






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:
					snake_case_   :	Optional[int]							      =None


snake_case_   :	List[str]							      =logging.get_logger(__name__)

snake_case_   :	Optional[int]							      ={'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case_   :	Union[str, 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'''
        ),
    },
}

snake_case_   :	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        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       int 					= VOCAB_FILES_NAMES
		UpperCAmelCase_							:       str 					= PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase_							:       Union[str, Any] 					= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase_							:       int 					= ['input_ids', 'attention_mask']
		UpperCAmelCase_							:       Optional[int] 					= XLMRobertaTokenizer






		def __init__(      self							, lowercase__=None							, lowercase__=None							, lowercase__="<s>"							, lowercase__="</s>"							, lowercase__="</s>"							, lowercase__="<s>"							, lowercase__="<unk>"							, lowercase__="<pad>"							, lowercase__="<mask>"							, **lowercase__							, )				->				List[str]:
				# Mask token behave like a normal word, i.e. include the space before it
				__A    =							AddedToken(lowercase__							, lstrip=lowercase__							, rstrip=lowercase__						) if isinstance(lowercase__							, lowercase__						) else mask_token

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:

				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 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				List[int]:
				__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]






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__ = None						)				->				Tuple[str]:
				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(lowercase__						):
						logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""						)
						return
				__A    =							os.path.join(
				    lowercase__							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

				if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase__						):
						copyfile(self.vocab_file							, lowercase__						)

				return (out_vocab_file,)





"""simple docstring"""







import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCamelCase_  :  Tuple     		=    logging.get_logger(__name__)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=nn.functional.normalize(lowercase						)
    a											=nn.functional.normalize(lowercase						)
    return torch.mm(lowercase			,       normalized_text_embeds.t()						)






class  __A							(			_SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 __lowerCAmelCase					   =    CLIPConfig

 __lowerCAmelCase					   =    ["CLIPEncoderLayer"]


 def __init__(						self					,       __A		)   ->	int:
     super().__init__(__A		)

     a											=CLIPVisionModel(config.vision_config		)
     a											=nn.Linear(config.vision_config.hidden_size					,       config.projection_dim					,       bias=__A		)

     a											=nn.Parameter(torch.ones(17					,       config.projection_dim		)					,       requires_grad=__A		)
     a											=nn.Parameter(torch.ones(3					,       config.projection_dim		)					,       requires_grad=__A		)

     a											=nn.Parameter(torch.ones(17		)					,       requires_grad=__A		)
     a											=nn.Parameter(torch.ones(3		)					,       requires_grad=__A		)


 @torch.no_grad()
 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	Union[str, Any]:
     a											=self.vision_model(__A		)[1]  # pooled_output
     a											=self.visual_projection(__A		)

     # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
     a											=cosine_distance(__A					,       self.special_care_embeds		).cpu().float().numpy()
     a											=cosine_distance(__A					,       self.concept_embeds		).cpu().float().numpy()

     a											=[]
     a											=image_embeds.shape[0]
     for i in range(__A		):
         a											={'''special_scores''': {}, '''special_care''': [], '''concept_scores''': {}, '''bad_concepts''': []}

         # increase this value to create a stronger `nfsw` filter
         # at the cost of increasing the possibility of filtering benign images
         a											=0.0

         for concept_idx in range(len(special_cos_dist[0]		)		):
             a											=special_cos_dist[i][concept_idx]
             a											=self.special_care_embeds_weights[concept_idx].item()
             a											=round(concept_cos - concept_threshold + adjustment					,       3		)
             if result_img["special_scores"][concept_idx] > 0:
                 result_img["special_care"].append({concept_idx, result_img['''special_scores'''][concept_idx]}		)
                 a											=0.01

         for concept_idx in range(len(cos_dist[0]		)		):
             a											=cos_dist[i][concept_idx]
             a											=self.concept_embeds_weights[concept_idx].item()
             a											=round(concept_cos - concept_threshold + adjustment					,       3		)
             if result_img["concept_scores"][concept_idx] > 0:
                 result_img["bad_concepts"].append(__A		)

         result.append(__A		)

     a											=[len(res['''bad_concepts''']		) > 0 for res in result]

     for idx, has_nsfw_concept in enumerate(__A		):
         if has_nsfw_concept:
             if torch.is_tensor(__A		) or torch.is_tensor(images[0]		):
                 a											=torch.zeros_like(images[idx]		)  # black image
             else:
                 a											=np.zeros(images[idx].shape		)  # black image

     if any(__A		):
         logger.warning(
             '''Potential NSFW content was detected in one or more images. A black image will be returned instead.'''
             ''' Try again with a different prompt and/or seed.'''		)

     return images, has_nsfw_concepts





 @torch.no_grad()
 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.vision_model(__A		)[1]  # pooled_output
     a											=self.visual_projection(__A		)

     a											=cosine_distance(__A					,       self.special_care_embeds		)
     a											=cosine_distance(__A					,       self.concept_embeds		)

     # increase this value to create a stronger `nsfw` filter
     # at the cost of increasing the possibility of filtering benign images
     a											=0.0

     a											=special_cos_dist - self.special_care_embeds_weights + adjustment
     # special_scores = special_scores.round(decimals=3)
     a											=torch.any(special_scores > 0					,       dim=1		)
     a											=special_care * 0.01
     a											=special_adjustment.unsqueeze(1		).expand(-1					,       cos_dist.shape[1]		)

     a											=(cos_dist - self.concept_embeds_weights) + special_adjustment
     # concept_scores = concept_scores.round(decimals=3)
     a											=torch.any(concept_scores > 0					,       dim=1		)

     a											=0.0  # black image

     return images, has_nsfw_concepts