
"""simple docstring"""







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

from accelerate.utils.dataclasses import DistributedType



class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Dict  , a_							:			Optional[int]=2  , a_							:			List[Any]=3  , a_							:			int=64  , a_							:			Dict=None			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	np.random.default_rng(a_			)
      UpperCAmelCase_    :     Union[str, Any]            =	length
      UpperCAmelCase_    :     str            =	rng.normal(size=(length,)			).astype(np.floataa			)
      UpperCAmelCase_    :     int            =	a * self.x + b + rng.normal(scale=0.1  , size=(length,)			).astype(np.floataa			)




 def __len__(			self							:			List[Any]			)->       int:



      """simple docstring"""

      return self.length






 def __getitem__(			self							:			List[str]  , a_							:			int			)->       Dict:



      """simple docstring"""

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



class 		UpperCAmelCase_						(torch.nn.Module    ):
 """simple docstring"""




 def __init__(			self							:			int  , a_							:			Optional[Any]=0  , a_							:			Any=0  , a_							:			Union[str, Any]=False			)->       List[Any]:



      """simple docstring"""

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






 def 	a   (			self							:			Dict  , a_							:			Tuple=None			)->       int:



      """simple docstring"""

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



class 		UpperCAmelCase_						(torch.nn.Module    ):
 """simple docstring"""




 def __init__(			self							:			List[str]  , a_							:			List[Any]=0  , a_							:			Dict=0  , a_							:			Optional[Any]=False			)->       Optional[Any]:



      """simple docstring"""

      super().__init__()
      UpperCAmelCase_    :     Any            =	torch.nn.Parameter(torch.tensor(a_			).float()			)
      UpperCAmelCase_    :     Union[str, Any]            =	torch.nn.Parameter(torch.tensor(a_			).float()			)
      UpperCAmelCase_    :     int            =	True






 def 	a   (			self							:			Dict  , a_							:			Optional[int]=None			)->       Optional[int]:



      """simple docstring"""

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




def       A_      (       lowercase    ,			lowercase = 16   )  ->				int:


     """simple docstring"""







     from datasets import load_dataset
     from transformers import AutoTokenizer

     UpperCAmelCase_    :     List[Any]            =	AutoTokenizer.from_pretrained("""bert-base-cased"""   )
     UpperCAmelCase_    :     Optional[int]            =	{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     UpperCAmelCase_    :     Dict            =	load_dataset("""csv"""    ,			data_files=lowercase   )
     UpperCAmelCase_    :     Any            =	datasets["""train"""].unique("""label"""   )

     UpperCAmelCase_    :     Tuple            =	{v: i for i, v in enumerate(lowercase   )}

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

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

     # Instantiate dataloaders.
     UpperCAmelCase_    :     List[str]            =	DataLoader(tokenized_datasets["""train"""]    ,			shuffle=lowercase    ,			collate_fn=lowercase    ,			batch_size=2   )
     UpperCAmelCase_    :     Tuple            =	DataLoader(tokenized_datasets["""validation"""]    ,			shuffle=lowercase    ,			collate_fn=lowercase    ,			batch_size=1   )

     return train_dataloader, eval_dataloader



"""simple docstring"""







def       A_      (       lowercase = 1000   )  ->				int:


     """simple docstring"""







     return sum(e for e in range(3    ,			lowercase   ) if e % 3 == 0 or e % 5 == 0   )


if __name__ == "__main__":
       print(f"""{solution() = }""")

