






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			'''▁'''

SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''prophetnet.tokenizer'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''vocab_file''': {
        '''microsoft/xprophetnet-large-wiki100-cased''': (
            '''https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'''
        ),
    }
}

SCREAMING_SNAKE_CASE__				     =			{
    '''microsoft/xprophetnet-large-wiki100-cased''': {'''do_lower_case''': False},
}

SCREAMING_SNAKE_CASE__				     =			{
    '''microsoft/xprophetnet-large-wiki100-cased''': 512,
}

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict				):
			__a						:  Any									=     collections.OrderedDict()
			with open(lowerCAmelCase_   ,  'r'   ,  encoding='utf-8'				) as reader:
						__a						:  Optional[Any]									=     reader.readlines()
			for index, token in enumerate(lowerCAmelCase_				):
						__a						:  Optional[int]									=     token.rstrip('\n'				)
						__a						:  Tuple									=     index
			return vocab






class 			_UpperCamelCase(       __SCREAMING_SNAKE_CASE		):
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       Tuple							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       int							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       Optional[int]							=							['''input_ids''', '''attention_mask''']

		def __init__(					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Any="[SEP]"    ,       SCREAMING_SNAKE_CASE__		:    List[Any]="[SEP]"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]="[SEP]"    ,       SCREAMING_SNAKE_CASE__		:    Any="[UNK]"    ,       SCREAMING_SNAKE_CASE__		:    str="[PAD]"    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]="[CLS]"    ,       SCREAMING_SNAKE_CASE__		:    Tuple="[MASK]"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  Any									=     {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=__lowerCamelCase    ,       eos_token=__lowerCamelCase    ,       sep_token=__lowerCamelCase    ,       unk_token=__lowerCamelCase    ,       pad_token=__lowerCamelCase    ,       cls_token=__lowerCamelCase    ,       mask_token=__lowerCamelCase    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **__lowerCamelCase    ,       )

					try:
								import sentencepiece as spm
					except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'    )
								raise

					__a						:  Dict									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(str(__lowerCamelCase    )    )
					__a						:  Tuple									=     vocab_file

					# Original fairseq vocab and spm vocab must be "aligned":
					# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
					# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
					# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
					# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

					# put special tokens and [unused] tokens into the vocab
					__a						:  List[str]									=     {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

					for i in range(1_0    ):
								__a						:  str									=     f'''[unused{i}]'''
								__a						:  Optional[int]									=     5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					__a						:  Union[str, Any]									=     1_2
					__a						:  Tuple									=     {v: k for k, v in self.fairseq_tokens_to_ids.items()}
					for k in self.fairseq_tokens_to_ids.keys():
								self.unique_no_split_tokens.append(__lowerCamelCase    )

		def __getstate__(					self		:    List[str]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     self.__dict__.copy()
					__a						:  Optional[int]									=     None
					return state

		def __setstate__(					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



					__a						:  Dict									=     d
					try:
								import sentencepiece as spm
					except ImportError:
								logger.warning(
								    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
								    ' pip install sentencepiece'    )
								raise

					# for backward compatibility
					if not hasattr(self    ,       'sp_model_kwargs'    ):
								__a						:  Union[str, Any]									=     {}

					__a						:  Any									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.vocab_file    )

		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ,       SCREAMING_SNAKE_CASE__		:    bool = False    ):



					'''simple docstring'''



					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=__lowerCamelCase    ,       token_ids_a=__lowerCamelCase    ,       already_has_special_tokens=__lowerCamelCase    )

					if token_ids_a is None:
								return ([0] * len(__lowerCamelCase    )) + [1]
					return ([0] * len(__lowerCamelCase    )) + [1] + ([0] * len(__lowerCamelCase    )) + [1]

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					__a						:  int									=     [self.sep_token_id]

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

		@property
		def    __lowerCAmelCase  (					self		:    Optional[int]    ):



					'''simple docstring'''



					return len(self.sp_model    ) + self.fairseq_offset

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					__a						:  Any									=     {self.convert_ids_to_tokens(__lowerCamelCase    ): i for i in range(self.vocab_size    )}
					vocab.update(self.added_tokens_encoder    )
					return vocab

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					return self.sp_model.encode(__lowerCamelCase    ,       out_type=__lowerCamelCase    )

		def    __lowerCAmelCase  (					self		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
					__a						:  Any									=     self.sp_model.PieceToId(__lowerCamelCase    )

					# Need to return unknown token if the SP model returned 0
					return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ):



					'''simple docstring'''



					if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset    )

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  int									=     ''.join(__lowerCamelCase    ).replace(__lowerCamelCase    ,       ' '    ).strip()
					return out_string

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					if not os.path.isdir(__lowerCamelCase    ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''    )
								return
					__a						:  List[Any]									=     os.path.join(
					    __lowerCamelCase    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )

					if os.path.abspath(self.vocab_file    ) != os.path.abspath(__lowerCamelCase    ) and os.path.isfile(self.vocab_file    ):
								copyfile(self.vocab_file    ,       __lowerCamelCase    )
					elif not os.path.isfile(self.vocab_file    ):
								with open(__lowerCamelCase    ,       'wb'    ) as fi:
											__a						:  Dict									=     self.sp_model.serialized_model_proto()
											fi.write(__lowerCamelCase    )

					return (out_vocab_file,)



		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					if token_ids_a is None:
								return token_ids_a + [self.sep_token_id]
					__a						:  List[str]									=     [self.sep_token_id]
					return token_ids_a + sep + token_ids_a + sep










"""simple docstring"""






def 			snake_case		(		lowerCAmelCase_  ,						lowerCAmelCase_	)						->				int:
  return 1 if input_a == input_a else 0




def 			snake_case		(		)						->				None:
  assert xnor_gate(0  ,						0	) == 1
  assert xnor_gate(0  ,						1	) == 0
  assert xnor_gate(1  ,						0	) == 0
  assert xnor_gate(1  ,						1	) == 1


if __name__ == "__main__":
     print(xnor_gate(0, 0))
     print(xnor_gate(0, 1))
     print(xnor_gate(1, 0))
     print(xnor_gate(1, 1))





