





"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links







def        __lowerCamelCase (							__a       :Dict   ,					__a       :List[Any]=7   )		->    Union[str, Any]:






		"""simple docstring"""

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

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

		A__  				=       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}'

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

		return result["workflow_runs"]







def        __lowerCamelCase (							__a       :List[Any]   )		->    Tuple:






		"""simple docstring"""

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

		return workflow_run_id







def        __lowerCamelCase (							__a       :List[str]   ,					__a       :int   ,					__a       :List[str]   )		->    Optional[int]:






		"""simple docstring"""

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







def        __lowerCamelCase (							__a       :Any   ,					__a       :Optional[Any]   ,					__a       :Dict   )		->    Dict:






		"""simple docstring"""

		get_last_daily_ci_artifacts(UpperCamelCase__   ,					UpperCamelCase__   ,					UpperCamelCase__   )

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

		return results










import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
					A   : List[str]			=				argparse.ArgumentParser(
					    description=(
					        '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
					        ''' Distillation'''
					    )
					)
					parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
					parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
					parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
					parser.add_argument('''--vocab_transform''', action='''store_true''')
					A   : Tuple			=				parser.parse_args()

					if args.model_type == "bert":
										A   : Dict			=				BertForMaskedLM.from_pretrained(args.model_name)
										A   : List[str]			=				'''bert'''
					else:
										raise ValueError('''args.model_type should be "bert".''')

					A   : Optional[Any]			=				model.state_dict()
					A   : int			=				{}

					for w in ["word_embeddings", "position_embeddings"]:
										A   : str			=				state_dict[F'''{prefix}.embeddings.{w}.weight''']
					for w in ["weight", "bias"]:
										A   : Any			=				state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

					A   : Tuple			=				0
					for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
										for w in ["weight", "bias"]:
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
															]
															A   : Optional[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
															]

															A   : int			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
															]
															A   : List[Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
															]

															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
															]
															A   : Union[str, Any]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
															]
															A   : List[str]			=				state_dict[
															    F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
															]
										std_idx += 1

					A   : int			=				state_dict['''cls.predictions.decoder.weight''']
					A   : str			=				state_dict['''cls.predictions.bias''']
					if args.vocab_transform:
										for w in ["weight", "bias"]:
															A   : List[Any]			=				state_dict[F'''cls.predictions.transform.dense.{w}''']
															A   : List[str]			=				state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

					print(F'''N layers selected for distillation: {std_idx}''')
					print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

					print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
					torch.save(compressed_sd, args.dump_checkpoint)



