


"""simple docstring"""




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

from accelerate.utils.dataclasses import DistributedType




class 			__snake_case   :




							def __init__(     self:    List[str]     ,							A_:    Tuple=2     ,							A_:    Any=3     ,							A_:    List[Any]=64     ,							A_:    str=None   ):
										__lowerCamelCase          =      np.random.default_rng(A_   )
										__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:    Tuple   ):
										return self.length




							def __getitem__(     self:    Optional[Any]     ,							A_:    Any   ):
										return {"x": self.x[i], "y": self.y[i]}




class 			__snake_case   (torch.nn.Module  ):




							def __init__(     self:    int     ,							A_:    Optional[Any]=0     ,							A_:    int=0     ,							A_:    Optional[Any]=False   ):
										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:    int     ,							A_:    Optional[Any]=None   ):
										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 			__snake_case   (torch.nn.Module  ):




							def __init__(     self:    Optional[int]     ,							A_:    str=0     ,							A_:    List[Any]=0     ,							A_:    Optional[Any]=False   ):
										super().__init__()
										__lowerCamelCase          =      torch.nn.Parameter(torch.tensor(A_   ).float()   )
										__lowerCamelCase          =      torch.nn.Parameter(torch.tensor(A_   ).float()   )
										__lowerCamelCase          =      True




							def   __a   (     self:    Tuple     ,							A_:    List[str]=None   ):
										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_ (	lowercase__ :int,			lowercase__ :int = 16     ):
			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=lowercase__     )
			__lowerCamelCase          =      datasets["""train"""].unique("""label"""     )

			__lowerCamelCase          =      {v: i for i, v in enumerate(lowercase__     )}

			def tokenize_function(lowercase__ :List[Any]     ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCamelCase          =      tokenizer(
						    examples["""sentence1"""],			examples["""sentence2"""],			truncation=lowercase__,			max_length=lowercase__,			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(
			    lowercase__,			batched=lowercase__,			remove_columns=["""sentence1""", """sentence2""", """label"""],			)

			def collate_fn(lowercase__ :int     ):
						# 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.
			__lowerCamelCase          =      DataLoader(tokenized_datasets["""train"""],			shuffle=lowercase__,			collate_fn=lowercase__,			batch_size=2     )
			__lowerCamelCase          =      DataLoader(tokenized_datasets["""validation"""],			shuffle=lowercase__,			collate_fn=lowercase__,			batch_size=1     )

			return train_dataloader, eval_dataloader








"""simple docstring"""




__magic_name__						: Optional[Any]    =				'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'





def       a_ (	lowercase__ :bytes     ):
			# Make sure the supplied data is a bytes-like object
			if not isinstance(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      f'a bytes-like object is required, not \'{data.__class__.__name__}\''
						raise TypeError(lowercase__     )

			__lowerCamelCase          =      """""".join(bin(lowercase__     )[2:].zfill(8     ) for byte in data     )

			__lowerCamelCase          =      len(lowercase__     ) % 6 != 0

			if padding_needed:
						# The padding that will be added later
						__lowerCamelCase          =      B"""=""" * ((6 - len(lowercase__     ) % 6) // 2)

						# Append binary_stream with arbitrary binary digits (0's by default) to make its
						# length a multiple of 6.
						binary_stream += "0" * (6 - len(lowercase__     ) % 6)
			else:
						__lowerCamelCase          =      B""""""

			# Encode every 6 binary digits to their corresponding Base64 character
			return (
			    "".join(
			        B64_CHARSET[int(binary_stream[index : index + 6],			2     )]
			        for index in range(0,			len(lowercase__     ),			6     )     ).encode()
			    + padding
			)





def       a_ (	lowercase__ :str     ):
			# Make sure encoded_data is either a string or a bytes-like object
			if not isinstance(lowercase__,			lowercase__     ) and not isinstance(lowercase__,			lowercase__     ):
						__lowerCamelCase          =      (
						    """argument should be a bytes-like object or ASCII string, """
						    f'not \'{encoded_data.__class__.__name__}\''
						)
						raise TypeError(lowercase__     )

			# In case encoded_data is a bytes-like object, make sure it contains only
			# ASCII characters so we convert it to a string object
			if isinstance(lowercase__,			lowercase__     ):
						try:
									__lowerCamelCase          =      encoded_data.decode("""utf-8"""     )
						except UnicodeDecodeError:
									raise ValueError("""base64 encoded data should only contain ASCII characters"""     )

			__lowerCamelCase          =      encoded_data.count("""="""     )

			# Check if the encoded string contains non base64 characters
			if padding:
						assert all(
						    char in B64_CHARSET for char in encoded_data[:-padding]     ), "Invalid base64 character(s) found."
			else:
						assert all(
						    char in B64_CHARSET for char in encoded_data     ), "Invalid base64 character(s) found."

			# Check the padding
			assert len(lowercase__     ) % 4 == 0 and padding < 3, "Incorrect padding"

			if padding:
						# Remove padding if there is one
						__lowerCamelCase          =      encoded_data[:-padding]

						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )[: -padding * 2]
			else:
						__lowerCamelCase          =      """""".join(
						    bin(B64_CHARSET.index(lowercase__     )     )[2:].zfill(6     ) for char in encoded_data     )

			__lowerCamelCase          =      [
			    int(binary_stream[index : index + 8],			2     )
			    for index in range(0,			len(lowercase__     ),			8     )
			]

			return bytes(lowercase__     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




