
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''







					if not isinstance(__UpperCamelCase ,			__UpperCamelCase		):
										raise TypeError('''only integers accepted as input'''		)
					else:
										lowercase							=						str(abs(__UpperCamelCase		)		)
										lowercase							=						[list(__UpperCamelCase		) for char in range(len(__UpperCamelCase		)		)]
										for index in range(len(__UpperCamelCase		)		):
															num_transpositions[index].pop(__UpperCamelCase		)
										return max(
										    int(''''''.join(list(__UpperCamelCase		)		)		) for transposition in num_transpositions		)


if __name__ == "__main__":
							__import__("doctest").testmod()












"""simple docstring"""


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

from accelerate.utils.dataclasses import DistributedType


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     Optional[int],       _lowerCamelCase :     Optional[int]=2,       _lowerCamelCase :     Optional[int]=3,       _lowerCamelCase :     int=64,       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     __A									= np.random.default_rng(_lowerCamelCase  )
     __A									= length
     __A									= rng.normal(size=(length,)  ).astype(np.floataa  )
     __A									= 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,       _lowerCamelCase :     Optional[int]  ):

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





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     Optional[Any],       _lowerCamelCase :     Tuple=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     Optional[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     Optional[Any]=None  ):

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





class 					snake_case		(							torch.nn.Module						):


   '''simple docstring'''

   def __init__(	self :     str,       _lowerCamelCase :     Optional[Any]=0,       _lowerCamelCase :     Any=0,       _lowerCamelCase :     List[Any]=False  ):

     '''simple docstring'''
     super().__init__()
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= torch.nn.Parameter(torch.tensor(_lowerCamelCase  ).float()  )
     __A									= True






   def 				_SCREAMING_SNAKE_CASE				(	self :     Optional[Any],       _lowerCamelCase :     List[str]=None  ):

     '''simple docstring'''
     if self.first_batch:
       print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
       __A									= False
     return x * self.a + self.b





def 					lowerCAmelCase				(    __UpperCamelCase		,       __UpperCamelCase = 1_6 ):




  """simple docstring"""







  from datasets import load_dataset
  from transformers import AutoTokenizer

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

  __A									= {v: i for i, v in enumerate(__UpperCamelCase )}

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

  def collate_fn(__UpperCamelCase ):
    # 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_2_8		,       return_tensors='''pt''' )
    return tokenizer.pad(__UpperCamelCase		,       padding='''longest'''		,       return_tensors='''pt''' )

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

  return train_dataloader, eval_dataloader




