from ...configuration_utils import PretrainedConfig


A   :     Tuple       =				{
    "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      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''tapas'''







  def __init__(							self       :				Dict     ,   __magic_name__       :				int=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=12     ,   __magic_name__       :				Optional[Any]=12     ,   __magic_name__       :				Optional[Any]=3_072     ,   __magic_name__       :				Any="gelu"     ,   __magic_name__       :				str=0.1     ,   __magic_name__       :				int=0.1     ,   __magic_name__       :				Any=1_024     ,   __magic_name__       :				List[Any]=[3, 256, 256, 2, 256, 256, 10]     ,   __magic_name__       :				Optional[int]=0.02     ,   __magic_name__       :				Optional[int]=1e-12     ,   __magic_name__       :				Optional[int]=0     ,   __magic_name__       :				str=10.0     ,   __magic_name__       :				Optional[Any]=0     ,   __magic_name__       :				Any=1.0     ,   __magic_name__       :				List[Any]=None     ,   __magic_name__       :				List[Any]=1.0     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				List[str]=1.0     ,   __magic_name__       :				Dict=1.0     ,   __magic_name__       :				Union[str, Any]=False     ,   __magic_name__       :				Dict=False     ,   __magic_name__       :				Union[str, Any]="ratio"     ,   __magic_name__       :				List[Any]=None     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				Dict=64     ,   __magic_name__       :				Dict=32     ,   __magic_name__       :				Union[str, Any]=False     ,   __magic_name__       :				Optional[int]=True     ,   __magic_name__       :				Dict=False     ,   __magic_name__       :				List[Any]=False     ,   __magic_name__       :				str=True     ,   __magic_name__       :				Dict=False     ,   __magic_name__       :				Dict=None     ,   __magic_name__       :				Optional[Any]=None     ,   **__magic_name__       :				Union[str, Any]     ,   )     ->		Any:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        # BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_sizes
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps

        # Fine-tuning task hyperparameters
        SCREAMING_SNAKE_CASE_      =     positive_label_weight
        SCREAMING_SNAKE_CASE_      =     num_aggregation_labels
        SCREAMING_SNAKE_CASE_      =     aggregation_loss_weight
        SCREAMING_SNAKE_CASE_      =     use_answer_as_supervision
        SCREAMING_SNAKE_CASE_      =     answer_loss_importance
        SCREAMING_SNAKE_CASE_      =     use_normalized_answer_loss
        SCREAMING_SNAKE_CASE_      =     huber_loss_delta
        SCREAMING_SNAKE_CASE_      =     temperature
        SCREAMING_SNAKE_CASE_      =     aggregation_temperature
        SCREAMING_SNAKE_CASE_      =     use_gumbel_for_cells
        SCREAMING_SNAKE_CASE_      =     use_gumbel_for_aggregation
        SCREAMING_SNAKE_CASE_      =     average_approximation_function
        SCREAMING_SNAKE_CASE_      =     cell_selection_preference
        SCREAMING_SNAKE_CASE_      =     answer_loss_cutoff
        SCREAMING_SNAKE_CASE_      =     max_num_rows
        SCREAMING_SNAKE_CASE_      =     max_num_columns
        SCREAMING_SNAKE_CASE_      =     average_logits_per_cell
        SCREAMING_SNAKE_CASE_      =     select_one_column
        SCREAMING_SNAKE_CASE_      =     allow_empty_column_selection
        SCREAMING_SNAKE_CASE_      =     init_cell_selection_weights_to_zero
        SCREAMING_SNAKE_CASE_      =     reset_position_index_per_cell
        SCREAMING_SNAKE_CASE_      =     disable_per_token_loss

        # Aggregation hyperparameters
        SCREAMING_SNAKE_CASE_      =     aggregation_labels
        SCREAMING_SNAKE_CASE_      =     no_aggregation_label_index

        if isinstance(self.aggregation_labels     ,   __magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     {int(__magic_name__      ): v for k, v in aggregation_labels.items()}



import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
A   :     Tuple       =				"src/transformers"
A   :     Optional[Any]       =				"docs/source/en/tasks"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      with open(__UpperCamelCase   ,					"r"   ,					encoding="utf-8"   ,					newline="\n" ) as f:
            SCREAMING_SNAKE_CASE_      =     f.readlines()
      # Find the start prompt.
      SCREAMING_SNAKE_CASE_      =     0
      while not lines[start_index].startswith(__UpperCamelCase ):
            start_index += 1
      start_index += 1

      SCREAMING_SNAKE_CASE_      =     start_index
      while not lines[end_index].startswith(__UpperCamelCase ):
            end_index += 1
      end_index -= 1

      while len(lines[start_index] ) <= 1:
            start_index += 1
      while len(lines[end_index] ) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index] ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
A   :     List[str]       =				direct_transformers_import(TRANSFORMERS_PATH)

A   :     List[Any]       =				{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
A   :     Any       =				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase   ,					set() )
      SCREAMING_SNAKE_CASE_      =     {
          code: name
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if (code in model_maping_names or code in special_model_types)
      }
      return ", ".join([F'''[{name}](../model_doc/{code})''' for code, name in model_names.items()] ) + "\n"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     _find_text_in_file(
          filename=os.path.join(__UpperCamelCase   ,					__UpperCamelCase )   ,					start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"   ,					end_prompt="<!--End of the generated tip-->"   ,					)

      SCREAMING_SNAKE_CASE_      =     get_model_list_for_task(__UpperCamelCase )

      if current_list != new_list:
            if overwrite:
                  with open(os.path.join(__UpperCamelCase   ,					__UpperCamelCase )   ,					"w"   ,					encoding="utf-8"   ,					newline="\n" ) as f:
                        f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
            else:
                  raise ValueError(
                      F'''The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'''
                      " to fix this." )


if __name__ == "__main__":
      A   :     Tuple       =				argparse.ArgumentParser()
      parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
      A   :     Dict       =				parser.parse_args()

      for task_guide in TASK_GUIDE_TO_MODELS.keys():
            check_model_list_for_task(task_guide, args.fix_and_overwrite)


