





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

from accelerate.utils.dataclasses import DistributedType

class  __A:

     def __init__(      self,       A=2,       A=3,       A=64,       A=None      ):





            """simple docstring"""

            _UpperCamelCase              =						np.random.default_rng(A      )
            _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      ):





            """simple docstring"""

            return self.length



     def __getitem__(      self,       A      ):





            """simple docstring"""

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






class  __A(						torch.nn.Module ):

     def __init__(      self,       A=0,       A=0,       A=False      ):





            """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,       A=None      ):





            """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  __A(						torch.nn.Module ):

     def __init__(      self,       A=0,       A=0,       A=False      ):





            """simple docstring"""

            super().__init__()
            _UpperCamelCase              =						torch.nn.Parameter(torch.tensor(A      ).float()      )
            _UpperCamelCase              =						torch.nn.Parameter(torch.tensor(A      ).float()      )
            _UpperCamelCase              =						True



     def 	_UpperCamelCase     (      self,       A=None      ):





            """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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase = 16					):
       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













def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase = 1_000					):
       _UpperCamelCase              =						3
       _UpperCamelCase              =						0
       while a < n:
              if a % 3 == 0 or a % 5 == 0:
                     result += a
              elif a % 15 == 0:
                     result -= a
              a += 1
       return result


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






