
"""simple docstring"""





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

import requests



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Any:


					"""simple docstring"""





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

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

					try:
										job_links.update({job['''name''']: job['''html_url'''] for job in result['''jobs''']}      )
										_UpperCamelCase									=    math.ceil((result['''total_count'''] - 1_00) / 1_00      )

										for i in range(__snake_case      ):
															_UpperCamelCase									=    requests.get(url + F'''&page={i + 2}''',	headers=__snake_case      ).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__						(     __snake_case,	__snake_case=None      )				->						Any:


					"""simple docstring"""





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

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

					try:
										artifacts.update({artifact['''name''']: artifact['''archive_download_url'''] for artifact in result['''artifacts''']}      )
										_UpperCamelCase									=    math.ceil((result['''total_count'''] - 1_00) / 1_00      )

										for i in range(__snake_case      ):
															_UpperCamelCase									=    requests.get(url + F'''&page={i + 2}''',	headers=__snake_case      ).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__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    []
					_UpperCamelCase									=    None

					with zipfile.ZipFile(__snake_case      ) as z:
										for filename in z.namelist():
															if not os.path.isdir(__snake_case      ):
																				# read the file
																				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
																									with z.open(__snake_case      ) as f:
																														for line in f:
																																			_UpperCamelCase									=    line.decode('''UTF-8'''      ).strip()
																																			if filename == "failures_line.txt":
																																								try:
																																													# `error_line` is the place where `error` occurs
																																													_UpperCamelCase									=    line[: line.index(''': '''      )]
																																													_UpperCamelCase									=    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
																																								_UpperCamelCase									=    line[len('''FAILED '''      ) :]
																																								failed_tests.append(__snake_case      )
																																			elif filename == "job_name.txt":
																																								_UpperCamelCase									=    line

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

					_UpperCamelCase									=    None
					if job_name and job_links:
										_UpperCamelCase									=    job_links.get(__snake_case,	__snake_case      )

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

					return result



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    []

					_UpperCamelCase									=    [os.path.join(__snake_case,	__snake_case      ) for p in os.listdir(__snake_case      ) if p.endswith('''.zip'''      )]
					for p in paths:
										errors.extend(get_errors_from_single_artifact(__snake_case,	job_links=__snake_case      )      )

					return errors



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Optional[Any]:


					"""simple docstring"""





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

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



def  lowerCamelCase__						(     __snake_case      )				->						List[Any]:


					"""simple docstring"""





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

					return test



def  lowerCamelCase__						(     __snake_case,	__snake_case=None      )				->						Union[str, Any]:


					"""simple docstring"""





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

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

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



def  lowerCamelCase__						(     __snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    '''| no. | error | status |'''
					_UpperCamelCase									=    '''|-:|:-|:-|'''
					_UpperCamelCase									=    [header, sep]
					for error in reduced_by_error:
										_UpperCamelCase									=    reduced_by_error[error]['''count''']
										_UpperCamelCase									=    F'''| {count} | {error[:1_00]} |  |'''
										lines.append(__snake_case      )

					return "\n".join(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						Optional[int]:


					"""simple docstring"""





					_UpperCamelCase									=    '''| model | no. of errors | major error | count |'''
					_UpperCamelCase									=    '''|-:|-:|-:|-:|'''
					_UpperCamelCase									=    [header, sep]
					for model in reduced_by_model:
										_UpperCamelCase									=    reduced_by_model[model]['''count''']
										_UpperCamelCase					,    _UpperCamelCase									=    list(reduced_by_model[model]['''errors'''].items()      )[0]
										_UpperCamelCase									=    F'''| {model} | {count} | {error[:60]} | {_count} |'''
										lines.append(__snake_case      )

					return "\n".join(__snake_case      )


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()

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

				_a								=						get_job_links(args.workflow_run_id, token=args.token)
				_a								=						{}
				# 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								=						k.find(""" / """)
																_a								=						k[index + len(""" / """) :]
												_a								=						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								=						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								=						get_all_errors(args.output_dir, job_links=job_links)

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

				# print the top 30 most common test errors
				_a								=						counter.most_common(30)
				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								=						reduce_by_error(errors)
				_a								=						reduce_by_model(errors)

				_a								=						make_github_table(reduced_by_error)
				_a								=						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)








"""simple docstring"""





import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
				_a								=						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								=						parser.parse_args()

				if args.model_type == "bert":
								_a								=						BertForMaskedLM.from_pretrained(args.model_name)
								_a								=						"""bert"""
				else:
								raise ValueError("""args.model_type should be \"bert\".""")

				_a								=						model.state_dict()
				_a								=						{}

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

				_a								=						0
				for teacher_idx in [0, 2, 4, 7, 9, 11]:
								for w in ["weight", "bias"]:
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
												]

												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
												]
												_a								=						state_dict[
												    F"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
												]
								std_idx += 1

				_a								=						state_dict["""cls.predictions.decoder.weight"""]
				_a								=						state_dict["""cls.predictions.bias"""]
				if args.vocab_transform:
								for w in ["weight", "bias"]:
												_a								=						state_dict[F"""cls.predictions.transform.dense.{w}"""]
												_a								=						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)






