

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

from accelerate.utils.dataclasses import DistributedType





class 				__lowerCAmelCase  :




  def __init__(       self							,     lowerCAmelCase=2							,     lowerCAmelCase=3							,     lowerCAmelCase=64							,     lowerCAmelCase=None      )						->				Optional[Any]:
        '''simple docstring'''






        _lowercase			 =np.random.default_rng(_SCREAMING_SNAKE_CASE      )
        _lowercase			 =length
        _lowercase			 =rng.normal(size=(length,)      ).astype(np.floataa      )
        _lowercase			 =a * self.x + b + rng.normal(scale=0.1							,     size=(length,)      ).astype(np.floataa      )




  def __len__(       self      )						->				Optional[int]:
        '''simple docstring'''






        return self.length




  def __getitem__(       self							,     lowerCAmelCase      )						->				Optional[Any]:
        '''simple docstring'''






        return {"x": self.x[i], "y": self.y[i]}





class 				__lowerCAmelCase  (      torch.nn.Module							):




  def __init__(       self							,     lowerCAmelCase=0							,     lowerCAmelCase=0							,     lowerCAmelCase=False      )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase			 =torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
        _lowercase			 =torch.nn.Parameter(torch.tensor([2, 3]      ).float()      )
        _lowercase			 =True




  def 						A__  (       self							,     lowerCAmelCase=None      )						->				Tuple:
        '''simple docstring'''






        if self.first_batch:
              print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
              _lowercase			 =False
        return x * self.a[0] + self.b[0]





class 				__lowerCAmelCase  (      torch.nn.Module							):




  def __init__(       self							,     lowerCAmelCase=0							,     lowerCAmelCase=0							,     lowerCAmelCase=False      )						->				Any:
        '''simple docstring'''






        super().__init__()
        _lowercase			 =torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE      ).float()      )
        _lowercase			 =torch.nn.Parameter(torch.tensor(_SCREAMING_SNAKE_CASE      ).float()      )
        _lowercase			 =True




  def 						A__  (       self							,     lowerCAmelCase=None      )						->				Tuple:
        '''simple docstring'''






        if self.first_batch:
              print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''      )
              _lowercase			 =False
        return x * self.a + self.b
def   a   (    A__			:     Dict   ,     A__			:     int = 16  )					->   Union[str, Any]:





      """simple docstring"""



      from datasets import load_dataset
      from transformers import AutoTokenizer

      _lowercase			 =AutoTokenizer.from_pretrained('bert-base-cased'  )
      _lowercase			 ={'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      _lowercase			 =load_dataset('csv'   ,     data_files=_A  )
      _lowercase			 =datasets['train'].unique('label'  )

      _lowercase			 ={v: i for i, v in enumerate(_A  )}

      def tokenize_function(A__			:     int  ):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase			 =tokenizer(
                examples['sentence1']   ,     examples['sentence2']   ,     truncation=_A   ,     max_length=_A   ,     padding='max_length'  )
            if "label" in examples:
                  _lowercase			 =[label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      _lowercase			 =datasets.map(
          _A   ,     batched=_A   ,     remove_columns=['sentence1', 'sentence2', 'label']   ,     )

      def collate_fn(A__			:     List[str]  ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(_A   ,     padding='max_length'   ,     max_length=128   ,     return_tensors='pt'  )
            return tokenizer.pad(_A   ,     padding='longest'   ,     return_tensors='pt'  )

      # Instantiate dataloaders.
      _lowercase			 =DataLoader(tokenized_datasets['train']   ,     shuffle=_A   ,     collate_fn=_A   ,     batch_size=2  )
      _lowercase			 =DataLoader(tokenized_datasets['validation']   ,     shuffle=_A   ,     collate_fn=_A   ,     batch_size=1  )

      return train_dataloader, eval_dataloader





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__			:     Dict   ,     A__			:     Optional[Any]   ,     A__			:     Optional[Any]   ,     A__			:     Optional[int]   ,     A__			:     Union[str, Any]  )					->   Optional[int]:





      """simple docstring"""



      _lowercase			 =TapasConfig.from_json_file(A__  )
      # set absolute/relative position embeddings parameter
      _lowercase			 =reset_position_index_per_cell

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

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

            _lowercase			 =TapasForQuestionAnswering(config=A__  )
      elif task == "TABFACT":
            _lowercase			 =TapasForSequenceClassification(config=A__  )
      elif task == "MLM":
            _lowercase			 =TapasForMaskedLM(config=A__  )
      elif task == "INTERMEDIATE_PRETRAINING":
            _lowercase			 =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}'''  )
      _lowercase			 =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__":
  lowercase_             =     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.'
  )
  lowercase_             =     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,
  )


