interval) cont_token: Optional[str] = kwargs.pop("continuation_token", None) cls: ClsType[ItemPaged["TextActions"]] = kwargs.pop("cls", None) kwargs.pop("error_map", None) path_format_arguments = { "Endpoint": self._serialize.url("self._config.endpoint", self._config.endpoint, "str", skip_quote=True), } def _fetch_state_by_next_link(next_link: str) -> AnalyzeTextOperationState: req = HttpRequest("GET", next_link) resp = self._client.send_request(req) # type: ignore[attr-defined] if resp.status_code != 200: raise HttpResponseError(response=resp) data = json.loads(resp.text()) return AnalyzeTextOperationState(data) def _build_pager_from_state(state: AnalyzeTextOperationState) -> ItemPaged["TextActions"]: def extract_data(s: AnalyzeTextOperationState): next_link = s.next_link actions_payload: TextActions = s.actions return next_link, [actions_payload] def get_next(token: Optional[str]) -> Optional[AnalyzeTextOperationState]: if token is None: # First call → return the initial state return state if not token: # No nextLink → stop iteration return None return _fetch_state_by_next_link(token) # Fetch next page return ItemPaged(get_next, extract_data) poller_holder: Dict[str, AnalyzeTextLROPoller[ItemPaged["TextActions"]]] = {} def get_long_running_output(pipeline_response: PipelineResponse[HttpRequest, HttpResponse]): final_response = pipeline_response.http_response if final_response.status_code == 200: data = json.loads(final_response.text()) op_state = AnalyzeTextOperationState(data) poller_ref = poller_holder["poller"] poller_ref._record_state_for_details(op_state) # pylint: disable=protected-access paged = _build_pager_from_state(op_state) return cls(pipeline_response, paged, {}) if cls else paged raise HttpResponseError(response=final_response) if polling is True: polling_method: PollingMethod[ItemPaged["TextActions"]] = cast( PollingMethod[ItemPaged["TextActions"]], LROBasePolling(lro_delay, path_format_arguments=path_format_arguments, **kwargs), ) elif polling is False: polling_method = cast(PollingMethod[ItemPaged["TextActions"]], NoPolling()) else: polling_method = cast(PollingMethod[ItemPaged["TextActions"]], polling) if cont_token: return AnalyzeTextLROPoller[ItemPaged["TextActions"]].from_continuation_token( polling_method=polling_method, continuation_token=cont_token, ) initial_kwargs = dict( # pylint:disable=use-dict-literal text_input=text_input, actions=actions, display_name=display_name, default_language=default_language, cancel_after=cancel_after, content_type=content_type, cls=lambda x, y, z: x, # passthrough raw pipeline response headers=_headers, params=_params, **kwargs, ) if body is not _Unset and body is not None: initial_kwargs["body"] = body raw_result = self._analyze_text_job_initial(**initial_kwargs) raw_result.http_response.read() # type: ignore[attr-defined] lro: AnalyzeTextLROPoller[ItemPaged["TextActions"]] = AnalyzeTextLROPoller( self._client, raw_result, get_long_running_output, polling_method ) poller_holder["poller"] = lro return lro def patch_sdk(): """Do not remove from this file. `patch_sdk` is a last resort escape hatch that allows you to do customizations you can't accomplish using the techniques described in https://aka.ms/azsdk/python/dpcodegen/python/customize """ __all__ = ["TextAnalysisClient", "AnalyzeTextLROPoller"]