




"""simple docstring"""





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


A				=							logging.get_logger(__name__)

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



class   a__					(						__magic_name__ ):
			lowercase_		=						"mra"


			def __init__(   self   :     Any   ,	UpperCamelCase_   :     List[Any]=50265   ,	UpperCamelCase_   :     List[Any]=768   ,	UpperCamelCase_   :     Dict=12   ,	UpperCamelCase_   :     int=12   ,	UpperCamelCase_   :     str=3072   ,	UpperCamelCase_   :     Optional[int]="gelu"   ,	UpperCamelCase_   :     Union[str, Any]=0.1   ,	UpperCamelCase_   :     List[Any]=0.1   ,	UpperCamelCase_   :     Dict=512   ,	UpperCamelCase_   :     Optional[int]=1   ,	UpperCamelCase_   :     Optional[Any]=0.02   ,	UpperCamelCase_   :     List[str]=1e-5   ,	UpperCamelCase_   :     str="absolute"   ,	UpperCamelCase_   :     Tuple=4   ,	UpperCamelCase_   :     Any="full"   ,	UpperCamelCase_   :     List[Any]=0   ,	UpperCamelCase_   :     List[Any]=0   ,	UpperCamelCase_   :     Tuple=1   ,	UpperCamelCase_   :     Dict=0   ,	UpperCamelCase_   :     int=2   ,	**UpperCamelCase_   :     Optional[Any]   ,	):


						"""simple docstring"""




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

						__UpperCAmelCase						:			str												=       vocab_size
						__UpperCAmelCase						:			int												=       max_position_embeddings
						__UpperCAmelCase						:			Dict												=       hidden_size
						__UpperCAmelCase						:			List[str]												=       num_hidden_layers
						__UpperCAmelCase						:			Any												=       num_attention_heads
						__UpperCAmelCase						:			int												=       intermediate_size
						__UpperCAmelCase						:			Tuple												=       hidden_act
						__UpperCAmelCase						:			Optional[Any]												=       hidden_dropout_prob
						__UpperCAmelCase						:			int												=       attention_probs_dropout_prob
						__UpperCAmelCase						:			Union[str, Any]												=       initializer_range
						__UpperCAmelCase						:			List[Any]												=       type_vocab_size
						__UpperCAmelCase						:			Dict												=       layer_norm_eps
						__UpperCAmelCase						:			Optional[Any]												=       position_embedding_type
						__UpperCAmelCase						:			Any												=       block_per_row
						__UpperCAmelCase						:			Optional[int]												=       approx_mode
						__UpperCAmelCase						:			int												=       initial_prior_first_n_blocks
						__UpperCAmelCase						:			Tuple												=       initial_prior_diagonal_n_blocks







"""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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]		,    SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		Optional[int]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  Any:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE__							)
     # set absolute/relative position embeddings parameter
     _UpperCAmelCase     :      Optional[Any]														=       reset_position_index_per_cell

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

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

          _UpperCAmelCase     :      Optional[Any]														=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
     elif task == "TABFACT":
          _UpperCAmelCase     :      int														=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE__							)
     elif task == "MLM":
          _UpperCAmelCase     :      List[str]														=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
     elif task == "INTERMEDIATE_PRETRAINING":
          _UpperCAmelCase     :      Optional[Any]														=       TapasModel(config=SCREAMING_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(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

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

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'							)
     _UpperCAmelCase     :      Optional[int]														=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"		,    model_max_length=512							)
     tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__							)

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


if __name__ == "__main__":
 _lowerCAmelCase							:   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."
 )
 _lowerCAmelCase							:   Tuple				=				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,
 )
