






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_UpperCamelCase			:	int								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase			:	Optional[int]								={
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase			:	str								={
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   VOCAB_FILES_NAMES
		__snake_case      :       List[Any]     =   PRETRAINED_VOCAB_FILES_MAP
		__snake_case      :       int     =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__snake_case      :       Union[str, Any]     =   ["input_ids", "attention_mask"]
		__snake_case      :       Optional[int]     =   RobertaTokenizer







		def __init__(					self       ,A__=None       ,A__=None       ,A__=None       ,A__="replace"       ,A__="<s>"       ,A__="</s>"       ,A__="</s>"       ,A__="<s>"       ,A__="<unk>"       ,A__="<pad>"       ,A__="<mask>"       ,A__=False       ,A__=True       ,**A__       ,):
						super().__init__(
						    A__       ,A__       ,tokenizer_file=A__       ,errors=A__       ,bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,cls_token=A__       ,unk_token=A__       ,pad_token=A__       ,mask_token=A__       ,add_prefix_space=A__       ,trim_offsets=A__       ,**A__       ,)

						_A	:				Union[str, Any]   =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
						if pre_tok_state.get('''add_prefix_space'''       ,A__		) != add_prefix_space:
										_A	:				List[str]   =      getattr(A__       ,pre_tok_state.pop('''type'''		)		)
										_A	:				int   =      add_prefix_space
										_A	:				Dict   =      pre_tok_class(**A__		)

						_A	:				List[str]   =      add_prefix_space

						_A	:				Union[str, Any]   =      '''post_processor'''
						_A	:				Any   =      getattr(self.backend_tokenizer       ,A__       ,A__		)
						if tokenizer_component_instance:
										_A	:				Any   =      json.loads(tokenizer_component_instance.__getstate__()		)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
														_A	:				List[str]   =      tuple(state['''sep''']		)
										if "cls" in state:
														_A	:				Tuple   =      tuple(state['''cls''']		)

										_A	:				str   =      False

										if state.get('''add_prefix_space'''       ,A__		) != add_prefix_space:
														_A	:				Dict   =      add_prefix_space
														_A	:				Dict   =      True

										if state.get('''trim_offsets'''       ,A__		) != trim_offsets:
														_A	:				Dict   =      trim_offsets
														_A	:				Tuple   =      True

										if changes_to_apply:
														_A	:				List[Any]   =      getattr(A__       ,state.pop('''type'''		)		)
														_A	:				Union[str, Any]   =      component_class(**A__		)
														setattr(self.backend_tokenizer       ,A__       ,A__		)







		@property
		def  A__      (					self		):
						if self._mask_token is None:
										if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''		)
										return None
						return str(self._mask_token		)







		@mask_token.setter
		def  A__      (					self       ,A__		):
						_A	:				Optional[int]   =      AddedToken(A__       ,lstrip=A__       ,rstrip=A__		) if isinstance(A__       ,A__		) else value
						_A	:				Union[str, Any]   =      value







		def  A__      (					self       ,*A__       ,**A__		):
						_A	:				List[Any]   =      kwargs.get('''is_split_into_words'''       ,A__		)
						assert self.add_prefix_space or not is_split_into_words, (
						    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						_A	:				Dict   =      kwargs.get('''is_split_into_words'''       ,A__		)

						assert self.add_prefix_space or not is_split_into_words, (
						    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None		):
						_A	:				List[Any]   =      self._tokenizer.model.save(A__       ,name=A__		)
						return tuple(A__		)







		def  A__      (					self       ,A__       ,A__=None		):
						_A	:				Union[str, Any]   =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
										return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







		def  A__      (					self       ,A__       ,A__ = None		):
						_A	:				List[str]   =      [self.sep_token_id]
						_A	:				List[str]   =      [self.cls_token_id]

						if token_ids_a is None:
										return len(cls + token_ids_a + sep		) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]











from collections import defaultdict



def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> bool:
				_A	:				Union[str, Any]   =      first_str.lower().strip()
				_A	:				int   =      second_str.lower().strip()

				# Remove whitespace
				_A	:				int   =      first_str.replace(''' '''					,  ''''''   )
				_A	:				Optional[Any]   =      second_str.replace(''' '''					,  ''''''   )

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

				# Default values for count should be 0
				_A	:				defaultdict[str, int]   =      defaultdict(__lowercase   )

				# For each character in input strings,
				# increment count in the corresponding
				for i in range(len(__lowercase   )   ):
								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()
	_UpperCamelCase			:	Dict								=input('Enter the first string ').strip()
	_UpperCamelCase			:	Union[str, Any]								=input('Enter the second string ').strip()

	_UpperCamelCase			:	Dict								=check_anagrams(input_a, input_b)
	print(f'''{input_a} and {input_b} are {"" if status else "not "}anagrams.''')



