





'''simple docstring'''




import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					Optional[int]					,				__snake_case    :					Any					,				__snake_case    :					int					,				__snake_case    :					List[Any]     )	->	str:
     # Initialise PyTorch model.
     # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
     # TapasConfig to False.

     # initialize configuration from json file
     __A		:					List[Any]						=			TapasConfig.from_json_file(__snake_case     )
     # set absolute/relative position embeddings parameter
     __A		:					List[str]						=			reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          __A		:					int						=			TapasForQuestionAnswering(config=__snake_case     )
     elif task == "WTQ":
          # run_task_main.py hparams
          __A		:					Optional[Any]						=			4
          __A		:					Tuple						=			True
          # hparam_utils.py hparams
          __A		:					Optional[Any]						=			0.664_694
          __A		:					Any						=			0.207_951
          __A		:					int						=			0.121_194
          __A		:					Dict						=			True
          __A		:					Optional[Any]						=			True
          __A		:					Union[str, Any]						=			False
          __A		:					str						=			0.0_352_513

          __A		:					Union[str, Any]						=			TapasForQuestionAnswering(config=__snake_case     )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          __A		:					Optional[Any]						=			4
          __A		:					List[str]						=			False
          # hparam_utils.py hparams
          __A		:					Union[str, Any]						=			36.4_519
          __A		:					Optional[int]						=			0.903_421
          __A		:					Optional[Any]						=			222.088
          __A		:					Union[str, Any]						=			True
          __A		:					List[Any]						=			True
          __A		:					str						=			True
          __A		:					List[Any]						=			0.763_141

          __A		:					Any						=			TapasForQuestionAnswering(config=__snake_case     )
     elif task == "TABFACT":
          __A		:					int						=			TapasForSequenceClassification(config=__snake_case     )
     elif task == "MLM":
          __A		:					int						=			TapasForMaskedLM(config=__snake_case     )
     elif task == "INTERMEDIATE_PRETRAINING":
          __A		:					Tuple						=			TapasModel(config=__snake_case     )
     else:
          raise ValueError(f'Task {task} not supported.'     )

     print(f'Building PyTorch model from configuration: {config}'     )
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__snake_case					,				__snake_case					,				__snake_case     )

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'     )
     model.save_pretrained(__snake_case     )

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'     )
     __A		:					str						=			TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'					,				model_max_length=5_12     )
     tokenizer.save_pretrained(__snake_case     )

     print('Used relative position embeddings:'					,				model.config.reset_position_index_per_cell     )


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
  )
  parser.add_argument(
      '''--reset_position_index_per_cell''',
      default=False,
      action='''store_true''',
      help='''Whether to use relative position embeddings or not. Defaults to True.''',
  )
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--tapas_config_file''',
      default=None,
      type=str,
      required=True,
      help=(
          '''The config json file corresponding to the pre-trained TAPAS model. \n'''
          '''This specifies the model architecture.'''
      ),
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Union[str, Any]         =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.task,
      args.reset_position_index_per_cell,
      args.tf_checkpoint_path,
      args.tapas_config_file,
      args.pytorch_dump_path,
  )







def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for data in source_data:
								for i, el in enumerate(lowerCamelCase_							):
												if len(lowerCamelCase_							) < i + 1:
																data_lists.append([]							)
												data_lists[i].append(float(lowerCamelCase_							)							)
				return data_lists






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     []
				for dlist, weight in zip(lowerCamelCase_						,  lowerCamelCase_							):
								lowercase__  					=     min(lowerCamelCase_							)
								lowercase__  					=     max(lowerCamelCase_							)

								lowercase__  					=     []
								# for weight 0 score is 1 - actual score
								if weight == 0:
												for item in dlist:
																try:
																				score.append(1 - ((item - mind) / (maxd - mind))							)
																except ZeroDivisionError:
																				score.append(1							)

								elif weight == 1:
												for item in dlist:
																try:
																				score.append((item - mind) / (maxd - mind)							)
																except ZeroDivisionError:
																				score.append(0							)

        # weight not 0 or 1
								else:
												lowercase__  					=     F"""Invalid weight of {weight:f} provided"""
												raise ValueError(lowerCamelCase_							)

								score_lists.append(lowerCamelCase_							)

				return score_lists






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     [0 for i in range(len(score_lists[0]							)							)]

				for slist in score_lists:
								for j, ele in enumerate(lowerCamelCase_							):
												lowercase__  					=     final_scores[j] + ele

				return final_scores






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     get_data(lowerCamelCase_							)
				lowercase__  					=     calculate_each_score(lowerCamelCase_						,  lowerCamelCase_							)
				lowercase__  					=     generate_final_scores(lowerCamelCase_							)

				# append scores to source data
				for i, ele in enumerate(lowerCamelCase_							):
								source_data[i].append(lowerCamelCase_							)

				return source_data




