



from ...processing_utils import ProcessorMixin






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""SpeechT5FeatureExtractor"""
	lowerCAmelCase	:   Any				=	"""SpeechT5Tokenizer"""



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							super().__init__(UpperCAmelCase__			,	UpperCAmelCase__		)



	def __call__(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       kwargs.pop("audio"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("text"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("text_target"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("audio_target"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("sampling_rate"			,	UpperCAmelCase__		)

							if audio is not None and text is not None:
													raise ValueError(
													    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"		)
							if audio_target is not None and text_target is not None:
													raise ValueError(
													    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"		)
							if audio is None and audio_target is None and text is None and text_target is None:
													raise ValueError(
													    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."		)

							if audio is not None:
													A__							=       self.feature_extractor(UpperCAmelCase__			,	*UpperCAmelCase__			,	sampling_rate=UpperCAmelCase__			,	**UpperCAmelCase__		)
							elif text is not None:
													A__							=       self.tokenizer(UpperCAmelCase__			,	**UpperCAmelCase__		)
							else:
													A__							=       None

							if audio_target is not None:
													A__							=       self.feature_extractor(audio_target=UpperCAmelCase__			,	*UpperCAmelCase__			,	sampling_rate=UpperCAmelCase__			,	**UpperCAmelCase__		)
													A__							=       targets["input_values"]
							elif text_target is not None:
													A__							=       self.tokenizer(UpperCAmelCase__			,	**UpperCAmelCase__		)
													A__							=       targets["input_ids"]
							else:
													A__							=       None

							if inputs is None:
													return targets

							if targets is not None:
													A__							=       labels

													A__							=       targets.get("attention_mask"		)
													if decoder_attention_mask is not None:
																			A__							=       decoder_attention_mask

							return inputs



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							A__							=       kwargs.pop("input_values"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("input_ids"			,	UpperCAmelCase__		)
							A__							=       kwargs.pop("labels"			,	UpperCAmelCase__		)

							if input_values is not None and input_ids is not None:
													raise ValueError("Cannot process both `input_values` and `input_ids` inputs."		)
							if input_values is None and input_ids is None and labels is None:
													raise ValueError(
													    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."		)

							if input_values is not None:
													A__							=       self.feature_extractor.pad(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
							elif input_ids is not None:
													A__							=       self.tokenizer.pad(UpperCAmelCase__			,	**UpperCAmelCase__		)
							else:
													A__							=       None

							if labels is not None:
													if "input_ids" in labels or (isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) and "input_ids" in labels[0]):
																			A__							=       self.tokenizer.pad(UpperCAmelCase__			,	**UpperCAmelCase__		)
																			A__							=       targets["input_ids"]
													else:
																			A__							=       self.feature_extractor.feature_size
																			A__							=       self.feature_extractor.num_mel_bins
																			A__							=       self.feature_extractor.pad(UpperCAmelCase__			,	*UpperCAmelCase__			,	**UpperCAmelCase__		)
																			A__							=       feature_size_hack
																			A__							=       targets["input_values"]
							else:
													A__							=       None

							if inputs is None:
													return targets

							if targets is not None:
													A__							=       labels

													A__							=       targets.get("attention_mask"		)
													if decoder_attention_mask is not None:
																			A__							=       decoder_attention_mask

							return inputs



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.batch_decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)



	def 			__A							(       self			,	*UpperCAmelCase__			,	**UpperCAmelCase__		):
							return self.tokenizer.decode(*UpperCAmelCase__			,	**UpperCAmelCase__		)










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





