





import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   params

       UpperCamelCase__							:       Optional[Any]      =   np.array(lowercase_							)
       UpperCamelCase__							:       Any      =   np.array([len(lowercase_							) for t in data]							)

       self.check()
       self.remove_long_sequences()
       self.remove_empty_sequences()
       self.remove_unknown_sequences()
       self.check()
       self.print_statistics()




   def __getitem__(					self,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       return (self.token_ids[index], self.lengths[index])




   def __len__(					self							)				->   str:






       """simple docstring"""
       return len(self.lengths							)




   def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






       """simple docstring"""
       assert len(self.token_ids							) == len(self.lengths							)
       assert all(self.lengths[i] == len(self.token_ids[i]							) for i in range(len(self.lengths							)							)							)




   def    UpperCamelCase__						(					self							)				->   List[str]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   self.params.max_model_input_size
       UpperCamelCase__							:       int      =   self.lengths > max_len
       logger.info(f"Splitting {sum(lowercase_							)} too long sequences."							)

       def divide_chunks(__magic_name__,	__magic_name__							):
           return [l[i : i + n] for i in range(0,	len(lowercase_							),	lowercase_							)]

       UpperCamelCase__							:       List[str]      =   []
       UpperCamelCase__							:       List[str]      =   []
       if self.params.mlm:
           UpperCamelCase__     ,UpperCamelCase__							:       str      =   self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
       else:
           UpperCamelCase__     ,UpperCamelCase__							:       Any      =   self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

       for seq_, len_ in zip(self.token_ids,	self.lengths							):
           assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
           if len_ <= max_len:
               new_tok_ids.append(seq_							)
               new_lengths.append(len_							)
           else:
               UpperCamelCase__							:       Optional[int]      =   []
               for sub_s in divide_chunks(seq_,	max_len - 2							):
                   if sub_s[0] != cls_id:
                       UpperCamelCase__							:       str      =   np.insert(lowercase_,	0,	lowercase_							)
                   if sub_s[-1] != sep_id:
                       UpperCamelCase__							:       List[str]      =   np.insert(lowercase_,	len(lowercase_							),	lowercase_							)
                   assert len(lowercase_							) <= max_len
                   assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
                   sub_seqs.append(lowercase_							)

               new_tok_ids.extend(lowercase_							)
               new_lengths.extend([len(lowercase_							) for l in sub_seqs]							)

       UpperCamelCase__							:       Any      =   np.array(lowercase_							)
       UpperCamelCase__							:       Union[str, Any]      =   np.array(lowercase_							)




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Optional[int]      =   len(self							)
       UpperCamelCase__							:       str      =   self.lengths > 11
       UpperCamelCase__							:       Optional[Any]      =   self.token_ids[indices]
       UpperCamelCase__							:       List[str]      =   self.lengths[indices]
       UpperCamelCase__							:       int      =   len(self							)
       logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""







       if "unk_token" not in self.params.special_tok_ids:
           return
       else:
           UpperCamelCase__							:       Any      =   self.params.special_tok_ids['''unk_token''']
       UpperCamelCase__							:       Optional[int]      =   len(self							)
       UpperCamelCase__							:       Optional[int]      =   np.array([np.count_nonzero(a == unk_token_id							) for a in self.token_ids]							)
       UpperCamelCase__							:       List[str]      =   (unk_occs / self.lengths) < 0.5
       UpperCamelCase__							:       Union[str, Any]      =   self.token_ids[indices]
       UpperCamelCase__							:       str      =   self.lengths[indices]
       UpperCamelCase__							:       Union[str, Any]      =   len(self							)
       logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."							)




   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""







       if not self.params.is_master:
           return
       logger.info(f"{len(self							)} sequences"							)
       # data_len = sum(self.lengths)
       # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
       # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

       # unk_idx = self.params.special_tok_ids['unk_token']
       # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
       # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')




   def    UpperCamelCase__						(					self,	__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   [t[0] for t in batch]
       UpperCamelCase__							:       Tuple      =   [t[1] for t in batch]
       assert len(lowercase_							) == len(lowercase_							)

       # Max for paddings
       UpperCamelCase__							:       Optional[int]      =   max(lowercase_							)

       # Pad token ids
       if self.params.mlm:
           UpperCamelCase__							:       Any      =   self.params.special_tok_ids['''pad_token''']
       else:
           UpperCamelCase__							:       List[Any]      =   self.params.special_tok_ids['''unk_token''']
       UpperCamelCase__							:       Optional[int]      =   [list(t.astype(lowercase_							)							) + [pad_idx] * (max_seq_len_ - len(lowercase_							)) for t in token_ids]
       assert len(tk_							) == len(lowercase_							)
       assert all(len(lowercase_							) == max_seq_len_ for t in tk_							)

       UpperCamelCase__							:       Optional[int]      =   torch.tensor(tk_							)  # (bs, max_seq_len_)
       UpperCamelCase__							:       List[Any]      =   torch.tensor(lowercase_							)  # (bs)
       return tk_t, lg_t








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_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    str   )   ->			Optional[int]:
    # 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
    UpperCamelCase__							:       Any      =   TapasConfig.from_json_file(__UpperCAmelCase   )
    # 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__							:       List[Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WTQ":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   True
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   0.664694
        UpperCamelCase__							:       Union[str, Any]      =   0.207951
        UpperCamelCase__							:       Any      =   0.121194
        UpperCamelCase__							:       int      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       str      =   False
        UpperCamelCase__							:       int      =   0.0352513

        UpperCamelCase__							:       Dict      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        UpperCamelCase__							:       Any      =   4
        UpperCamelCase__							:       List[str]      =   False
        # hparam_utils.py hparams
        UpperCamelCase__							:       int      =   36.4519
        UpperCamelCase__							:       int      =   0.903421
        UpperCamelCase__							:       List[str]      =   222.088
        UpperCamelCase__							:       Dict      =   True
        UpperCamelCase__							:       List[str]      =   True
        UpperCamelCase__							:       Any      =   True
        UpperCamelCase__							:       int      =   0.763141

        UpperCamelCase__							:       Union[str, Any]      =   TapasForQuestionAnswering(config=__UpperCAmelCase   )
    elif task == "TABFACT":
        UpperCamelCase__							:       List[Any]      =   TapasForSequenceClassification(config=__UpperCAmelCase   )
    elif task == "MLM":
        UpperCamelCase__							:       Optional[Any]      =   TapasForMaskedLM(config=__UpperCAmelCase   )
    elif task == "INTERMEDIATE_PRETRAINING":
        UpperCamelCase__							:       str      =   TapasModel(config=__UpperCAmelCase   )
    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(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )

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

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

    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,
  )

