

"""simple docstring"""






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
							__lowerCAmelCase				      =   None


__lowerCAmelCase				      =   logging.get_logger(__name__)

__lowerCAmelCase				      =   {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase				      =   {
    '''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'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCAmelCase				      =   {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    List[Any]     =  VOCAB_FILES_NAMES
							__lowerCAmelCase					:    Tuple     =  PRETRAINED_VOCAB_FILES_MAP
							__lowerCAmelCase					:    Union[str, Any]     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowerCAmelCase					:    int     =  ["input_ids", "attention_mask"]
							__lowerCAmelCase					:    str     =  XLMRobertaTokenizer

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

									"""simple docstring"""
									lowercase										=							AddedToken(a ,		lstrip=a ,		rstrip=a    ) if isinstance(a ,		a    ) else mask_token

									super().__init__(
									    a ,		tokenizer_file=a ,		bos_token=a ,		eos_token=a ,		sep_token=a ,		cls_token=a ,		unk_token=a ,		pad_token=a ,		mask_token=a ,		**a ,		)

									lowercase										=							vocab_file
									lowercase										=							False if not self.vocab_file else True

							def 					_lowerCAmelCase  ( self       :    List[str] ,		a       :    List[int] ,		a       :    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]
									lowercase										=							[self.cls_token_id]
									lowercase										=							[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep

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

									"""simple docstring"""
									lowercase										=							[self.sep_token_id]
									lowercase										=							[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]

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

									"""simple docstring"""







									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''    )

									if not os.path.isdir(a    ):
											logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""    )
											return
									lowercase										=							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    ):
											copyfile(self.vocab_file ,		a    )

									return (out_vocab_file,)







'''simple docstring'''


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE						):


			'''simple docstring'''





			__a				:				Union[List[PIL.Image.Image], np.ndarray]
			__a				:				Optional[List[bool]]


if is_transformers_available() and is_torch_available():
					from .modeling_roberta_series import RobertaSeriesModelWithTransformation
					from .pipeline_alt_diffusion import AltDiffusionPipeline
					from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
