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

from accelerate.utils.dataclasses import DistributedType







class 			__A:




				def __init__(     self		,     _snake_case=2		,     _snake_case=3		,     _snake_case=64		,     _snake_case=None				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    np.random.default_rng(_snake_case				)
								__a										=    length
								__a										=    rng.normal(size=(length,)				).astype(np.floataa				)
								__a										=    a * self.x + b + rng.normal(scale=0.1		,     size=(length,)				).astype(np.floataa				)




				def __len__(     self				)    ->      Tuple:


								'''simple docstring'''




								return self.length




				def __getitem__(     self		,     _snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




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







class 			__A( torch.nn.Module			):




				def __init__(     self		,     _snake_case=0		,     _snake_case=0		,     _snake_case=False				)    ->      Any:


								'''simple docstring'''




								super().__init__()
								__a										=    torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
								__a										=    torch.nn.Parameter(torch.tensor([2, 3]				).float()				)
								__a										=    True




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None				)    ->      Tuple:


								'''simple docstring'''




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







class 			__A( torch.nn.Module			):




				def __init__(     self		,     _snake_case=0		,     _snake_case=0		,     _snake_case=False				)    ->      Dict:


								'''simple docstring'''




								super().__init__()
								__a										=    torch.nn.Parameter(torch.tensor(_snake_case				).float()				)
								__a										=    torch.nn.Parameter(torch.tensor(_snake_case				).float()				)
								__a										=    True




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None				)    ->      List[Any]:


								'''simple docstring'''




								if self.first_batch:
												print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""				)
												__a										=    False
								return x * self.a + self.b



def     __lowerCAmelCase			(			a__						,    a__ = 16  )       ->  List[str]:
				from datasets import load_dataset
				from transformers import AutoTokenizer

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

				__a										=    {v: i for i, v in enumerate(a__  )}

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

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

				# Instantiate dataloaders.
				__a										=    DataLoader(tokenized_datasets['''train''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=2  )
				__a										=    DataLoader(tokenized_datasets['''validation''']						,    shuffle=a__						,    collate_fn=a__						,    batch_size=1  )

				return train_dataloader, eval_dataloader

"""simple docstring"""





from collections import defaultdict



def 						_lowerCAmelCase				(				UpperCamelCase_  ,				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = first_str.lower().strip()
		__SCREAMING_SNAKE_CASE            = second_str.lower().strip()

		# Remove whitespace
		__SCREAMING_SNAKE_CASE            = first_str.replace(""" """  ,				""""""				)
		__SCREAMING_SNAKE_CASE            = second_str.replace(""" """  ,				""""""				)

		# Strings of different lengths are not anagrams
		if len(UpperCamelCase_				) != len(UpperCamelCase_				):
				return False

		# Default values for count should be 0
		__SCREAMING_SNAKE_CASE            = defaultdict(UpperCamelCase_				)

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(UpperCamelCase_				)				):
				count[first_str[i]] += 1
				count[second_str[i]] -= 1

		return all(_count == 0 for _count in count.values()				)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							__magic_name__       =			input("Enter the first string ").strip()
							__magic_name__       =			input("Enter the second string ").strip()

							__magic_name__       =			check_anagrams(input_a, input_b)
							print(F"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")




