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


_lowerCamelCase   :				Any     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Union[str, Any]     			=						'''▁'''

_lowerCamelCase   :				List[Any]     			=						{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowerCamelCase   :				Optional[Any]     			=						{
    '''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'''
        ),
    }
}

_lowerCamelCase   :				List[Any]     			=						{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}


class   lowercase      (       a       ):
							lowercase__  :    Dict									=							VOCAB_FILES_NAMES
							lowercase__  :    List[str]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Any									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Union[str, Any]									=							["""input_ids""", """attention_mask"""]




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase		,							_UpperCamelCase ) else mask_token

												SCREAMING_SNAKE_CASE           =    {} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_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'

												# Mimic fairseq token-to-id alignment for the first 4 token
												SCREAMING_SNAKE_CASE           =    {"<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
												SCREAMING_SNAKE_CASE           =    1

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




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



												'''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 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   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]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None		,							_UpperCamelCase				:   bool = False )   ->			List[int]:



												'''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 )) + [1]
												return [1] + ([0] * len(_UpperCamelCase )) + [1, 1] + ([0] * len(_UpperCamelCase )) + [1]




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [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 		__snake_case(  self				:   Optional[int] )   ->			Optional[Any]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Tuple )   ->			Tuple:



												'''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 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   str )   ->			List[str]:



												'''simple docstring'''

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




							def 		__snake_case(  self				:   Any		,							_UpperCamelCase				:   Optional[int] )   ->			Optional[Any]:



												'''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 		__snake_case(  self				:   int		,							_UpperCamelCase				:   str )   ->			List[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 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   int )   ->			str:



												'''simple docstring'''

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





							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''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 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							str = " "					):
					SCREAMING_SNAKE_CASE           =    []

					SCREAMING_SNAKE_CASE           =    0
					for index, char in enumerate(UpperCAmelCase__					):
										if char == separator:
															split_words.append(string[last_index:index]					)
															SCREAMING_SNAKE_CASE           =    index + 1
										elif index + 1 == len(UpperCAmelCase__					):
															split_words.append(string[last_index : index + 1]					)
					return split_words


if __name__ == "__main__":
							from doctest import testmod

							testmod()




