import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			List[Any]			,						lowerCAmelCase_:			Dict=7  ):
      snake_case_  :      List[Any]  	=							None
      if token is not None:
            snake_case_  :      int  	=							{"""Accept""": """application/vnd.github+json""", """Authorization""": f"Bearer {token}"}

      # The id of a workflow (not of a workflow run)
      snake_case_  :      Any  	=							"""636036"""

      snake_case_  :      str  	=							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}"

      snake_case_  :      str  	=							requests.get(snake_case__			,						headers=snake_case__  ).json()

      return result["workflow_runs"]






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Dict  ):
      snake_case_  :      Dict  	=							get_daily_ci_runs(snake_case__  )
      snake_case_  :      Any  	=							None
      for workflow_run in workflow_runs:
            if workflow_run["status"] == "completed":
                  snake_case_  :      Optional[Any]  	=							workflow_run["""id"""]
                  break

      return workflow_run_id






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			List[Any]			,						lowerCAmelCase_:			Tuple			,						lowerCAmelCase_:			Optional[Any]  ):
      snake_case_  :      List[str]  	=							get_last_daily_ci_runs(snake_case__  )
      if workflow_run_id is not None:
            snake_case_  :      Optional[int]  	=							get_artifacts_links(worflow_run_id=snake_case__			,						token=snake_case__  )
            for artifact_name in artifact_names:
                  if artifact_name in artifacts_links:
                        snake_case_  :      Optional[Any]  	=							artifacts_links[artifact_name]
                        download_artifact(
                            artifact_name=snake_case__			,						artifact_url=snake_case__			,						output_dir=snake_case__			,						token=snake_case__  )






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			Tuple			,						lowerCAmelCase_:			Tuple			,						lowerCAmelCase_:			Union[str, Any]  ):
      get_last_daily_ci_artifacts(snake_case__			,						snake_case__			,						snake_case__  )

      snake_case_  :      Any  	=							{}
      for artifact_name in artifact_names:
            snake_case_  :      Optional[Any]  	=							os.path.join(snake_case__			,						f"{artifact_name}.zip"  )
            if os.path.isfile(snake_case__  ):
                  snake_case_  :      Dict  	=							{}
                  with zipfile.ZipFile(snake_case__  ) as z:
                        for filename in z.namelist():
                              if not os.path.isdir(snake_case__  ):
                                    # read the file
                                    with z.open(snake_case__  ) as f:
                                          snake_case_  :      Optional[int]  	=							f.read().decode("UTF-8"  )

      return results











from ...configuration_utils import PretrainedConfig


lowerCamelCase__       		=  {
    """google/tapas-base-finetuned-sqa""": (
        """https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wtq""": (
        """https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-wikisql-supervised""": (
        """https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json"""
    ),
    """google/tapas-base-finetuned-tabfact""": (
        """https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json"""
    ),
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					Tuple								=			"tapas"



       def __init__(				self      ,    a__=3_05_22      ,    a__=7_68      ,    a__=12      ,    a__=12      ,    a__=30_72      ,    a__="gelu"      ,    a__=0.1      ,    a__=0.1      ,    a__=10_24      ,    a__=[3, 2_56, 2_56, 2, 2_56, 2_56, 10]      ,    a__=0.02      ,    a__=1e-12      ,    a__=0      ,    a__=10.0      ,    a__=0      ,    a__=1.0      ,    a__=None      ,    a__=1.0      ,    a__=False      ,    a__=None      ,    a__=1.0      ,    a__=1.0      ,    a__=False      ,    a__=False      ,    a__="ratio"      ,    a__=None      ,    a__=None      ,    a__=64      ,    a__=32      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=False      ,    a__=True      ,    a__=False      ,    a__=None      ,    a__=None      ,    **a__      ,    )			->   Any:






            '''simple docstring'''





            super().__init__(pad_token_id=a__      ,    **a__     )

            # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
            __snake_case      :Optional[Any] 							=    vocab_size
            __snake_case      :Tuple 							=    hidden_size
            __snake_case      :Tuple 							=    num_hidden_layers
            __snake_case      :Any 							=    num_attention_heads
            __snake_case      :List[str] 							=    hidden_act
            __snake_case      :List[str] 							=    intermediate_size
            __snake_case      :int 							=    hidden_dropout_prob
            __snake_case      :List[Any] 							=    attention_probs_dropout_prob
            __snake_case      :Any 							=    max_position_embeddings
            __snake_case      :Optional[int] 							=    type_vocab_sizes
            __snake_case      :Tuple 							=    initializer_range
            __snake_case      :Any 							=    layer_norm_eps

            # Fine-tuning task hyperparameters
            __snake_case      :Optional[int] 							=    positive_label_weight
            __snake_case      :Optional[Any] 							=    num_aggregation_labels
            __snake_case      :Dict 							=    aggregation_loss_weight
            __snake_case      :str 							=    use_answer_as_supervision
            __snake_case      :int 							=    answer_loss_importance
            __snake_case      :List[Any] 							=    use_normalized_answer_loss
            __snake_case      :List[str] 							=    huber_loss_delta
            __snake_case      :Any 							=    temperature
            __snake_case      :Optional[int] 							=    aggregation_temperature
            __snake_case      :List[str] 							=    use_gumbel_for_cells
            __snake_case      :Optional[int] 							=    use_gumbel_for_aggregation
            __snake_case      :Optional[Any] 							=    average_approximation_function
            __snake_case      :Union[str, Any] 							=    cell_selection_preference
            __snake_case      :Tuple 							=    answer_loss_cutoff
            __snake_case      :Union[str, Any] 							=    max_num_rows
            __snake_case      :Optional[int] 							=    max_num_columns
            __snake_case      :str 							=    average_logits_per_cell
            __snake_case      :Union[str, Any] 							=    select_one_column
            __snake_case      :Optional[int] 							=    allow_empty_column_selection
            __snake_case      :Optional[Any] 							=    init_cell_selection_weights_to_zero
            __snake_case      :Tuple 							=    reset_position_index_per_cell
            __snake_case      :Any 							=    disable_per_token_loss

            # Aggregation hyperparameters
            __snake_case      :List[str] 							=    aggregation_labels
            __snake_case      :int 							=    no_aggregation_label_index

            if isinstance(self.aggregation_labels      ,    a__     ):
                 __snake_case      :Any 							=    {int(a__     ): v for k, v in aggregation_labels.items()}


