



'''simple docstring'''




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets






class 						_UpperCamelCase  (							_UpperCAmelCase    ):


			'''simple docstring'''

			@slow
			@require_torch
			def UpperCamelCase__					(							self     : Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny"""							,     """prajjwal1/bert-tiny"""						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		BertTokenizer.from_pretrained("""bert-base-uncased"""						)

					__SCREAMING_SNAKE_CASE       :		Dict           =		bertabert.config.encoder.vocab_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tokenizer.sep_token_id
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		tokenizer.cls_token_id
					__SCREAMING_SNAKE_CASE       :		int           =		1_2_8

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		datasets.load_dataset("""cnn_dailymail"""							,     """3.0.0"""							,     split="""train[:1%]"""						)
					__SCREAMING_SNAKE_CASE       :		int           =		datasets.load_dataset("""cnn_dailymail"""							,     """3.0.0"""							,     split="""validation[:1%]"""						)

					__SCREAMING_SNAKE_CASE       :		str           =		train_dataset.select(range(3_2						)						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		val_dataset.select(range(1_6						)						)

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		4

					def _map_to_encoder_decoder_inputs(lowerCAmelCase__     : Dict						):
							# Tokenizer will automatically set [BOS] <text> [EOS]
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tokenizer(batch["""article"""]							,     padding="""max_length"""							,     truncation=lowercase_							,     max_length=5_1_2						)
							__SCREAMING_SNAKE_CASE       :		List[str]           =		tokenizer(batch["""highlights"""]							,     padding="""max_length"""							,     truncation=lowercase_							,     max_length=1_2_8						)
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		inputs.input_ids
							__SCREAMING_SNAKE_CASE       :		List[str]           =		inputs.attention_mask

							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		outputs.input_ids
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		outputs.input_ids.copy()
							__SCREAMING_SNAKE_CASE       :		int           =		[
							    [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
							]
							__SCREAMING_SNAKE_CASE       :		List[str]           =		outputs.attention_mask

							assert all(len(lowercase_						) == 5_1_2 for x in inputs.input_ids						)
							assert all(len(lowercase_						) == 1_2_8 for x in outputs.input_ids						)

							return batch

					def _compute_metrics(lowerCAmelCase__     : Union[str, Any]						):
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		pred.label_ids
							__SCREAMING_SNAKE_CASE       :		List[str]           =		pred.predictions

							# all unnecessary tokens are removed
							__SCREAMING_SNAKE_CASE       :		List[str]           =		tokenizer.batch_decode(lowercase_							,     skip_special_tokens=lowercase_						)
							__SCREAMING_SNAKE_CASE       :		Tuple           =		tokenizer.batch_decode(lowercase_							,     skip_special_tokens=lowercase_						)

							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		sum([int(pred_str[i] == label_str[i]						) for i in range(len(lowercase_						)						)]						) / len(lowercase_						)

							return {"accuracy": accuracy}

					# map train dataset
					__SCREAMING_SNAKE_CASE       :		Any           =		train_dataset.map(
					    _map_to_encoder_decoder_inputs							,     batched=lowercase_							,     batch_size=lowercase_							,     remove_columns=["""article""", """highlights"""]							,     )
					train_dataset.set_format(
					    type="""torch"""							,     columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]							,     )

					# same for validation dataset
					__SCREAMING_SNAKE_CASE       :		Any           =		val_dataset.map(
					    _map_to_encoder_decoder_inputs							,     batched=lowercase_							,     batch_size=lowercase_							,     remove_columns=["""article""", """highlights"""]							,     )
					val_dataset.set_format(
					    type="""torch"""							,     columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""]							,     )

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.get_auto_remove_tmp_dir()

					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		SeqaSeqTrainingArguments(
					    output_dir=lowercase_							,     per_device_train_batch_size=lowercase_							,     per_device_eval_batch_size=lowercase_							,     predict_with_generate=lowercase_							,     evaluation_strategy="""steps"""							,     do_train=lowercase_							,     do_eval=lowercase_							,     warmup_steps=0							,     eval_steps=2							,     logging_steps=2							,     )

					# instantiate trainer
					__SCREAMING_SNAKE_CASE       :		int           =		SeqaSeqTrainer(
					    model=lowercase_							,     args=lowercase_							,     compute_metrics=_compute_metrics							,     train_dataset=lowercase_							,     eval_dataset=lowercase_							,     tokenizer=lowercase_							,     )

					# start training
					trainer.train()
'''simple docstring'''




from collections import defaultdict




def 							lowerCamelCase	(      UpperCAmelCase__       :					str      ,      UpperCAmelCase__       :					str )			->      bool:
							lowercase_					:			Tuple										=       first_str.lower().strip()
							lowercase_					:			List[Any]										=       second_str.lower().strip()

							# Remove whitespace
							lowercase_					:			Dict										=       first_str.replace(""" """      ,      """""" )
							lowercase_					:			Tuple										=       second_str.replace(""" """      ,      """""" )

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

							# Default values for count should be 0
							lowercase_					:			defaultdict[str, int]										=       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()
						_lowercase						: int						=						input("Enter the first string ").strip()
						_lowercase						: Tuple						=						input("Enter the second string ").strip()

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





