




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=7      )       ->	Optional[Any]:

    """simple docstring"""


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

    # The id of a workflow (not of a workflow run)
    __UpperCamelCase  		=						'''636036'''

    __UpperCamelCase  		=						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}"

    __UpperCamelCase  		=						requests.get(__lowerCAmelCase					,     headers=__lowerCAmelCase      ).json()

    return result["workflow_runs"]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    __UpperCamelCase  		=						get_daily_ci_runs(__lowerCAmelCase      )
    __UpperCamelCase  		=						None
    for workflow_run in workflow_runs:
        if workflow_run["status"] == "completed":
            __UpperCamelCase  		=						workflow_run['''id''']
            break

    return workflow_run_id




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Optional[int]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	Any:

    """simple docstring"""


    get_last_daily_ci_artifacts(__lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase      )

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

    return results












from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






