





'''simple docstring'''

import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=7      )						->					int:

   '''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(__UpperCAmelCase							,	headers=__UpperCAmelCase      ).json()

   return result["workflow_runs"]

def 						__magic_name__       (							__UpperCAmelCase      )						->					Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       get_daily_ci_runs(__UpperCAmelCase      )
   __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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







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







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   get_last_daily_ci_artifacts(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

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

   return results












'''simple docstring'''

import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD

def 						__magic_name__       (							__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   def wrapper(*__UpperCAmelCase							,	**__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer()
      __SCREAMING_SNAKE_CASE      							=       func(*__UpperCAmelCase							,	**__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       timeit.default_timer() - starttime
      return delta

   __SCREAMING_SNAKE_CASE      							=       func.__name__

   return wrapper

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					Optional[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   __SCREAMING_SNAKE_CASE      							=       seq_shapes or {}
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       {}
      for col_id, (k, v) in enumerate(features.items()      ):
         if isinstance(__UpperCAmelCase							,	_ArrayXD      ):
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*v.shape      ).astype(v.dtype      )
         elif isinstance(__UpperCAmelCase							,	datasets.Value      ):
            if v.dtype == "string":
               __SCREAMING_SNAKE_CASE      							=       """The small grey turtle was surprisingly fast when challenged."""
            else:
               __SCREAMING_SNAKE_CASE      							=       np.random.randint(10							,	size=1      ).astype(v.dtype      ).item()
         elif isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
            while isinstance(__UpperCAmelCase							,	datasets.Sequence      ):
               __SCREAMING_SNAKE_CASE      							=       v.feature
            __SCREAMING_SNAKE_CASE      							=       seq_shapes[k]
            __SCREAMING_SNAKE_CASE      							=       np.random.rand(*__UpperCAmelCase      ).astype(v.dtype      )
         __SCREAMING_SNAKE_CASE      							=       data

      dummy_data.append((i, example)      )

   return dummy_data







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=100							,	__UpperCAmelCase=None      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       generate_examples(__UpperCAmelCase							,	num_examples=__UpperCAmelCase							,	seq_shapes=__UpperCAmelCase      )

   with ArrowWriter(features=__UpperCAmelCase							,	path=__UpperCAmelCase      ) as writer:
      for key, record in dummy_data:
         __SCREAMING_SNAKE_CASE      							=       features.encode_example(__UpperCAmelCase      )
         writer.write(__UpperCAmelCase      )

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""      )

   __SCREAMING_SNAKE_CASE      							=       datasets.Dataset.from_file(filename=__UpperCAmelCase							,	info=datasets.DatasetInfo(features=__UpperCAmelCase      )      )

   return dataset





