

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

from accelerate.utils.dataclasses import DistributedType




class      UpperCamelCase :

    def __init__(			self :      Dict							,    snake_case__ :      Tuple=2							,    snake_case__ :      int=3							,    snake_case__ :      Any=6_4							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






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

    def __len__(			self :      str    ):

     """simple docstring"""






     return self.length




    def __getitem__(			self :      Dict							,    snake_case__ :      int    ):

     """simple docstring"""






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




class      UpperCamelCase (     torch.nn.Module   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      List[Any]=0							,    snake_case__ :      Any=0							,    snake_case__ :      Union[str, Any]=False    ):

     """simple docstring"""






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




    def UpperCamelCase			(			self :      List[Any]							,    snake_case__ :      Tuple=None    ):

     """simple docstring"""






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




class      UpperCamelCase (     torch.nn.Module   ):

    def __init__(			self :      Union[str, Any]							,    snake_case__ :      Tuple=0							,    snake_case__ :      int=0							,    snake_case__ :      Union[str, Any]=False    ):

     """simple docstring"""






     super().__init__()
     SCREAMING_SNAKE_CASE								=			torch.nn.Parameter(torch.tensor(snake_case__    ).float()    )
     SCREAMING_SNAKE_CASE								=			torch.nn.Parameter(torch.tensor(snake_case__    ).float()    )
     SCREAMING_SNAKE_CASE								=			True




    def UpperCamelCase			(			self :      Tuple							,    snake_case__ :      Any=None    ):

     """simple docstring"""






     if self.first_batch:
      print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
      SCREAMING_SNAKE_CASE								=			False
     return x * self.a + self.b


def        __lowerCAmelCase    (       _UpperCamelCase		:  List[str]		,							_UpperCamelCase		:  int = 16	)		->							Any:
 '''simple docstring'''


 from datasets import load_dataset
 from transformers import AutoTokenizer

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

 SCREAMING_SNAKE_CASE								=			{v: i for i, v in enumerate(_UpperCamelCase	)}

 def tokenize_function(_UpperCamelCase		:  List[Any]	):
  # max_length=None => use the model max length (it's actually the default)
  SCREAMING_SNAKE_CASE								=			tokenizer(
      examples['sentence1']		,							examples['sentence2']		,							truncation=_UpperCamelCase		,							max_length=_UpperCamelCase		,							padding='max_length'	)
  if "label" in examples:
   SCREAMING_SNAKE_CASE								=			[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
 SCREAMING_SNAKE_CASE								=			datasets.map(
     _UpperCamelCase		,							batched=_UpperCamelCase		,							remove_columns=['sentence1', 'sentence2', 'label']		,							)

 def collate_fn(_UpperCamelCase		:  Optional[Any]	):
  # 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(_UpperCamelCase		,							padding='max_length'		,							max_length=1_28		,							return_tensors='pt'	)
  return tokenizer.pad(_UpperCamelCase		,							padding='longest'		,							return_tensors='pt'	)

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

 return train_dataloader, eval_dataloader









def        __lowerCAmelCase    (       _UpperCamelCase		:  str		,							_UpperCamelCase		:  int	)		->							list[str]:
 '''simple docstring'''


 return [sentence[i : i + ngram_size] for i in range(len(_UpperCamelCase	) - ngram_size + 1	)]


if __name__ == "__main__":
      from doctest import testmod

      testmod()

