


"""simple docstring"""







from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCAmelCase__				    =       logging.get_logger(__name__)  # pylint: disable=invalid-name
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  if not path:
    return "pipe"

  for ext in PipelineDataFormat.SUPPORTED_FORMATS:
    if path.endswith(SCREAMING_SNAKE_CASE    ):
      return ext

  raise Exception(
      f"""Unable to determine file format from file extension {path}. """
      f"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							pipeline(
      task=args.task    ,				model=args.model if args.model else None    ,				config=args.config    ,				tokenizer=args.tokenizer    ,				device=args.device    ,				)
  lowerCAmelCase					:					List[str]								=							try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
  lowerCAmelCase					:					Union[str, Any]								=							PipelineDataFormat.from_str(
      format=SCREAMING_SNAKE_CASE    ,				output_path=args.output    ,				input_path=args.input    ,				column=args.column if args.column else nlp.default_input_names    ,				overwrite=args.overwrite    ,				)
  return RunCommand(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )





class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							nlp
         lowerCAmelCase					:					List[Any]								=							reader

       @staticmethod
       def 	lowercase__							(						snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					str								=							parser.add_parser("run"		,					help="Run a pipeline through the CLI"						)
         run_parser.add_argument("--task"		,					choices=get_supported_tasks()		,					help="Task to run"						)
         run_parser.add_argument("--input"		,					type=snake_case__		,					help="Path to the file to use for inference"						)
         run_parser.add_argument("--output"		,					type=snake_case__		,					help="Path to the file that will be used post to write results."						)
         run_parser.add_argument("--model"		,					type=snake_case__		,					help="Name or path to the model to instantiate."						)
         run_parser.add_argument("--config"		,					type=snake_case__		,					help="Name or path to the model's config to instantiate."						)
         run_parser.add_argument(
             "--tokenizer"		,					type=snake_case__		,					help="Name of the tokenizer to use. (default: same as the model name)"						)
         run_parser.add_argument(
             "--column"		,					type=snake_case__		,					help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"		,					)
         run_parser.add_argument(
             "--format"		,					type=snake_case__		,					default="infer"		,					choices=PipelineDataFormat.SUPPORTED_FORMATS		,					help="Input format to read from"		,					)
         run_parser.add_argument(
             "--device"		,					type=snake_case__		,					default=-1		,					help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"		,					)
         run_parser.add_argument("--overwrite"		,					action="store_true"		,					help="Allow overwriting the output file."						)
         run_parser.set_defaults(func=snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					str								=							self._nlp, []

         for entry in self._reader:
           lowerCAmelCase					:					Any								=							nlp(**snake_case__						) if self._reader.is_multi_columns else nlp(snake_case__						)
           if isinstance(snake_case__		,					snake_case__						):
             outputs.append(snake_case__						)
           else:
             outputs += output

        # Saving data
         if self._nlp.binary_output:
           lowerCAmelCase					:					Any								=							self._reader.save_binary(snake_case__						)
           logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}"""						)
         else:
           self._reader.save(snake_case__						)









"""simple docstring"""







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 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       a  :    Any       ="tapas"

       def __init__(						self		,					snake_case__=30_522		,					snake_case__=768		,					snake_case__=12		,					snake_case__=12		,					snake_case__=3_072		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=1_024		,					snake_case__=[3, 256, 256, 2, 256, 256, 10]		,					snake_case__=0.02		,					snake_case__=1e-12		,					snake_case__=0		,					snake_case__=10.0		,					snake_case__=0		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=None		,					snake_case__=1.0		,					snake_case__=1.0		,					snake_case__=False		,					snake_case__=False		,					snake_case__="ratio"		,					snake_case__=None		,					snake_case__=None		,					snake_case__=64		,					snake_case__=32		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=False		,					snake_case__=True		,					snake_case__=False		,					snake_case__=None		,					snake_case__=None		,					**snake_case__		,					):






         """simple docstring"""





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

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

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

         # Aggregation hyperparameters
         lowerCAmelCase					:					List[str]								=							aggregation_labels
         lowerCAmelCase					:					List[str]								=							no_aggregation_label_index

         if isinstance(self.aggregation_labels		,					snake_case__						):
           lowerCAmelCase					:					Union[str, Any]								=							{int(snake_case__						): v for k, v in aggregation_labels.items()}





