
'''simple docstring'''



import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=7						)     ->     Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          =				None
   if token is not None:
      _SCREAMING_SNAKE_CASE          =				{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

   # The id of a workflow (not of a workflow run)
   _SCREAMING_SNAKE_CASE          =				"636036"

   _SCREAMING_SNAKE_CASE          =				F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
   # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
   url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"

   _SCREAMING_SNAKE_CASE          =				requests.get(__SCREAMING_SNAKE_CASE    ,      headers=__SCREAMING_SNAKE_CASE						).json()

   return result["workflow_runs"]
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     int:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          =				get_daily_ci_runs(__SCREAMING_SNAKE_CASE						)
   _SCREAMING_SNAKE_CASE          =				None
   for workflow_run in workflow_runs:
      if workflow_run["status"] == "completed":
         _SCREAMING_SNAKE_CASE          =				workflow_run["id"]
         break

   return workflow_run_id
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE          =				get_last_daily_ci_runs(__SCREAMING_SNAKE_CASE						)
   if workflow_run_id is not None:
      _SCREAMING_SNAKE_CASE          =				get_artifacts_links(worflow_run_id=__SCREAMING_SNAKE_CASE    ,      token=__SCREAMING_SNAKE_CASE						)
      for artifact_name in artifact_names:
         if artifact_name in artifacts_links:
            _SCREAMING_SNAKE_CASE          =				artifacts_links[artifact_name]
            download_artifact(
                artifact_name=__SCREAMING_SNAKE_CASE    ,      artifact_url=__SCREAMING_SNAKE_CASE    ,      output_dir=__SCREAMING_SNAKE_CASE    ,      token=__SCREAMING_SNAKE_CASE						)







def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     List[Any]:




   """simple docstring"""
   get_last_daily_ci_artifacts(__SCREAMING_SNAKE_CASE    ,      __SCREAMING_SNAKE_CASE    ,      __SCREAMING_SNAKE_CASE						)

   _SCREAMING_SNAKE_CASE          =				{}
   for artifact_name in artifact_names:
      _SCREAMING_SNAKE_CASE          =				os.path.join(__SCREAMING_SNAKE_CASE    ,      F"{artifact_name}.zip"						)
      if os.path.isfile(__SCREAMING_SNAKE_CASE						):
         _SCREAMING_SNAKE_CASE          =				{}
         with zipfile.ZipFile(__SCREAMING_SNAKE_CASE						) as z:
            for filename in z.namelist():
               if not os.path.isdir(__SCREAMING_SNAKE_CASE						):
                  # read the file
                  with z.open(__SCREAMING_SNAKE_CASE						) as f:
                     _SCREAMING_SNAKE_CASE          =				f.read().decode("""UTF-8"""						)

   return results








'''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__)






