

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__=7				):







							'''simple docstring'''






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

							# The id of a workflow (not of a workflow run)
							snake_case_          =   '636036'

							snake_case_          =   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}'''

							snake_case_          =   requests.get(UpperCamelCase__       ,							headers=UpperCamelCase__				).json()

							return result["workflow_runs"]





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   get_daily_ci_runs(UpperCamelCase__				)
							snake_case_          =   None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					snake_case_          =   workflow_run['id']
																					break

							return workflow_run_id





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							get_last_daily_ci_artifacts(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				)

							snake_case_          =   {}
							for artifact_name in artifact_names:
														snake_case_          =   os.path.join(UpperCamelCase__       ,							F'''{artifact_name}.zip'''				)
														if os.path.isfile(UpperCamelCase__				):
																					snake_case_          =   {}
																					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:
																																																	snake_case_          =   f.read().decode('UTF-8'				)

							return results








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

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


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	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(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




