





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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_		     = """▁"""

SCREAMING_SNAKE_CASE_		     = {"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE_		     = {
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

SCREAMING_SNAKE_CASE_		     = {
    """facebook/xglm-564M""": 2_0_4_8,
}

class 	UpperCamelCase__				(   __UpperCAmelCase     ):



					'''simple docstring'''

					__snake_case					:			Optional[Any]				=    VOCAB_FILES_NAMES
					__snake_case					:			Optional[Any]				=    PRETRAINED_VOCAB_FILES_MAP
					__snake_case					:			Optional[Any]				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__snake_case					:			str				=    ["""input_ids""", """attention_mask"""]






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




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		{} if sp_model_kwargs is None else sp_model_kwargs

											# Compatibility with the original tokenizer
											SCREAMING_SNAKE_CASE	=		7
											SCREAMING_SNAKE_CASE	=		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words )]

											SCREAMING_SNAKE_CASE	=		kwargs.get("""additional_special_tokens"""		,[] )
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

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

											SCREAMING_SNAKE_CASE	=		spm.SentencePieceProcessor(**self.sp_model_kwargs )
											self.sp_model.Load(str(UpperCAmelCase_ ) )
											SCREAMING_SNAKE_CASE	=		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'

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

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

											SCREAMING_SNAKE_CASE	=		len(self.sp_model )
											SCREAMING_SNAKE_CASE	=		{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
											self.fairseq_tokens_to_ids.update(UpperCAmelCase_ )

											SCREAMING_SNAKE_CASE	=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}






					def __getstate__(							self		:					List[str] )  ->	Optional[Any]:




											'''simple docstring'''


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






					def __setstate__(							self		:					Optional[Any]		,lowerCamelCase__		:					Dict )  ->	Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		d

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

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






					def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[Any] = None )  ->	Tuple:




											'''simple docstring'''


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






					def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					Dict = None		,lowerCamelCase__		:					List[Any] = False )  ->	Union[str, Any]:




											'''simple docstring'''


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

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






					def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					str		,lowerCamelCase__		:					int = None )  ->	List[Any]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		[self.sep_token_id]

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






					@property
					def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Dict:




											'''simple docstring'''


											return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words






					def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[int]:




											'''simple docstring'''


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






					def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					List[Any] )  ->	Tuple:




											'''simple docstring'''


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






					def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					Dict )  ->	Tuple:




											'''simple docstring'''


											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											SCREAMING_SNAKE_CASE	=		self.sp_model.PieceToId(UpperCAmelCase_ )

											# 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        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					int )  ->	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        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[Any] )  ->	Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE	=		"""""".join(UpperCAmelCase_ ).replace(UpperCAmelCase_		,""" """ ).strip()
											return out_string






					def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					List[str] = None )  ->	int:




											'''simple docstring'''


											if not os.path.isdir(UpperCAmelCase_ ):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
																	return
											SCREAMING_SNAKE_CASE	=		os.path.join(
											    UpperCAmelCase_		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

											return (out_vocab_file,)









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(a - b			) for a, b in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     None:


      '''simple docstring'''


      if point:
            if isinstance(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			):
                  for item in point:
                        if not isinstance(_SCREAMING_SNAKE_CASE				,		(int, float)			):
                              SCREAMING_SNAKE_CASE	=		(
                                  """Expected a list of numbers as input, found """
                                  F"""{type(_SCREAMING_SNAKE_CASE			).__name__}"""
                              )
                              raise TypeError(_SCREAMING_SNAKE_CASE			)
            else:
                  SCREAMING_SNAKE_CASE	=		F"""Expected a list of numbers as input, found {type(_SCREAMING_SNAKE_CASE			).__name__}"""
                  raise TypeError(_SCREAMING_SNAKE_CASE			)
      else:
            raise ValueError("""Missing an input"""			)



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      _validate_point(_SCREAMING_SNAKE_CASE			)
      _validate_point(_SCREAMING_SNAKE_CASE			)
      if len(_SCREAMING_SNAKE_CASE			) != len(_SCREAMING_SNAKE_CASE			):
            raise ValueError("""Both points must be in the same n-dimensional space"""			)

      return float(sum(abs(x - y			) for x, y in zip(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)			)			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()


