


import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase=7  ):


			'''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(_UpperCamelCase       ,			headers=_UpperCamelCase  ).json()

			return result["workflow_runs"]


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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

			return workflow_run_id


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







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





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			get_last_daily_ci_artifacts(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

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

			return results









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
	)
	parser.add_argument(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


