

'''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   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = TapasConfig.from_json_file(a__		)
     # set absolute/relative position embeddings parameter
     __SCREAMING_SNAKE_CASE      = reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          __SCREAMING_SNAKE_CASE      = TapasForQuestionAnswering(config=a__		)
     elif task == "WTQ":
          # run_task_main.py hparams
          __SCREAMING_SNAKE_CASE      = 4
          __SCREAMING_SNAKE_CASE      = True
          # hparam_utils.py hparams
          __SCREAMING_SNAKE_CASE      = 0.664_694
          __SCREAMING_SNAKE_CASE      = 0.207_951
          __SCREAMING_SNAKE_CASE      = 0.121_194
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = False
          __SCREAMING_SNAKE_CASE      = 0.0_352_513

          __SCREAMING_SNAKE_CASE      = TapasForQuestionAnswering(config=a__		)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          __SCREAMING_SNAKE_CASE      = 4
          __SCREAMING_SNAKE_CASE      = False
          # hparam_utils.py hparams
          __SCREAMING_SNAKE_CASE      = 36.4_519
          __SCREAMING_SNAKE_CASE      = 0.903_421
          __SCREAMING_SNAKE_CASE      = 222.088
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = True
          __SCREAMING_SNAKE_CASE      = 0.763_141

          __SCREAMING_SNAKE_CASE      = TapasForQuestionAnswering(config=a__		)
     elif task == "TABFACT":
          __SCREAMING_SNAKE_CASE      = TapasForSequenceClassification(config=a__		)
     elif task == "MLM":
          __SCREAMING_SNAKE_CASE      = TapasForMaskedLM(config=a__		)
     elif task == "INTERMEDIATE_PRETRAINING":
          __SCREAMING_SNAKE_CASE      = TapasModel(config=a__		)
     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(a__ ,       a__ ,       a__		)

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

     # Save tokenizer files
     print(F'Save tokenizer files to {pytorch_dump_path}'		)
     __SCREAMING_SNAKE_CASE      = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt""" ,       model_max_length=5_12		)
     tokenizer.save_pretrained(a__		)

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


if __name__ == "__main__":
  UpperCAmelCase					:			Optional[int]       =	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.'
  )
  UpperCAmelCase					:			Dict       =	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,
  )









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Union[str, Any]       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "mra"
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Dict=50_265   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=768   ,				__SCREAMING_SNAKE_CASE   :			int=12   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=12   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=3_072   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Any=0.02   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1E-5   ,				__SCREAMING_SNAKE_CASE   :			str="absolute"   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=4   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="full"   ,				__SCREAMING_SNAKE_CASE   :			str=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0   ,				__SCREAMING_SNAKE_CASE   :			Dict=2   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					Tuple:


      """simple docstring"""




      super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE   ,				bos_token_id=__SCREAMING_SNAKE_CASE   ,				eos_token_id=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = layer_norm_eps
      __SCREAMING_SNAKE_CASE      = position_embedding_type
      __SCREAMING_SNAKE_CASE      = block_per_row
      __SCREAMING_SNAKE_CASE      = approx_mode
      __SCREAMING_SNAKE_CASE      = initial_prior_first_n_blocks
      __SCREAMING_SNAKE_CASE      = initial_prior_diagonal_n_blocks






