




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

import requests

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    None
							if token is not None:
														SCREAMING_SNAKE_CASE_   	=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

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

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

														for i in range(_lowerCAmelCase     ):
																					SCREAMING_SNAKE_CASE_   	=    requests.get(url + F"&page={i + 2}"  ,    headers=_lowerCAmelCase     ).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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    None
							if token is not None:
														SCREAMING_SNAKE_CASE_   	=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

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

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

														for i in range(_lowerCAmelCase     ):
																					SCREAMING_SNAKE_CASE_   	=    requests.get(url + F"&page={i + 2}"  ,    headers=_lowerCAmelCase     ).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 			a   (_lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    None
							if token is not None:
														SCREAMING_SNAKE_CASE_   	=    {'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    []
							SCREAMING_SNAKE_CASE_   	=    []
							SCREAMING_SNAKE_CASE_   	=    None

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

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

							SCREAMING_SNAKE_CASE_   	=    None
							if job_name and job_links:
														SCREAMING_SNAKE_CASE_   	=    job_links.get(_lowerCAmelCase  ,    _lowerCAmelCase     )

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

							return result

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    []

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

							return errors

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

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

def 			a   (_lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    test.split('''::'''     )[0]
							if test.startswith('''tests/models/'''     ):
														SCREAMING_SNAKE_CASE_   	=    test.split('''/'''     )[2]
							else:
														SCREAMING_SNAKE_CASE_   	=    None

							return test

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=None     ):
							SCREAMING_SNAKE_CASE_   	=    [(x[0], x[1], get_model(x[2]     )) for x in logs]
							SCREAMING_SNAKE_CASE_   	=    [x for x in logs if x[2] is not None]
							SCREAMING_SNAKE_CASE_   	=    {x[2] for x in logs}

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

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

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

							return "\n".join(_lowerCAmelCase     )

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

							return "\n".join(_lowerCAmelCase     )


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

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

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

			__SCREAMING_SNAKE_CASE 						=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)

			__SCREAMING_SNAKE_CASE 						=get_all_errors(args.output_dir, job_links=job_links)

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

			# print the top 30 most common test errors
			__SCREAMING_SNAKE_CASE 						=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)

			__SCREAMING_SNAKE_CASE 						=reduce_by_error(errors)
			__SCREAMING_SNAKE_CASE 						=reduce_by_model(errors)

			__SCREAMING_SNAKE_CASE 						=make_github_table(reduced_by_error)
			__SCREAMING_SNAKE_CASE 						=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 warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					"Wav2Vec2FeatureExtractor"
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					"AutoTokenizer"


				def __init__(							self:   Tuple		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[Any]		):
											super().__init__(_lowerCamelCase		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.feature_extractor
											SCREAMING_SNAKE_CASE_   	=    False


				@classmethod
				def     _A							(							cls:   List[Any]		,					_lowerCamelCase:   Tuple		,					**_lowerCamelCase:   List[str]		):
											try:
																		return super().from_pretrained(_lowerCamelCase		,					**_lowerCamelCase		)
											except OSError:
																		warnings.warn(
																		    f"Loading a tokenizer inside {cls.__name__} from a config that does not"
																		    ''' include a `tokenizer_class` attribute is deprecated and will be '''
																		    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
																		    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
																		    '''file to suppress this warning: '''		,					_lowerCamelCase		,					)

																		SCREAMING_SNAKE_CASE_   	=    WavaVecaFeatureExtractor.from_pretrained(_lowerCamelCase		,					**_lowerCamelCase		)
																		SCREAMING_SNAKE_CASE_   	=    WavaVecaCTCTokenizer.from_pretrained(_lowerCamelCase		,					**_lowerCamelCase		)

																		return cls(feature_extractor=_lowerCamelCase		,					tokenizer=_lowerCamelCase		)


				def __call__(							self:   Union[str, Any]		,					*_lowerCamelCase:   List[Any]		,					**_lowerCamelCase:   str		):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*_lowerCamelCase		,					**_lowerCamelCase		)

											if "raw_speech" in kwargs:
																		warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''		)
																		SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''raw_speech'''		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''audio'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''sampling_rate'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''text'''		,					_lowerCamelCase		)
											if len(_lowerCamelCase		) > 0:
																		SCREAMING_SNAKE_CASE_   	=    args[0]
																		SCREAMING_SNAKE_CASE_   	=    args[1:]

											if audio is None and text is None:
																		raise ValueError('''You need to specify either an `audio` or `text` input to process.'''		)

											if audio is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.feature_extractor(_lowerCamelCase		,					*_lowerCamelCase		,					sampling_rate=_lowerCamelCase		,					**_lowerCamelCase		)
											if text is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.tokenizer(_lowerCamelCase		,					**_lowerCamelCase		)

											if text is None:
																		return inputs
											elif audio is None:
																		return encodings
											else:
																		SCREAMING_SNAKE_CASE_   	=    encodings['''input_ids''']
																		return inputs


				def     _A							(							self:   Optional[int]		,					*_lowerCamelCase:   str		,					**_lowerCamelCase:   List[Any]		):
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*_lowerCamelCase		,					**_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''input_features'''		,					_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    kwargs.pop('''labels'''		,					_lowerCamelCase		)
											if len(_lowerCamelCase		) > 0:
																		SCREAMING_SNAKE_CASE_   	=    args[0]
																		SCREAMING_SNAKE_CASE_   	=    args[1:]

											if input_features is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.feature_extractor.pad(_lowerCamelCase		,					*_lowerCamelCase		,					**_lowerCamelCase		)
											if labels is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.tokenizer.pad(_lowerCamelCase		,					**_lowerCamelCase		)

											if labels is None:
																		return input_features
											elif input_features is None:
																		return labels
											else:
																		SCREAMING_SNAKE_CASE_   	=    labels['''input_ids''']
																		return input_features


				def     _A							(							self:   str		,					*_lowerCamelCase:   Dict		,					**_lowerCamelCase:   Dict		):
											return self.tokenizer.batch_decode(*_lowerCamelCase		,					**_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					*_lowerCamelCase:   Optional[int]		,					**_lowerCamelCase:   Tuple		):
											return self.tokenizer.decode(*_lowerCamelCase		,					**_lowerCamelCase		)





				@contextmanager
				def     _A							(							self:   Tuple		):
											warnings.warn(
											    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
											    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
											    '''your audio inputs, or in a separate call.'''		)
											SCREAMING_SNAKE_CASE_   	=    True
											SCREAMING_SNAKE_CASE_   	=    self.tokenizer
											yield
											SCREAMING_SNAKE_CASE_   	=    self.feature_extractor
											SCREAMING_SNAKE_CASE_   	=    False


