


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A			:						Tuple        =  {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[Any]        =  ['WhisperTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						int        =  [
	    'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'WhisperForConditionalGeneration',
	    'WhisperModel',
	    'WhisperPreTrainedModel',
	    'WhisperForAudioClassification',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						Optional[int]        =  [
	    'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFWhisperForConditionalGeneration',
	    'TFWhisperModel',
	    'TFWhisperPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'FlaxWhisperForConditionalGeneration',
	    'FlaxWhisperModel',
	    'FlaxWhisperPreTrainedModel',
	    'FlaxWhisperForAudioClassification',
	]


if TYPE_CHECKING:
	from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
	from .feature_extraction_whisper import WhisperFeatureExtractor
	from .processing_whisper import WhisperProcessor
	from .tokenization_whisper import WhisperTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_whisper_fast import WhisperTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_whisper import (
		    WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WhisperForAudioClassification,
		    WhisperForConditionalGeneration,
		    WhisperModel,
		    WhisperPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_whisper import (
		    TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWhisperForConditionalGeneration,
		    TFWhisperModel,
		    TFWhisperPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_whisper import (
		    FlaxWhisperForAudioClassification,
		    FlaxWhisperForConditionalGeneration,
		    FlaxWhisperModel,
		    FlaxWhisperPreTrainedModel,
		)

else:
	import sys

	A			:						Any        =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				List[str]=7	)  ->    Dict:
		"""simple docstring"""




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

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

		lowercase__	     =						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}'''

		lowercase__	     =						requests.get(__magic_name__       ,       headers=__magic_name__	).json()

		return result["workflow_runs"]






def 					UpperCamelCase   (      __magic_name__	:				str	)  ->    Dict:
		"""simple docstring"""




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

		return workflow_run_id






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				Dict	)  ->    str:
		"""simple docstring"""




		lowercase__	     =						get_last_daily_ci_runs(__magic_name__	)
		if workflow_run_id is not None:
				lowercase__	     =						get_artifacts_links(worflow_run_id=__magic_name__       ,       token=__magic_name__	)
				for artifact_name in artifact_names:
						if artifact_name in artifacts_links:
								lowercase__	     =						artifacts_links[artifact_name]
								download_artifact(
								    artifact_name=__magic_name__       ,       artifact_url=__magic_name__       ,       output_dir=__magic_name__       ,       token=__magic_name__	)







def 					UpperCamelCase   (      __magic_name__	:				List[str]       ,       __magic_name__	:				str       ,       __magic_name__	:				Union[str, Any]	)  ->    Tuple:
		"""simple docstring"""




		get_last_daily_ci_artifacts(__magic_name__       ,       __magic_name__       ,       __magic_name__	)

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

		return results

