


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:
 SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    None


SCREAMING_SNAKE_CASE__				:    Dict         =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				:    List[Any]         =    {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE__				:    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'
        ),
    },
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE__				:    Optional[int]         =    {
    '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 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE    				=						PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE    				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE    				=						['''input_ids''', '''attention_mask''']
     __SCREAMING_SNAKE_CASE    				=						XLMRobertaTokenizer


     def __init__(     self       ,				A_=None       ,				A_=None       ,				A_="<s>"       ,				A_="</s>"       ,				A_="</s>"       ,				A_="<s>"       ,				A_="<unk>"       ,				A_="<pad>"       ,				A_="<mask>"       ,				**A_       ,				):
       # Mask token behave like a normal word, i.e. include the space before it
       _UpperCAmelCase		:       int       =    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_       ,				)

       _UpperCAmelCase		:       Union[str, Any]       =    vocab_file
       _UpperCAmelCase		:       List[str]       =    False if not self.vocab_file else True


     def 					__snake_case(     self       ,				A_       ,				A_ = None						):

       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _UpperCAmelCase		:       Optional[Any]       =    [self.cls_token_id]
       _UpperCAmelCase		:       Union[str, Any]       =    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 					__snake_case(     self       ,				A_       ,				A_ = None						):
       _UpperCAmelCase		:       Tuple       =    [self.sep_token_id]
       _UpperCAmelCase		:       Optional[int]       =    [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 					__snake_case(     self       ,				A_       ,				A_ = None						):
       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
       _UpperCAmelCase		:       List[str]       =    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,)









from dataclasses import dataclass
from enum import Enum
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
class 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 1_5,
         '''sld_guidance_scale''': 2_0,
         '''sld_threshold''': 0.0,
         '''sld_momentum_scale''': 0.0,
         '''sld_mom_beta''': 0.0,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 1_0,
         '''sld_guidance_scale''': 1_0_0_0,
         '''sld_threshold''': 0.01,
         '''sld_momentum_scale''': 0.3,
         '''sld_mom_beta''': 0.4,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 7,
         '''sld_guidance_scale''': 2_0_0_0,
         '''sld_threshold''': 0.025,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }
     __SCREAMING_SNAKE_CASE    				=						{
         '''sld_warmup_steps''': 0,
         '''sld_guidance_scale''': 5_0_0_0,
         '''sld_threshold''': 1.0,
         '''sld_momentum_scale''': 0.5,
         '''sld_mom_beta''': 0.7,
     }


@dataclass
class 		_SCREAMING_SNAKE_CASE    (	A    ):
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42
     __SCREAMING_SNAKE_CASE    				=						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker





