import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


lowerCamelCase_    :		Union[str, Any]							=	logging.get_logger(__name__)

lowerCamelCase_    :		List[str]							=	{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_    :		Optional[Any]							=	{
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

lowerCamelCase_    :		Union[str, Any]							=	{
    """junnyu/roformer_chinese_small""": 1_536,
    """junnyu/roformer_chinese_base""": 1_536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


lowerCamelCase_    :		Tuple							=	{
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}


class 						a__							(       __snake_case		):
			A__    :       List[str]        =      VOCAB_FILES_NAMES
			A__    :       Optional[int]        =      PRETRAINED_VOCAB_FILES_MAP
			A__    :       Tuple        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A__    :       Union[str, Any]        =      PRETRAINED_INIT_CONFIGURATION
			A__    :       str        =      RoFormerTokenizer



			def __init__(						self				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=True				,						UpperCAmelCase="[UNK]"				,						UpperCAmelCase="[SEP]"				,						UpperCAmelCase="[PAD]"				,						UpperCAmelCase="[CLS]"				,						UpperCAmelCase="[MASK]"				,						UpperCAmelCase=True				,						UpperCAmelCase=None				,						**UpperCAmelCase				,						)			->   Union[str, Any]:
								super().__init__(
								    UpperCAmelCase				,						tokenizer_file=UpperCAmelCase				,						do_lower_case=UpperCAmelCase				,						unk_token=UpperCAmelCase				,						sep_token=UpperCAmelCase				,						pad_token=UpperCAmelCase				,						cls_token=UpperCAmelCase				,						mask_token=UpperCAmelCase				,						tokenize_chinese_chars=UpperCAmelCase				,						strip_accents=UpperCAmelCase				,						**UpperCAmelCase				,						)

								__a		 =       json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
								if (
								    pre_tok_state.get('lowercase'				,						UpperCAmelCase   ) != do_lower_case
								    or pre_tok_state.get('strip_accents'				,						UpperCAmelCase   ) != strip_accents
								):
													__a		 =       getattr(UpperCAmelCase				,						pre_tok_state.pop('type'   )   )
													__a		 =       do_lower_case
													__a		 =       strip_accents
													__a		 =       pre_tok_class(**UpperCAmelCase   )

								__a		 =       do_lower_case



			def __getstate__(						self   )			->   Union[str, Any]:
								__a		 =       self.__dict__.copy()
								__a		 =       BertPreTokenizer()
								return state



			def __setstate__(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       d
								__a		 =       self.__dict__['_tokenizer'].get_vocab()
								__a		 =       PreTokenizer.custom(JiebaPreTokenizer(UpperCAmelCase   )   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=None   )			->   Dict:
								__a		 =       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

								return output



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   List[int]:
								__a		 =       [self.sep_token_id]
								__a		 =       [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   ) * [0] + len(token_ids_a + sep   ) * [1]



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[str]:
								__a		 =       self._tokenizer.model.save(UpperCAmelCase				,						name=UpperCAmelCase   )
								return tuple(UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=None				,						UpperCAmelCase=None				,						UpperCAmelCase=False				,						**UpperCAmelCase				,						)			->   Tuple:
								__a		 =       BertPreTokenizer()
								return super().save_pretrained(UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )





from collections import defaultdict
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       first_str.lower().strip()
					__a		 =       second_str.lower().strip()

					# Remove whitespace
					__a		 =       first_str.replace(' '				,    ''						)
					__a		 =       second_str.replace(' '				,    ''						)

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

					# Default values for count should be 0
					__a		 =       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()
	lowerCamelCase_    :		List[str]							=	input("""Enter the first string """).strip()
	lowerCamelCase_    :		Optional[Any]							=	input("""Enter the second string """).strip()

	lowerCamelCase_    :		str							=	check_anagrams(input_a, input_b)
	print(F'''{input_a} and {input_b} are {'' if status else 'not '}anagrams.''')




