





"""simple docstring"""
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		Tuple=None							)      ->				Any:
      __SCREAMING_SNAKE_CASE       :int		    =  None
      if token is not None:
            __SCREAMING_SNAKE_CASE       :Any		    =  {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

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

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

            for i in range(a_							):
                  __SCREAMING_SNAKE_CASE       :Tuple		    =  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_					:		Dict			,       a_					:		Optional[Any]=None							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  None
      if token is not None:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

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

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

            for i in range(a_							):
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  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_					:		int			,       a_					:		Dict			,       a_					:		str			,       a_					:		str							)      ->				str:
      __SCREAMING_SNAKE_CASE       :str		    =  None
      if token is not None:
            __SCREAMING_SNAKE_CASE       :Any		    =  {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

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



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

      __SCREAMING_SNAKE_CASE       :int		    =  None
      if job_name and job_links:
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  job_links.get(a_			,       a_							)

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

      return result



def 					__lowerCamelCase       (							a_					:		Tuple			,       a_					:		Any=None							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  []

      __SCREAMING_SNAKE_CASE       :Dict		    =  [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_					:		Optional[Any]			,       a_					:		List[Any]=None							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  Counter()
      counter.update([x[1] for x in logs]							)
      __SCREAMING_SNAKE_CASE       :int		    =  counter.most_common()
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  {}
      for error, count in counts:
            if error_filter is None or error not in error_filter:
                  __SCREAMING_SNAKE_CASE       :Optional[int]		    =  {'''count''': count, '''failed_tests''': [(x[2], x[0]) for x in logs if x[1] == error]}

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



def 					__lowerCamelCase       (							a_					:		Optional[Any]							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :Dict		    =  test.split('''::'''							)[0]
      if test.startswith('''tests/models/'''							):
            __SCREAMING_SNAKE_CASE       :Optional[int]		    =  test.split('''/'''							)[2]
      else:
            __SCREAMING_SNAKE_CASE       :Tuple		    =  None

      return test



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		Optional[Any]=None							)      ->				Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  [(x[0], x[1], get_model(x[2]							)) for x in logs]
      __SCREAMING_SNAKE_CASE       :Tuple		    =  [x for x in logs if x[2] is not None]
      __SCREAMING_SNAKE_CASE       :List[str]		    =  {x[2] for x in logs}

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

      __SCREAMING_SNAKE_CASE       :List[str]		    =  dict(sorted(r.items()			,       key=lambda a_							: item[1]["count"]			,       reverse=a_							)							)
      return r



def 					__lowerCamelCase       (							a_					:		Any							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  '''| no. | error | status |'''
      __SCREAMING_SNAKE_CASE       :Tuple		    =  '''|-:|:-|:-|'''
      __SCREAMING_SNAKE_CASE       :List[str]		    =  [header, sep]
      for error in reduced_by_error:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  reduced_by_error[error]['''count''']
            __SCREAMING_SNAKE_CASE       :Dict		    =  f'''| {count} | {error[:1_00]} |  |'''
            lines.append(a_							)

      return "\n".join(a_							)



def 					__lowerCamelCase       (							a_					:		Tuple							)      ->				Tuple:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  '''| model | no. of errors | major error | count |'''
      __SCREAMING_SNAKE_CASE       :str		    =  '''|-:|-:|-:|-:|'''
      __SCREAMING_SNAKE_CASE       :List[str]		    =  [header, sep]
      for model in reduced_by_model:
            __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  reduced_by_model[model]['''count''']
            __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  list(reduced_by_model[model]['''errors'''].items()							)[0]
            __SCREAMING_SNAKE_CASE       :List[str]		    =  f'''| {model} | {count} | {error[:60]} | {_count} |'''
            lines.append(a_							)

      return "\n".join(a_							)


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

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

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

  lowerCamelCase_       =		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)

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

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

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

  lowerCamelCase_       =		reduce_by_error(errors)
  lowerCamelCase_       =		reduce_by_model(errors)

  lowerCamelCase_       =		make_github_table(reduced_by_error)
  lowerCamelCase_       =		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 (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCamelCase_       =		logging.get_logger(__name__)



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[int]			,       a_					:		Any							)      ->				List[Any]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForSequenceClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict['''projector.weight''']
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  downstream_dict['''projector.bias''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.post_net.linear.weight''']
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.post_net.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Union[str, Any]			,       a_					:		List[Any]			,       a_					:		List[Any]							)      ->				Optional[int]:
      __SCREAMING_SNAKE_CASE       :Any		    =  UniSpeechSatForAudioFrameClassification.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  downstream_dict['''model.linear.weight''']
      __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  downstream_dict['''model.linear.bias''']
      return model



def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Optional[Any]			,       a_					:		int							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :List[str]		    =  UniSpeechSatForXVector.from_pretrained(a_			,       config=a_							)
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''connector.weight''']
      __SCREAMING_SNAKE_CASE       :Tuple		    =  downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict[
                f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            __SCREAMING_SNAKE_CASE       :int		    =  downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      __SCREAMING_SNAKE_CASE       :Any		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      __SCREAMING_SNAKE_CASE       :Dict		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      __SCREAMING_SNAKE_CASE       :str		    =  downstream_dict['''objective.W''']
      return model



@torch.no_grad()
def 					__lowerCamelCase       (							a_					:		Optional[int]			,       a_					:		Union[str, Any]			,       a_					:		Any			,       a_					:		Union[str, Any]							)      ->				List[str]:
      __SCREAMING_SNAKE_CASE       :str		    =  torch.load(a_			,       map_location='''cpu'''							)

      __SCREAMING_SNAKE_CASE       :str		    =  checkpoint['''Downstream''']

      __SCREAMING_SNAKE_CASE       :str		    =  UniSpeechSatConfig.from_pretrained(a_							)
      __SCREAMING_SNAKE_CASE       :List[str]		    =  WavaVecaFeatureExtractor.from_pretrained(
          a_			,       return_attention_mask=a_			,       do_normalize=a_							)

      __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  hf_config.architectures[0]
      if arch.endswith('''ForSequenceClassification'''							):
            __SCREAMING_SNAKE_CASE       :str		    =  convert_classification(a_			,       a_			,       a_							)
      elif arch.endswith('''ForAudioFrameClassification'''							):
            __SCREAMING_SNAKE_CASE       :Tuple		    =  convert_diarization(a_			,       a_			,       a_							)
      elif arch.endswith('''ForXVector'''							):
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  convert_xvector(a_			,       a_			,       a_							)
      else:
            raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''							)

      if hf_config.use_weighted_layer_sum:
            __SCREAMING_SNAKE_CASE       :Dict		    =  checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(a_							)
      hf_model.save_pretrained(a_							)


if __name__ == "__main__":
  lowerCamelCase_       =		argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  lowerCamelCase_       =		parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)