

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=7       ):
						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(SCREAMING_SNAKE_CASE__  ,		headers=SCREAMING_SNAKE_CASE__       ).json()

						return result["workflow_runs"]


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


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

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

						return results
"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     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(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT 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."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





