






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      ,    a__=2      ,    a__=3      ,    a__=64      ,    a__=None     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :Optional[Any] 							=    np.random.default_rng(a__     )
            __snake_case      :Dict 							=    length
            __snake_case      :List[str] 							=    rng.normal(size=(length,)     ).astype(np.floataa     )
            __snake_case      :List[Any] 							=    a * self.x + b + rng.normal(scale=0.1      ,    size=(length,)     ).astype(np.floataa     )



       def __len__(				self     )			->   int:






            '''simple docstring'''





            return self.length



       def __getitem__(				self      ,    a__     )			->   str:






            '''simple docstring'''





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




class       snake_case__ (				torch.nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__=0      ,    a__=0      ,    a__=False     )			->   Union[str, Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :Union[str, Any] 							=    torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
            __snake_case      :Any 							=    torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
            __snake_case      :Optional[int] 							=    True



       def        __lowercase    (				self      ,    a__=None     )			->   Tuple:






            '''simple docstring'''





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




class       snake_case__ (				torch.nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__=0      ,    a__=0      ,    a__=False     )			->   Union[str, Any]:






            '''simple docstring'''





            super().__init__()
            __snake_case      :str 							=    torch.nn.Parameter(torch.tensor(a__     ).float()     )
            __snake_case      :Union[str, Any] 							=    torch.nn.Parameter(torch.tensor(a__     ).float()     )
            __snake_case      :Optional[Any] 							=    True



       def        __lowercase    (				self      ,    a__=None     )			->   List[Any]:






            '''simple docstring'''





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






def 	UpperCamelCase	(							snake_case__  : List[Any]						,snake_case__  : int = 16	):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

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

     __snake_case      :Tuple 							=    {v: i for i, v in enumerate(snake_case__	)}

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

     def collate_fn(snake_case__  : Union[str, 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(snake_case__						,padding="""max_length"""						,max_length=128						,return_tensors="""pt"""	)
          return tokenizer.pad(snake_case__						,padding="""longest"""						,return_tensors="""pt"""	)

     # Instantiate dataloaders.
     __snake_case      :Dict 							=    DataLoader(tokenized_datasets["""train"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=2	)
     __snake_case      :Tuple 							=    DataLoader(tokenized_datasets["""validation"""]						,shuffle=snake_case__						,collate_fn=snake_case__						,batch_size=1	)

     return train_dataloader, eval_dataloader










lowerCamelCase__       		=  8.3_1_4_4_5_9_8






def 	UpperCamelCase	(							snake_case__  : float						,snake_case__  : float	):




     '''simple docstring'''






     if temperature < 0:
          raise Exception("""Temperature cannot be less than 0 K"""	)
     if molar_mass <= 0:
          raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""	)
     else:
          return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # example
     lowerCamelCase__       		=  300
     lowerCamelCase__       		=  28
     lowerCamelCase__       		=  rms_speed_of_molecule(temperature, molar_mass)
     print(f'''Vrms of Nitrogen gas at 300 K is {vrms} m/s''')


