




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

from accelerate.utils.dataclasses import DistributedType





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=2  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=3  ,      SCREAMING_SNAKE_CASE__	:	Tuple=64  ,      SCREAMING_SNAKE_CASE__	:	Tuple=None  ) ->			int:
         lowerCAmelCase__            =					np.random.default_rng(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					length
         lowerCAmelCase__            =					rng.normal(size=(length,)  ).astype(np.floataa  )
         lowerCAmelCase__            =					a * self.x + b + rng.normal(scale=0.1  ,      size=(length,)  ).astype(np.floataa  )


    def __len__(			self	:	Dict  ) ->			List[str]:
         return self.length


    def __getitem__(			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Any  ) ->			Optional[int]:
         return {"x": self.x[i], "y": self.y[i]}






class 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	str  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=0  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0  ,      SCREAMING_SNAKE_CASE__	:	str=False  ) ->			Tuple:
         super().__init__()
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor([2, 3]  ).float()  )
         lowerCAmelCase__            =					True


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=None  ) ->			Tuple:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
              lowerCAmelCase__            =					False
         return x * self.a[0] + self.b[0]






class 					__lowerCamelCase			(       torch.nn.Module       ):




    """simple docstring"""

    def __init__(			self	:	Dict  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Any=0  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=False  ) ->			Dict:
         super().__init__()
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__  ).float()  )
         lowerCAmelCase__            =					True


    def   a (			self	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Any=None  ) ->			Dict:
         if self.first_batch:
              print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'  )
              lowerCAmelCase__            =					False
         return x * self.a + self.b






def 				_A  (		lowerCAmelCase_	:   str  ,							lowerCAmelCase_	:   int = 16     ):
     """simple docstring"""

     from datasets import load_dataset
     from transformers import AutoTokenizer

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

     lowerCAmelCase__            =					{v: i for i, v in enumerate(lowerCAmelCase_     )}

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

     def collate_fn(lowerCAmelCase_	:   Dict     ):
          # 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.
     lowerCAmelCase__            =					DataLoader(tokenized_datasets["train"]  ,							shuffle=lowerCAmelCase_  ,							collate_fn=lowerCAmelCase_  ,							batch_size=2     )
     lowerCAmelCase__            =					DataLoader(tokenized_datasets["validation"]  ,							shuffle=lowerCAmelCase_  ,							collate_fn=lowerCAmelCase_  ,							batch_size=1     )

     return train_dataloader, eval_dataloader



from math import factorial, radians







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 18		,				SCREAMING_SNAKE_CASE = 10   ):
      '''simple docstring'''







      A_							       =		angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)

      # Converting from degrees to radians
      A_							       =		radians(SCREAMING_SNAKE_CASE   )

      A_							       =		angle_in_radians
      A_							       =		3
      A_							       =		-1

      for _ in range(SCREAMING_SNAKE_CASE   ):
            result += (b * (angle_in_radians**a)) / factorial(SCREAMING_SNAKE_CASE   )

            A_							       =		-b  # One positive term and the next will be negative and so on...
            a += 2  # Increased by 2 for every term.

      return round(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


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





