





'''simple docstring'''




import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					Optional[int]					,				__snake_case    :					Optional[Any]     )	->	Dict:
     # Initialise PyTorch model
     __A		:					Tuple						=			LxmertConfig.from_json_file(__snake_case     )
     print(f'Building PyTorch model from configuration: {config}'     )
     __A		:					List[str]						=			LxmertForPreTraining(__snake_case     )

     # Load weights from tf checkpoint
     load_tf_weights_in_lxmert(__snake_case					,				__snake_case					,				__snake_case     )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'     )
     torch.save(model.state_dict()					,				__snake_case     )


if __name__ == "__main__":
  lowercase__       : str         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  lowercase__       : Dict         =       parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''




from ...configuration_utils import PretrainedConfig


lowercase__       : Any         =       {
    '''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       (a__				):
     lowerCAmelCase						     =			'''tapas'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=1024		,      _UpperCAmelCase=[3, 256, 256, 2, 256, 256, 10]		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase=10.0		,      _UpperCAmelCase=0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=1.0		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase="ratio"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=False		,      _UpperCAmelCase=True		,      _UpperCAmelCase=False		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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

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

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

          # Aggregation hyperparameters
          __A		:					Optional[Any]						=			aggregation_labels
          __A		:					List[str]						=			no_aggregation_label_index

          if isinstance(self.aggregation_labels		,      _UpperCAmelCase):
               __A		:					Optional[Any]						=			{int(_UpperCAmelCase): v for k, v in aggregation_labels.items()}