




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links







def 		lowercase__   (		__A:	int							,__A:	Optional[Any]=7				):





					'''simple docstring'''






					__magic_name__							:	List[str]    							=							None
					if token is not None:
										__magic_name__							:	Optional[int]    							=							{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F'''Bearer {token}'''}

					# The id of a workflow (not of a workflow run)
					__magic_name__							:	List[str]    							=							'''636036'''

					__magic_name__							:	int    							=							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}'''

					__magic_name__							:	str    							=							requests.get(__A							,headers=__A				).json()

					return result["workflow_runs"]







def 		lowercase__   (		__A:	str				):





					'''simple docstring'''






					__magic_name__							:	int    							=							get_daily_ci_runs(__A				)
					__magic_name__							:	Tuple    							=							None
					for workflow_run in workflow_runs:
										if workflow_run["status"] == "completed":
															__magic_name__							:	List[Any]    							=							workflow_run['''id''']
															break

					return workflow_run_id







def 		lowercase__   (		__A:	List[Any]							,__A:	Optional[Any]							,__A:	Union[str, Any]				):





					'''simple docstring'''






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







def 		lowercase__   (		__A:	int							,__A:	Optional[int]							,__A:	Optional[int]				):





					'''simple docstring'''






					get_last_daily_ci_artifacts(__A							,__A							,__A				)

					__magic_name__							:	List[str]    							=							{}
					for artifact_name in artifact_names:
										__magic_name__							:	str    							=							os.path.join(__A							,F'''{artifact_name}.zip'''				)
										if os.path.isfile(__A				):
															__magic_name__							:	List[str]    							=							{}
															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:
																																			__magic_name__							:	Optional[int]    							=							f.read().decode('''UTF-8'''				)

					return results









from typing import TYPE_CHECKING

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


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



