
'''simple docstring'''



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

import requests
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     Tuple:




   """simple docstring"""
   _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(SCREAMING_SNAKE_CASE_    ,      headers=SCREAMING_SNAKE_CASE_						).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_00) / 1_00						)

      for i in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				requests.get(url + F"&page={i + 2}"    ,      headers=SCREAMING_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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     Optional[int]:




   """simple docstring"""
   _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(SCREAMING_SNAKE_CASE_    ,      headers=SCREAMING_SNAKE_CASE_						).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_00) / 1_00						)

      for i in range(SCREAMING_SNAKE_CASE_						):
         _SCREAMING_SNAKE_CASE      =				requests.get(url + F"&page={i + 2}"    ,      headers=SCREAMING_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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _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(SCREAMING_SNAKE_CASE_    ,      headers=SCREAMING_SNAKE_CASE_    ,      allow_redirects=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				result.headers["""Location"""]
   _SCREAMING_SNAKE_CASE      =				requests.get(SCREAMING_SNAKE_CASE_    ,      allow_redirects=SCREAMING_SNAKE_CASE_						)
   _SCREAMING_SNAKE_CASE      =				os.path.join(SCREAMING_SNAKE_CASE_    ,      F"{artifact_name}.zip"						)
   with open(SCREAMING_SNAKE_CASE_    ,      """wb"""						) as fp:
      fp.write(response.content						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[]
   _SCREAMING_SNAKE_CASE      =				[]
   _SCREAMING_SNAKE_CASE      =				None

   with zipfile.ZipFile(SCREAMING_SNAKE_CASE_						) as z:
      for filename in z.namelist():
         if not os.path.isdir(SCREAMING_SNAKE_CASE_						):
            # read the file
            if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
               with z.open(SCREAMING_SNAKE_CASE_						) 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(SCREAMING_SNAKE_CASE_						)
                     elif filename == "job_name.txt":
                        _SCREAMING_SNAKE_CASE      =				line

   if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
      raise ValueError(
          F"`errors` and `failed_tests` should have the same number of elements. Got {len(SCREAMING_SNAKE_CASE_						)} for `errors` "
          F"and {len(SCREAMING_SNAKE_CASE_						)} 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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)

   # 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(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)]

   return result
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				[]

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

   return errors
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     Optional[int]:




   """simple docstring"""
   _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 SCREAMING_SNAKE_CASE_						: item[1]["count"]    ,      reverse=SCREAMING_SNAKE_CASE_						)						)
   return r
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     Union[str, Any]:




   """simple docstring"""
   _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    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None						)     ->     Optional[int]:




   """simple docstring"""
   _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 SCREAMING_SNAKE_CASE_						: item[1]["count"]    ,      reverse=SCREAMING_SNAKE_CASE_						)						)
   return r
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _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_00]} |  |"
      lines.append(SCREAMING_SNAKE_CASE_						)

   return "\n".join(SCREAMING_SNAKE_CASE_						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   _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[:60]} | {_count} |"
      lines.append(SCREAMING_SNAKE_CASE_						)

   return "\n".join(SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
     UpperCamelCase__      :		int       							=  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.")
     UpperCamelCase__      :		str       							=  parser.parse_args()

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

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

     UpperCamelCase__      :		List[str]       							=  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)

     UpperCamelCase__      :		List[str]       							=  get_all_errors(args.output_dir, job_links=job_links)

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

     # print the top 30 most common test errors
     UpperCamelCase__      :		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)

     UpperCamelCase__      :		Optional[Any]       							=  reduce_by_error(errors)
     UpperCamelCase__      :		Optional[int]       							=  reduce_by_model(errors)

     UpperCamelCase__      :		Optional[int]       							=  make_github_table(reduced_by_error)
     UpperCamelCase__      :		Optional[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'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase__      :		str       							=  {
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase__      :		List[Any]       							=  [
         "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "CanineForMultipleChoice",
         "CanineForQuestionAnswering",
         "CanineForSequenceClassification",
         "CanineForTokenClassification",
         "CanineLayer",
         "CanineModel",
         "CaninePreTrainedModel",
         "load_tf_weights_in_canine",
     ]


if TYPE_CHECKING:
     from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
     from .tokenization_canine import CanineTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_canine import (
              CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
              CanineForMultipleChoice,
              CanineForQuestionAnswering,
              CanineForSequenceClassification,
              CanineForTokenClassification,
              CanineLayer,
              CanineModel,
              CaninePreTrainedModel,
              load_tf_weights_in_canine,
          )


else:
     import sys

     UpperCamelCase__      :		str       							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






