






"""simple docstring"""







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

from accelerate.utils.dataclasses import DistributedType





class 	UpperCamelCase_			:



  def __init__(   self			,		snake_case__=2			,		snake_case__=3			,		snake_case__=64			,		snake_case__=None					)      ->      Dict:



         """simple docstring"""





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



  def __len__(   self					)      ->      Any:



         """simple docstring"""





         return self.length



  def __getitem__(   self			,		snake_case__					)      ->      List[str]:



         """simple docstring"""





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





class 	UpperCamelCase_			(							torch.nn.Module	):



  def __init__(   self			,		snake_case__=0			,		snake_case__=0			,		snake_case__=False					)      ->      List[Any]:



         """simple docstring"""





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



  def 		UpperCamelCase_					(   self			,		snake_case__=None					)      ->      Optional[Any]:



         """simple docstring"""





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





class 	UpperCamelCase_			(							torch.nn.Module	):



  def __init__(   self			,		snake_case__=0			,		snake_case__=0			,		snake_case__=False					)      ->      Any:



         """simple docstring"""





         super().__init__()
         UpperCAmelCase						     =      torch.nn.Parameter(torch.tensor(lowerCamelCase_					).float()					)
         UpperCAmelCase						     =      torch.nn.Parameter(torch.tensor(lowerCamelCase_					).float()					)
         UpperCAmelCase						     =      True



  def 		UpperCamelCase_					(   self			,		snake_case__=None					)      ->      Any:



         """simple docstring"""





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






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase = 16		):







       '''simple docstring'''
       from datasets import load_dataset
       from transformers import AutoTokenizer

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

       UpperCAmelCase						     =      {v: i for i, v in enumerate(lowerCAmelCase		)}

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

       def collate_fn(lowerCAmelCase		):
              # 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(lowerCAmelCase						,   padding="""max_length"""						,   max_length=128						,   return_tensors="""pt"""		)
              return tokenizer.pad(lowerCAmelCase						,   padding="""longest"""						,   return_tensors="""pt"""		)

       # Instantiate dataloaders.
       UpperCAmelCase						     =      DataLoader(tokenized_datasets["""train"""]						,   shuffle=lowerCAmelCase						,   collate_fn=lowerCAmelCase						,   batch_size=2		)
       UpperCAmelCase						     =      DataLoader(tokenized_datasets["""validation"""]						,   shuffle=lowerCAmelCase						,   collate_fn=lowerCAmelCase						,   batch_size=1		)

       return train_dataloader, eval_dataloader




'''simple docstring'''






import re


def    _snake_case  (	A			)		->       bool:
							lowerCAmelCase__			  =      re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$'''			)
							if match := re.search(A					,       A			):
														return match.string == phone
							return False


if __name__ == "__main__":
		print(indian_phone_validator('''+918827897895'''))