





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A       :   str					= logging.get_logger(__name__)

A       :   Tuple					= '''▁'''

A       :   int					= {'''vocab_file''': '''sentencepiece.bpe.model'''}

A       :   int					= {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

A       :   Optional[Any]					= {
    '''xlm-roberta-base''': 5_1_2,
    '''xlm-roberta-large''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
}


class     A     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




			__lowerCamelCase      :			Tuple							     =       VOCAB_FILES_NAMES
			__lowerCamelCase      :			Dict							     =       PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase      :			List[str]							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase      :			Any							     =       ['''input_ids''', '''attention_mask''']






			def __init__(     self  :       Dict				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       str="<s>"				,      __lowerCAmelCase  :       Any="</s>"				,      __lowerCAmelCase  :       Optional[Any]="</s>"				,      __lowerCAmelCase  :       Any="<s>"				,      __lowerCAmelCase  :       str="<unk>"				,      __lowerCAmelCase  :       Tuple="<pad>"				,      __lowerCAmelCase  :       Any="<mask>"				,      __lowerCAmelCase  :       Optional[Dict[str, Any]] = None				,      **__lowerCAmelCase  :       Any				,      )      ->      None:







					"""simple docstring"""






					A__     =  AddedToken(__lowerCAmelCase				,      lstrip=__lowerCAmelCase				,      rstrip=__lowerCAmelCase  ) if isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ) else mask_token

					A__     =  {} if sp_model_kwargs is None else sp_model_kwargs

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

					A__     =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.Load(str(__lowerCAmelCase  )  )
					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'

					# Mimic fairseq token-to-id alignment for the first 4 token
					A__     =  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

					# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
					A__     =  1

					A__     =  len(self.sp_model  ) + self.fairseq_offset
					A__     =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






			def __getstate__(     self  :       Any  )      ->      Any:







					"""simple docstring"""






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






			def __setstate__(     self  :       Optional[int]				,      __lowerCAmelCase  :       List[str]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  d

					# for backward compatibility
					if not hasattr(self				,      """sp_model_kwargs"""  ):
							A__     =  {}

					A__     =  spm.SentencePieceProcessor(**self.sp_model_kwargs  )
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )






			def   a_			(     self  :       int				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       Optional[List[int]] = None  )      ->      List[int]:







					"""simple docstring"""






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






			def   a_			(     self  :       Dict				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       Optional[List[int]] = None				,      __lowerCAmelCase  :       bool = False  )      ->      List[int]:







					"""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 [1] + ([0] * len(__lowerCAmelCase  )) + [1]
					return [1] + ([0] * len(__lowerCAmelCase  )) + [1, 1] + ([0] * len(__lowerCAmelCase  )) + [1]






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       List[int]				,      __lowerCAmelCase  :       Optional[List[int]] = None  )      ->      List[int]:







					"""simple docstring"""






					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 + sep + token_ids_a + sep  ) * [0]






			@property
			def   a_			(     self  :       Dict  )      ->      List[str]:







					"""simple docstring"""






					return len(self.sp_model  ) + self.fairseq_offset + 1  # Add the <mask> token






			def   a_			(     self  :       Dict  )      ->      int:







					"""simple docstring"""






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






			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       str  )      ->      List[str]:







					"""simple docstring"""






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






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       List[str]  )      ->      Any:







					"""simple docstring"""






					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					A__     =  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   a_			(     self  :       Any				,      __lowerCAmelCase  :       Dict  )      ->      Any:







					"""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   a_			(     self  :       Any				,      __lowerCAmelCase  :       Any  )      ->      Dict:







					"""simple docstring"""






					A__     =  """""".join(__lowerCAmelCase  ).replace(__lowerCAmelCase				,      """ """  ).strip()
					return out_string







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Optional[str] = None  )      ->      Tuple[str]:







					"""simple docstring"""






					if not os.path.isdir(__lowerCAmelCase  ):
							logger.error(f'Vocabulary path ({save_directory}) should be a directory'  )
							return
					A__     =  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__     =  self.sp_model.serialized_model_proto()
									fi.write(__lowerCAmelCase  )

					return (out_vocab_file,)










def   __lowerCamelCase   (				__a  :Dict       )						->							Optional[int]:






		"""simple docstring"""

		A__     =  []
		A__     =  []
		A__     =  {
		    """^""": 3,
		    """*""": 2,
		    """/""": 2,
		    """%""": 2,
		    """+""": 1,
		    """-""": 1,
		}  # Priority of each operator
		A__     =  len(__a       ) if (len(__a       ) > 7) else 7

		# Print table header for output
		print(
		    """Symbol""".center(8       )       ,	"""Stack""".center(__a       )       ,	"""Postfix""".center(__a       )       ,	sep=""" | """       ,	)
		print("""-""" * (print_width * 3 + 7)       )

		for x in infix:
				if x.isalpha() or x.isdigit():
						post_fix.append(__a       )  # if x is Alphabet / Digit, add it to Postfix
				elif x == "(":
						stack.append(__a       )  # if x is "(" push to Stack
				elif x == ")":  # if x is ")" pop stack until "(" is encountered
						while stack[-1] != "(":
								post_fix.append(stack.pop()       )  # Pop stack & add the content to Postfix
						stack.pop()
				else:
						if len(__a       ) == 0:
								stack.append(__a       )  # If stack is empty, push x to stack
						else:  # while priority of x is not > priority of element in the stack
								while len(__a       ) > 0 and priority[x] <= priority[stack[-1]]:
										post_fix.append(stack.pop()       )  # pop stack & add to Postfix
								stack.append(__a       )  # push x to stack

				print(
				    x.center(8       )       ,	("""""".join(__a       )).ljust(__a       )       ,	("""""".join(__a       )).ljust(__a       )       ,	sep=""" | """       ,	)  # Output in tabular format

		while len(__a       ) > 0:  # while stack is not empty
				post_fix.append(stack.pop()       )  # pop stack & add to Postfix
				print(
				    """ """.center(8       )       ,	("""""".join(__a       )).ljust(__a       )       ,	("""""".join(__a       )).ljust(__a       )       ,	sep=""" | """       ,	)  # Output in tabular format

		return "".join(__a       )  # return Postfix as str







def   __lowerCamelCase   (				__a  :List[str]       )						->							Union[str, Any]:






		"""simple docstring"""

		A__     =  list(infix[::-1]       )  # reverse the infix equation

		for i in range(len(__a       )       ):
				if infix[i] == "(":
						A__     =  """)"""  # change "(" to ")"
				elif infix[i] == ")":
						A__     =  """("""  # change ")" to "("

		return (infix_2_postfix("""""".join(__a       )       ))[
		    ::-1
		]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
					A       :   Union[str, Any]					= input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
					A       :   Dict					= ''''''.join(Infix.split())  # Remove spaces from the input
					print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



