





import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list
@require_torchaudio
@require_sentencepiece
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : str		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		'laion/clap-htsat-unfused'
							snake_case_						:     Union[str, Any]     =		tempfile.mkdtemp()






						def UpperCAmelCase_			( self : Optional[int]						,				**_A : Optional[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							return RobertaTokenizer.from_pretrained(self.checkpoint						,				**_A		)






						def UpperCAmelCase_			( self : Tuple						,				**_A : List[str]		)							->      int:







							"""simple docstring"""


							return ClapFeatureExtractor.from_pretrained(self.checkpoint						,				**_A		)






						def UpperCAmelCase_			( self : str		)							->      Optional[Any]:







							"""simple docstring"""


							shutil.rmtree(self.tmpdirname		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.get_tokenizer()
							snake_case_						:     int     =		self.get_feature_extractor()

							snake_case_						:     Optional[int]     =		ClapProcessor(tokenizer=_A						,				feature_extractor=_A		)

							processor.save_pretrained(self.tmpdirname		)
							snake_case_						:     Optional[int]     =		ClapProcessor.from_pretrained(self.tmpdirname		)

							self.assertEqual(processor.tokenizer.get_vocab()						,				tokenizer.get_vocab()		)
							self.assertIsInstance(processor.tokenizer						,				_A		)

							self.assertEqual(processor.feature_extractor.to_json_string()						,				feature_extractor.to_json_string()		)
							self.assertIsInstance(processor.feature_extractor						,				_A		)






						def UpperCAmelCase_			( self : Dict		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Any     =		ClapProcessor(tokenizer=self.get_tokenizer()						,				feature_extractor=self.get_feature_extractor()		)
							processor.save_pretrained(self.tmpdirname		)

							snake_case_						:     Tuple     =		self.get_tokenizer(bos_token='(BOS)'						,				eos_token='(EOS)'		)
							snake_case_						:     Dict     =		self.get_feature_extractor(do_normalize=_A						,				padding_value=1.0		)

							snake_case_						:     List[str]     =		ClapProcessor.from_pretrained(
							    self.tmpdirname						,				bos_token='(BOS)'						,				eos_token='(EOS)'						,				do_normalize=_A						,				padding_value=1.0		)

							self.assertEqual(processor.tokenizer.get_vocab()						,				tokenizer_add_kwargs.get_vocab()		)
							self.assertIsInstance(processor.tokenizer						,				_A		)

							self.assertEqual(processor.feature_extractor.to_json_string()						,				feature_extractor_add_kwargs.to_json_string()		)
							self.assertIsInstance(processor.feature_extractor						,				_A		)






						def UpperCAmelCase_			( self : str		)							->      str:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.get_feature_extractor()
							snake_case_						:     Optional[Any]     =		self.get_tokenizer()

							snake_case_						:     Tuple     =		ClapProcessor(tokenizer=_A						,				feature_extractor=_A		)

							snake_case_						:     Optional[Any]     =		floats_list((3, 1000)		)

							snake_case_						:     Optional[int]     =		feature_extractor(_A						,				return_tensors='np'		)
							snake_case_						:     Any     =		processor(audios=_A						,				return_tensors='np'		)

							for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()						,				input_processor[key].sum()						,				delta=1E-2		)






						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Any     =		self.get_feature_extractor()
							snake_case_						:     List[Any]     =		self.get_tokenizer()

							snake_case_						:     List[str]     =		ClapProcessor(tokenizer=_A						,				feature_extractor=_A		)

							snake_case_						:     Union[str, Any]     =		'This is a test string'

							snake_case_						:     str     =		processor(text=_A		)

							snake_case_						:     Any     =		tokenizer(_A		)

							for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]						,				encoded_processor[key]		)






						def UpperCAmelCase_			( self : Any		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     int     =		self.get_feature_extractor()
							snake_case_						:     List[Any]     =		self.get_tokenizer()

							snake_case_						:     Tuple     =		ClapProcessor(tokenizer=_A						,				feature_extractor=_A		)

							snake_case_						:     int     =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							snake_case_						:     Union[str, Any]     =		processor.batch_decode(_A		)
							snake_case_						:     int     =		tokenizer.batch_decode(_A		)

							self.assertListEqual(_A						,				_A		)






						def UpperCAmelCase_			( self : Optional[Any]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		self.get_feature_extractor()
							snake_case_						:     List[str]     =		self.get_tokenizer()

							snake_case_						:     Dict     =		ClapProcessor(tokenizer=_A						,				feature_extractor=_A		)

							self.assertListEqual(
							    processor.model_input_names[2:]						,				feature_extractor.model_input_names						,				msg='`processor` and `feature_extractor` model input names do not match'						,				)








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

import requests


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		None
	if token is not None:
		snake_case_						:     List[str]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

	snake_case_						:     Union[str, Any]     =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
	snake_case_						:     Optional[int]     =		requests.get(__a				,	headers=__a				).json()
	snake_case_						:     List[str]     =		{}

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

		for i in range(__a				):
			snake_case_						:     Optional[Any]     =		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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Union[str, Any]     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

	snake_case_						:     Optional[Any]     =		f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
	snake_case_						:     Union[str, Any]     =		requests.get(__a				,	headers=__a				).json()
	snake_case_						:     Any     =		{}

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

		for i in range(__a				):
			snake_case_						:     int     =		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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				):
	snake_case_						:     Dict     =		None
	if token is not None:
		snake_case_						:     List[Any]     =		{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

	snake_case_						:     Optional[int]     =		requests.get(__a				,	headers=__a				,	allow_redirects=__a				)
	snake_case_						:     str     =		result.headers['Location']
	snake_case_						:     List[str]     =		requests.get(__a				,	allow_redirects=__a				)
	snake_case_						:     Optional[Any]     =		os.path.join(__a				,	f"""{artifact_name}.zip"""				)
	with open(__a				,	'wb'				) as fp:
		fp.write(response.content				)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]
	snake_case_						:     Any     =		[]
	snake_case_						:     Tuple     =		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:
							snake_case_						:     Tuple     =		line.decode('UTF-8'				).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_						:     Tuple     =		line[: line.index(': '				)]
									snake_case_						:     Union[str, Any]     =		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
								snake_case_						:     Any     =		line[len('FAILED '				) :]
								failed_tests.append(__a				)
							elif filename == "job_name.txt":
								snake_case_						:     Union[str, Any]     =		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.'				)

	snake_case_						:     List[str]     =		None
	if job_name and job_links:
		snake_case_						:     Union[str, Any]     =		job_links.get(__a				,	__a				)

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

	return result


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Any     =		[]

	snake_case_						:     Any     =		[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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		Counter()
	counter.update([x[1] for x in logs]				)
	snake_case_						:     str     =		counter.most_common()
	snake_case_						:     Tuple     =		{}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			snake_case_						:     int     =		{'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Tuple     =		test.split('::'				)[0]
	if test.startswith('tests/models/'				):
		snake_case_						:     List[str]     =		test.split('/'				)[2]
	else:
		snake_case_						:     Union[str, Any]     =		None

	return test


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a=None				):
	snake_case_						:     Optional[int]     =		[(x[0], x[1], get_model(x[2]				)) for x in logs]
	snake_case_						:     str     =		[x for x in logs if x[2] is not None]
	snake_case_						:     int     =		{x[2] for x in logs}

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

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


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		'| no. | error | status |'
	snake_case_						:     str     =		'|-:|:-|:-|'
	snake_case_						:     Tuple     =		[header, sep]
	for error in reduced_by_error:
		snake_case_						:     Dict     =		reduced_by_error[error]['count']
		snake_case_						:     List[str]     =		f"""| {count} | {error[:1_00]} |  |"""
		lines.append(__a				)

	return "\n".join(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	snake_case_						:     Optional[Any]     =		'| model | no. of errors | major error | count |'
	snake_case_						:     Union[str, Any]     =		'|-:|-:|-:|-:|'
	snake_case_						:     Optional[int]     =		[header, sep]
	for model in reduced_by_model:
		snake_case_						:     Any     =		reduced_by_model[model]['count']
		snake_case_  ,snake_case_						:     Dict     =		list(reduced_by_model[model]['errors'].items()				)[0]
		snake_case_						:     Any     =		f"""| {model} | {count} | {error[:60]} | {_count} |"""
		lines.append(__a				)

	return "\n".join(__a				)


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)

