






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

import requests






def 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Optional[int]=None						):
  __UpperCAmelCase				:      Dict	 =    None
  if token is not None:
    __UpperCAmelCase				:      List[Any]	 =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}

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

  try:
    job_links.update({job["""name"""]: job["""html_url"""] for job in result["""jobs"""]}						)
    __UpperCAmelCase				:      Union[str, Any]	 =    math.ceil((result["""total_count"""] - 100) / 100						)

    for i in range(__lowerCamelCase						):
      __UpperCAmelCase				:      Optional[int]	 =    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 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Optional[int]=None						):
  __UpperCAmelCase				:      List[Any]	 =    None
  if token is not None:
    __UpperCAmelCase				:      List[Any]	 =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}

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

  try:
    artifacts.update({artifact["""name"""]: artifact["""archive_download_url"""] for artifact in result["""artifacts"""]}						)
    __UpperCAmelCase				:      Optional[int]	 =    math.ceil((result["""total_count"""] - 100) / 100						)

    for i in range(__lowerCamelCase						):
      __UpperCAmelCase				:      Any	 =    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 							lowerCamelCase__ (       __lowerCamelCase     :	Optional[Any]    ,						__lowerCamelCase     :	Dict    ,						__lowerCamelCase     :	Any    ,						__lowerCamelCase     :	Optional[int]						):
  __UpperCAmelCase				:      Union[str, Any]	 =    None
  if token is not None:
    __UpperCAmelCase				:      int	 =    {"""Accept""": """application/vnd.github+json""", """Authorization""": f"""Bearer {token}"""}

  __UpperCAmelCase				:      Tuple	 =    requests.get(__lowerCamelCase    ,						headers=__lowerCamelCase    ,						allow_redirects=__lowerCamelCase						)
  __UpperCAmelCase				:      Optional[Any]	 =    result.headers["""Location"""]
  __UpperCAmelCase				:      List[Any]	 =    requests.get(__lowerCamelCase    ,						allow_redirects=__lowerCamelCase						)
  __UpperCAmelCase				:      Dict	 =    os.path.join(__lowerCamelCase    ,						f"""{artifact_name}.zip"""						)
  with open(__lowerCamelCase    ,						"""wb"""						) as fp:
    fp.write(response.content						)






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

  __UpperCAmelCase				:      Dict	 =    None
  if job_name and job_links:
    __UpperCAmelCase				:      List[str]	 =    job_links.get(__lowerCamelCase    ,						__lowerCamelCase						)

  # A list with elements of the form (line of error, error, failed test)
  __UpperCAmelCase				:      Union[str, Any]	 =    [x + [y] + [job_link] for x, y in zip(__lowerCamelCase    ,						__lowerCamelCase						)]

  return result






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	Any=None						):
  __UpperCAmelCase				:      Optional[int]	 =    []

  __UpperCAmelCase				:      List[Any]	 =    [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 							lowerCamelCase__ (       __lowerCamelCase     :	Any    ,						__lowerCamelCase     :	int=None						):
  __UpperCAmelCase				:      str	 =    Counter()
  counter.update([x[1] for x in logs]						)
  __UpperCAmelCase				:      Union[str, Any]	 =    counter.most_common()
  __UpperCAmelCase				:      Optional[int]	 =    {}
  for error, count in counts:
    if error_filter is None or error not in error_filter:
      __UpperCAmelCase				:      str	 =    {"""count""": count, """failed_tests""": [(x[2], x[0]) for x in logs if x[1] == error]}

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






def 							lowerCamelCase__ (       __lowerCamelCase     :	Tuple						):
  __UpperCAmelCase				:      List[str]	 =    test.split("""::"""						)[0]
  if test.startswith("""tests/models/"""						):
    __UpperCAmelCase				:      List[Any]	 =    test.split("""/"""						)[2]
  else:
    __UpperCAmelCase				:      Any	 =    None

  return test






def 							lowerCamelCase__ (       __lowerCamelCase     :	List[Any]    ,						__lowerCamelCase     :	List[str]=None						):
  __UpperCAmelCase				:      Union[str, Any]	 =    [(x[0], x[1], get_model(x[2]						)) for x in logs]
  __UpperCAmelCase				:      Tuple	 =    [x for x in logs if x[2] is not None]
  __UpperCAmelCase				:      Union[str, Any]	 =    {x[2] for x in logs}

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

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






def 							lowerCamelCase__ (       __lowerCamelCase     :	Union[str, Any]						):
  __UpperCAmelCase				:      Union[str, Any]	 =    """| no. | error | status |"""
  __UpperCAmelCase				:      str	 =    """|-:|:-|:-|"""
  __UpperCAmelCase				:      str	 =    [header, sep]
  for error in reduced_by_error:
    __UpperCAmelCase				:      Tuple	 =    reduced_by_error[error]["""count"""]
    __UpperCAmelCase				:      List[Any]	 =    f"""| {count} | {error[:100]} |  |"""
    lines.append(__lowerCamelCase						)

  return "\n".join(__lowerCamelCase						)






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

  return "\n".join(__lowerCamelCase						)


if __name__ == "__main__":
       a						:			Tuple     	=					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						:			Union[str, Any]     	=					parser.parse_args()

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

       a						:			List[str]     	=					get_job_links(args.workflow_run_id, token=args.token)
       a						:			Optional[Any]     	=					{}
       # 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						:			Dict     	=					k.find(" / ")
                            a						:			Optional[Any]     	=					k[index + len(" / ") :]
                     a						:			Tuple     	=					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						:			Any     	=					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						:			Optional[Any]     	=					get_all_errors(args.output_dir, job_links=job_links)

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

       # print the top 30 most common test errors
       a						:			List[str]     	=					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						:			List[str]     	=					reduce_by_error(errors)
       a						:			str     	=					reduce_by_model(errors)

       a						:			Optional[Any]     	=					make_github_table(reduced_by_error)
       a						:			Union[str, Any]     	=					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 itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch

if is_datasets_available():
	from datasets import load_dataset

a							   =				random.Random()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1.0				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					List[str]:





			"""simple docstring"""







			if rng is None:
						snake_case:    Dict      							=global_rng

			snake_case:    Tuple      							=[]
			for batch_idx in range(shape[0]					):
						values.append([]					)
						for _ in range(shape[1]					):
									values[-1].append(rng.random() * scale					)

			return values






class 						a_		(     unittest.TestCase    ):


							def __init__(				self   :						int      ,						a_   :						Any      ,						a_   :						str=7      ,						a_   :						Tuple=4_0_0      ,						a_   :						List[Any]=2_0_0_0      ,						a_   :						str=2_0_4_8      ,						a_   :						List[str]=1_2_8      ,						a_   :						int=1      ,						a_   :						Tuple=5_1_2      ,						a_   :						Dict=3_0      ,						a_   :						Optional[int]=4_4_1_0_0      ,						)			->					Union[str, Any]:
										snake_case:    Union[str, Any]      							=parent
										snake_case:    Optional[Any]      							=batch_size
										snake_case:    Union[str, Any]      							=min_seq_length
										snake_case:    List[str]      							=max_seq_length
										snake_case:    str      							=(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
										snake_case:    int      							=spectrogram_length
										snake_case:    List[str]      							=feature_size
										snake_case:    Dict      							=num_audio_channels
										snake_case:    int      							=hop_length
										snake_case:    List[Any]      							=chunk_length
										snake_case:    Optional[Any]      							=sampling_rate


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										return {
										    "spectrogram_length": self.spectrogram_length,
										    "feature_size": self.feature_size,
										    "num_audio_channels": self.num_audio_channels,
										    "hop_length": self.hop_length,
										    "chunk_length": self.chunk_length,
										    "sampling_rate": self.sampling_rate,
										}




							def    UpperCamelCase     (				self   :						Tuple      ,						a_   :						Optional[Any]=False      ,						a_   :						Tuple=False			)			->					Optional[int]:
										def _flatten(a_   :						Dict			):
													return list(itertools.chain(*a_			)			)

										if equal_length:
													snake_case:    Union[str, Any]      							=[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
										else:
													# make sure that inputs increase in size
													snake_case:    Union[str, Any]      							=[
													    floats_list((x, self.feature_size)			)
													    for x in range(self.min_seq_length      ,						self.max_seq_length      ,						self.seq_length_diff			)
													]
										if numpify:
													snake_case:    Any      							=[np.asarray(a_			) for x in speech_inputs]
										return speech_inputs






@require_torch
@require_torchaudio
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		Optional[Any]        =				TvltFeatureExtractor


							def    UpperCamelCase     (				self   :						int			)			->					int:
										snake_case:    Union[str, Any]      							=TvltFeatureExtractionTester(self			)


							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					str:
										snake_case:    int      							=self.feature_extraction_class(**self.feat_extract_dict			)
										self.assertTrue(hasattr(a_      ,						'spectrogram_length'			)			)
										self.assertTrue(hasattr(a_      ,						'feature_size'			)			)
										self.assertTrue(hasattr(a_      ,						'num_audio_channels'			)			)
										self.assertTrue(hasattr(a_      ,						'hop_length'			)			)
										self.assertTrue(hasattr(a_      ,						'chunk_length'			)			)
										self.assertTrue(hasattr(a_      ,						'sampling_rate'			)			)


							def    UpperCamelCase     (				self   :						Tuple			)			->					Optional[int]:
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    Optional[Any]      							=feat_extract_first.save_pretrained(a_			)[0]
													check_json_file_has_correct_format(a_			)
													snake_case:    str      							=self.feature_extraction_class.from_pretrained(a_			)

										snake_case:    Optional[int]      							=feat_extract_first.to_dict()
										snake_case:    List[str]      							=feat_extract_second.to_dict()
										snake_case:    Optional[int]      							=dict_first.pop('mel_filters'			)
										snake_case:    List[Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						str			)			->					str:
										snake_case:    Any      							=self.feature_extraction_class(**self.feat_extract_dict			)

										with tempfile.TemporaryDirectory() as tmpdirname:
													snake_case:    int      							=os.path.join(a_      ,						'feat_extract.json'			)
													feat_extract_first.to_json_file(a_			)
													snake_case:    Union[str, Any]      							=self.feature_extraction_class.from_json_file(a_			)

										snake_case:    Optional[Any]      							=feat_extract_first.to_dict()
										snake_case:    Optional[Any]      							=feat_extract_second.to_dict()
										snake_case:    Union[str, Any]      							=dict_first.pop('mel_filters'			)
										snake_case:    Union[str, Any]      							=dict_second.pop('mel_filters'			)
										self.assertTrue(np.allclose(a_      ,						a_			)			)
										self.assertEqual(a_      ,						a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					List[str]:
										# Initialize feature_extractor
										snake_case:    Tuple      							=self.feature_extraction_class(**self.feat_extract_dict			)

										# create three inputs of length 800, 1000, and 1200
										snake_case:    Any      							=[floats_list((1, x)			)[0] for x in range(8_0_0      ,						1_4_0_0      ,						2_0_0			)]
										snake_case:    Optional[Any]      							=[np.asarray(a_			) for speech_input in speech_inputs]

										# Test not batched input
										snake_case:    List[Any]      							=feature_extractor(np_speech_inputs[0]      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test batched
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test audio masking
										snake_case:    Optional[Any]      							=feature_extractor(
										    a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0      ,						mask_audio=a_			).audio_values

										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)

										# Test 2-D numpy arrays are batched.
										snake_case:    List[Any]      							=[floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
										snake_case:    List[str]      							=np.asarray(a_			)
										snake_case:    str      							=feature_extractor(a_      ,						return_tensors='np'      ,						sampling_rate=4_4_1_0_0			).audio_values
										self.assertTrue(encoded_audios.ndim == 4			)
										self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size			)
										self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length			)
										self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels			)


							def    UpperCamelCase     (				self   :						Any      ,						a_   :						str			)			->					Union[str, Any]:
										snake_case:    Dict      							=load_dataset('hf-internal-testing/librispeech_asr_dummy'      ,						'clean'      ,						split='validation'			)
										# automatic decoding with librispeech
										snake_case:    Optional[int]      							=ds.sort('id'			).select(range(a_			)			)[:num_samples]['audio']

										return [x["array"] for x in speech_samples]




							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										snake_case:    Tuple      							=self._load_datasamples(1			)
										snake_case:    Any      							=TvltFeatureExtractor()
										snake_case:    Any      							=feature_extractor(a_      ,						return_tensors='pt'			).audio_values

										self.assertEquals(audio_values.shape      ,						(1, 1, 1_9_2, 1_2_8)			)

										snake_case:    int      							=torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]			)
										self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      ,						a_      ,						atol=1E-4			)			)




