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_			(					__A,  __A,  __A,  __A,  __A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  TapasConfig.from_json_file(__A							)
      # set absolute/relative position embeddings parameter
      UpperCAmelCase__								=  reset_position_index_per_cell

      # set remaining parameters of TapasConfig as well as the model based on the task
      if task == "SQA":
            UpperCAmelCase__								=  TapasForQuestionAnswering(config=__A							)
      elif task == "WTQ":
            # run_task_main.py hparams
            UpperCAmelCase__								=  4
            UpperCAmelCase__								=  True
            # hparam_utils.py hparams
            UpperCAmelCase__								=  0.664694
            UpperCAmelCase__								=  0.207951
            UpperCAmelCase__								=  0.121194
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  False
            UpperCAmelCase__								=  0.0352513

            UpperCAmelCase__								=  TapasForQuestionAnswering(config=__A							)
      elif task == "WIKISQL_SUPERVISED":
            # run_task_main.py hparams
            UpperCAmelCase__								=  4
            UpperCAmelCase__								=  False
            # hparam_utils.py hparams
            UpperCAmelCase__								=  36.4519
            UpperCAmelCase__								=  0.903421
            UpperCAmelCase__								=  222.088
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  True
            UpperCAmelCase__								=  0.763141

            UpperCAmelCase__								=  TapasForQuestionAnswering(config=__A							)
      elif task == "TABFACT":
            UpperCAmelCase__								=  TapasForSequenceClassification(config=__A							)
      elif task == "MLM":
            UpperCAmelCase__								=  TapasForMaskedLM(config=__A							)
      elif task == "INTERMEDIATE_PRETRAINING":
            UpperCAmelCase__								=  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}"""							)
      UpperCAmelCase__								=  TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt",  model_max_length=512							)
      tokenizer.save_pretrained(__A							)

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


if __name__ == "__main__":
      UpperCamelCase__    							= 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__    							= 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,
      )




from typing import Callable, Optional, Union

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


UpperCamelCase__    							= logging.get_logger(__name__)

UpperCamelCase__    							= {
    'microsoft/xprophetnet-large-wiki100-cased': (
        'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json'
    ),
}





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			Optional[int]              =      'xlm-prophetnet'
   __UpperCAmelCase			:			int              =      ['past_key_values']
   __UpperCAmelCase			:			Optional[int]              =      {
       'num_attention_heads': 'num_encoder_attention_heads',
   }


   def __init__(self					:						str	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[Union[str, Callable]] = "gelu"	,  __UpperCAmelCase					:						Optional[int] = 3_0_5_2_2	,  __UpperCAmelCase					:						Optional[int] = 1_0_2_4	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[int] = 4_0_9_6	,  __UpperCAmelCase					:						Optional[int] = 1_2	,  __UpperCAmelCase					:						Optional[int] = 1_6	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[float] = 0.1	,  __UpperCAmelCase					:						Optional[int] = 5_1_2	,  __UpperCAmelCase					:						Optional[float] = 0.02	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 2	,  __UpperCAmelCase					:						Optional[int] = 3_2	,  __UpperCAmelCase					:						Optional[int] = 1_2_8	,  __UpperCAmelCase					:						Optional[bool] = False	,  __UpperCAmelCase					:						Optional[float] = 0.0	,  __UpperCAmelCase					:						Optional[bool] = True	,  __UpperCAmelCase					:						Optional[int] = 0	,  __UpperCAmelCase					:						Optional[int] = 1	,  __UpperCAmelCase					:						Optional[int] = 2	,  **__UpperCAmelCase					:						Optional[Any]	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  encoder_ffn_dim
         UpperCAmelCase__								=  num_encoder_layers
         UpperCAmelCase__								=  num_encoder_attention_heads
         UpperCAmelCase__								=  decoder_ffn_dim
         UpperCAmelCase__								=  num_decoder_layers
         UpperCAmelCase__								=  num_decoder_attention_heads
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  init_std  # Normal(0, this parameter)
         UpperCAmelCase__								=  activation_function

         # parameters for xlmprophetnet
         UpperCAmelCase__								=  ngram
         UpperCAmelCase__								=  num_buckets
         UpperCAmelCase__								=  relative_max_distance
         UpperCAmelCase__								=  disable_ngram_loss
         UpperCAmelCase__								=  eps

         # 3 Types of Dropout
         UpperCAmelCase__								=  attention_dropout
         UpperCAmelCase__								=  activation_dropout
         UpperCAmelCase__								=  dropout

         UpperCAmelCase__								=  use_cache

         super().__init__(
             pad_token_id=__UpperCAmelCase	,  bos_token_id=__UpperCAmelCase	,  eos_token_id=__UpperCAmelCase	,  is_encoder_decoder=__UpperCAmelCase	,  add_cross_attention=__UpperCAmelCase	,  decoder_start_token_id=__UpperCAmelCase	,  **__UpperCAmelCase	,  )


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         return self.num_encoder_layers + self.num_decoder_layers





   @num_hidden_layers.setter
   def       lowercase_				(self					:						Any	,  __UpperCAmelCase					:						List[Any]		)						->    Union[str, Any]:
         """simple docstring"""



         raise NotImplementedError(
             "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
             " `num_decoder_layers`."		)



