





import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests







def        __lowerCamelCase (							__a       :Dict   ,					__a       :Tuple=None   )		->    Optional[Any]:






		"""simple docstring"""

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

		A__  				=       F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
		A__  				=       requests.get(__a   ,					headers=__a   ).json()
		A__  				=       {}

		try:
				job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )
				A__  				=       math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

				for i in range(__a   ):
						A__  				=       requests.get(url + F'&page={i + 2}'   ,					headers=__a   ).json()
						job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}   )

				return job_links
		except Exception:
				print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

		return {}







def        __lowerCamelCase (							__a       :Any   ,					__a       :Optional[Any]=None   )		->    Optional[Any]:






		"""simple docstring"""

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

		A__  				=       F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
		A__  				=       requests.get(__a   ,					headers=__a   ).json()
		A__  				=       {}

		try:
				artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )
				A__  				=       math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0   )

				for i in range(__a   ):
						A__  				=       requests.get(url + F'&page={i + 2}'   ,					headers=__a   ).json()
						artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}   )

				return artifacts
		except Exception:
				print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}'   )

		return {}







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






		"""simple docstring"""

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

		A__  				=       requests.get(__a   ,					headers=__a   ,					allow_redirects=__a   )
		A__  				=       result.headers["""Location"""]
		A__  				=       requests.get(__a   ,					allow_redirects=__a   )
		A__  				=       os.path.join(__a   ,					F'{artifact_name}.zip'   )
		with open(__a   ,					"""wb"""   ) as fp:
				fp.write(response.content   )







def        __lowerCamelCase (							__a       :Tuple   ,					__a       :int=None   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       []
		A__  				=       []
		A__  				=       None

		with zipfile.ZipFile(__a   ) as z:
				for filename in z.namelist():
						if not os.path.isdir(__a   ):
								# read the file
								if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
										with z.open(__a   ) as f:
												for line in f:
														A__  				=       line.decode("""UTF-8"""   ).strip()
														if filename == "failures_line.txt":
																try:
																		# `error_line` is the place where `error` occurs
																		A__  				=       line[: line.index(""": """   )]
																		A__  				=       line[line.index(""": """   ) + len(""": """   ) :]
																		errors.append([error_line, error]   )
																except Exception:
																		# skip un-related lines
																		pass
														elif filename == "summary_short.txt" and line.startswith("""FAILED """   ):
																# `test` is the test method that failed
																A__  				=       line[len("""FAILED """   ) :]
																failed_tests.append(__a   )
														elif filename == "job_name.txt":
																A__  				=       line

		if len(__a   ) != len(__a   ):
				raise ValueError(
				    F'`errors` and `failed_tests` should have the same number of elements. Got {len(__a   )} for `errors` '
				    F'and {len(__a   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some'
				    """ problem."""   )

		A__  				=       None
		if job_name and job_links:
				A__  				=       job_links.get(__a   ,					__a   )

		# A list with elements of the form (line of error, error, failed test)
		A__  				=       [x + [y] + [job_link] for x, y in zip(__a   ,					__a   )]

		return result







def        __lowerCamelCase (							__a       :Optional[Any]   ,					__a       :Union[str, Any]=None   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       []

		A__  				=       [os.path.join(__a   ,					__a   ) for p in os.listdir(__a   ) if p.endswith(""".zip"""   )]
		for p in paths:
				errors.extend(get_errors_from_single_artifact(__a   ,					job_links=__a   )   )

		return errors







def        __lowerCamelCase (							__a       :List[str]   ,					__a       :List[str]=None   )		->    Any:






		"""simple docstring"""

		A__  				=       Counter()
		counter.update([x[1] for x in logs]   )
		A__  				=       counter.most_common()
		A__  				=       {}
		for error, count in counts:
				if error_filter is None or error not in error_filter:
						A__  				=       {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

		A__  				=       dict(sorted(r.items()   ,					key=lambda __a   : item[1]["count"]   ,					reverse=__a   )   )
		return r







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






		"""simple docstring"""

		A__  				=       test.split("""::"""   )[0]
		if test.startswith("""tests/models/"""   ):
				A__  				=       test.split("""/"""   )[2]
		else:
				A__  				=       None

		return test







def        __lowerCamelCase (							__a       :Tuple   ,					__a       :str=None   )		->    Optional[Any]:






		"""simple docstring"""

		A__  				=       [(x[0], x[1], get_model(x[2]   )) for x in logs]
		A__  				=       [x for x in logs if x[2] is not None]
		A__  				=       {x[2] for x in logs}

		A__  				=       {}
		for test in tests:
				A__  				=       Counter()
				# count by errors in `test`
				counter.update([x[1] for x in logs if x[2] == test]   )
				A__  				=       counter.most_common()
				A__  				=       {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
				A__  				=       sum(error_counts.values()   )
				if n_errors > 0:
						A__  				=       {"""count""": n_errors, """errors""": error_counts}

		A__  				=       dict(sorted(r.items()   ,					key=lambda __a   : item[1]["count"]   ,					reverse=__a   )   )
		return r







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






		"""simple docstring"""

		A__  				=       """| no. | error | status |"""
		A__  				=       """|-:|:-|:-|"""
		A__  				=       [header, sep]
		for error in reduced_by_error:
				A__  				=       reduced_by_error[error]["""count"""]
				A__  				=       F'| {count} | {error[:1_0_0]} |  |'
				lines.append(__a   )

		return "\n".join(__a   )







def        __lowerCamelCase (							__a       :str   )		->    Any:






		"""simple docstring"""

		A__  				=       """| model | no. of errors | major error | count |"""
		A__  				=       """|-:|-:|-:|-:|"""
		A__  				=       [header, sep]
		for model in reduced_by_model:
				A__  				=       reduced_by_model[model]["""count"""]
				A__				,      A__  				=       list(reduced_by_model[model]["""errors"""].items()   )[0]
				A__  				=       F'| {model} | {count} | {error[:6_0]} | {_count} |'
				lines.append(__a   )

		return "\n".join(__a   )


if __name__ == "__main__":
					A   : str			=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
					parser.add_argument(
					    '''--output_dir''',
					    type=str,
					    required=True,
					    help='''Where to store the downloaded artifacts and other result files.''',
					)
					parser.add_argument('''--token''', default=None, type=str, help='''A token that has actions:read permission.''')
					A   : Optional[Any]			=				parser.parse_args()

					os.makedirs(args.output_dir, exist_ok=True)

					A   : List[Any]			=				get_job_links(args.workflow_run_id, token=args.token)
					A   : Tuple			=				{}
					# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
					# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
					if _job_links:
										for k, v in _job_links.items():
															# This is how GitHub actions combine job names.
															if " / " in k:
																				A   : int			=				k.find(''' / ''')
																				A   : str			=				k[index + len(''' / ''') :]
															A   : int			=				v
					with open(os.path.join(args.output_dir, '''job_links.json'''), '''w''', encoding='''UTF-8''') as fp:
										json.dump(job_links, fp, ensure_ascii=False, indent=4)

					A   : int			=				get_artifacts_links(args.workflow_run_id, token=args.token)
					with open(os.path.join(args.output_dir, '''artifacts.json'''), '''w''', encoding='''UTF-8''') as fp:
										json.dump(artifacts, fp, ensure_ascii=False, indent=4)

					for idx, (name, url) in enumerate(artifacts.items()):
										download_artifact(name, url, args.output_dir, args.token)
										# Be gentle to GitHub
										time.sleep(1)

					A   : str			=				get_all_errors(args.output_dir, job_links=job_links)

					# `e[1]` is the error
					A   : Tuple			=				Counter()
					counter.update([e[1] for e in errors])

					# print the top 30 most common test errors
					A   : Union[str, Any]			=				counter.most_common(3_0)
					for item in most_common:
										print(item)

					with open(os.path.join(args.output_dir, '''errors.json'''), '''w''', encoding='''UTF-8''') as fp:
										json.dump(errors, fp, ensure_ascii=False, indent=4)

					A   : str			=				reduce_by_error(errors)
					A   : Dict			=				reduce_by_model(errors)

					A   : Union[str, Any]			=				make_github_table(reduced_by_error)
					A   : List[str]			=				make_github_table_per_model(reduced_by_model)

					with open(os.path.join(args.output_dir, '''reduced_by_error.txt'''), '''w''', encoding='''UTF-8''') as fp:
										fp.write(sa)
					with open(os.path.join(args.output_dir, '''reduced_by_model.txt'''), '''w''', encoding='''UTF-8''') as fp:
										fp.write(sa)










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)



