




import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=1024    ,						lowerCAmelCase=1024    ,						lowerCAmelCase=False    ,						**lowerCAmelCase )->	int:
		SCREAMING_SNAKE_CASE_					      =							AutoTokenizer.from_pretrained(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							SeqaSeqDataset(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						type_path='train'    ,						**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							tok.pad_token_id

		def get_lens(lowerCAmelCase ):
				SCREAMING_SNAKE_CASE_					      =							tqdm(
				    DataLoader(lowerCAmelCase    ,						batch_size=512    ,						num_workers=8    ,						shuffle=lowerCAmelCase    ,						collate_fn=ds.collate_fn )    ,						desc=str(ds.len_file )    ,						)
				SCREAMING_SNAKE_CASE_					      =							[]
				for batch in dl:
						SCREAMING_SNAKE_CASE_					      =							batch['input_ids'].ne(lowerCAmelCase ).sum(1 ).tolist()
						SCREAMING_SNAKE_CASE_					      =							batch['labels'].ne(lowerCAmelCase ).sum(1 ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase    ,						lowerCAmelCase ):
										max_lens.append(max(lowerCAmelCase    ,						lowerCAmelCase ) )
						else:
								max_lens.extend(lowerCAmelCase )
				return max_lens

		SCREAMING_SNAKE_CASE_					      =							get_lens(lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							SeqaSeqDataset(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						type_path='val'    ,						**lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							get_lens(lowerCAmelCase )
		pickle_save(lowerCAmelCase    ,						train_ds.len_file )
		pickle_save(lowerCAmelCase    ,						val_ds.len_file )


if __name__ == "__main__":
						fire.Fire(save_len_file)





from collections import defaultdict


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	bool:
		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(lowerCAmelCase ) != len(lowerCAmelCase ):
				return False

		# Default values for count should be 0
		SCREAMING_SNAKE_CASE_					      =							defaultdict(lowerCAmelCase )

		# For each character in input strings,
		# increment count in the corresponding
		for i in range(len(lowerCAmelCase ) ):
				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()
						SCREAMING_SNAKE_CASE:     Dict									= input('''Enter the first string ''').strip()
						SCREAMING_SNAKE_CASE:     str									= input('''Enter the second string ''').strip()

						SCREAMING_SNAKE_CASE:     Optional[Any]									= check_anagrams(input_a, input_b)
						print(f"""{input_a} and {input_b} are {'' if status else 'not '}anagrams.""")