






"""simple docstring"""






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						:						Optional[Any]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						str					   =       "▁"

SCREAMING_SNAKE_CASE						:						int					   =       {"vocab_file": "prophetnet.tokenizer"}

SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       collections.OrderedDict()
				with open(lowerCAmelCase__       ,'r'       ,encoding='utf-8'				) as reader:
								A__							 =       reader.readlines()
				for index, token in enumerate(lowerCAmelCase__				):
								A__							 =       token.rstrip('\n'				)
								A__							 =       index
				return vocab







class       snake_case_       (    _SCREAMING_SNAKE_CASE       ):
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					["input_ids", "attention_mask"]


	def __init__(      self  ,					__a  ,					__a="[SEP]"  ,					__a="[SEP]"  ,					__a="[SEP]"  ,					__a="[UNK]"  ,					__a="[PAD]"  ,					__a="[CLS]"  ,					__a="[MASK]"  ,					__a = None  ,					**__a  ,					):


					"""simple docstring"""

					A__							 =       {} if sp_model_kwargs is None else sp_model_kwargs

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

					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__							 =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(str(A_						)						)
					A__							 =       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__							 =       {'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

					for i in range(10						):
									A__							 =       f'''[unused{i}]'''
									A__							 =       5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					A__							 =       12
					A__							 =       {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(A_						)


	def __getstate__(      self						):


					"""simple docstring"""

					A__							 =       self.__dict__.copy()
					A__							 =       None
					return state


	def __setstate__(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       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__							 =       {}

					A__							 =       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
					self.sp_model.Load(self.vocab_file						)


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None  ,					__a = False						):


					"""simple docstring"""

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

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       [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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return len(self.sp_model						) + self.fairseq_offset


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       {self.convert_ids_to_tokens(A_						): i for i in range(self.vocab_size						)}
					vocab.update(self.added_tokens_encoder						)
					return vocab


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					A__							 =       self.sp_model.PieceToId(A_						)

					# 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       _UpperCAmelCase						(      self  ,					__a						):


					"""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       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					A__							 =       ''.join(A_						).replace(A_  ,					' '						).strip()
					return out_string


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

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

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

					return (out_vocab_file,)






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					if token_ids_a is None:
									return token_ids_a + [self.sep_token_id]
					A__							 =       [self.sep_token_id]
					return token_ids_a + sep + token_ids_a + sep










"""simple docstring"""






def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				return 1 if input_a == input_a else 0



def  __lowerCamelCase					(					):
				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))


