
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


lowercase__	:Tuple										=					logging.get_logger(__name__)

lowercase__	:Union[str, Any]										=					{"vocab_file": "sentencepiece.bpe.model"}

lowercase__	:List[Any]										=					{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__	:int										=					{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__	:int										=					"▁"







class        lowercase	(      __lowerCAmelCase	):
		lowercase_ : Tuple       =VOCAB_FILES_NAMES
		lowercase_ : Optional[int]       =PRETRAINED_VOCAB_FILES_MAP
		lowercase_ : str       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowercase_ : Union[str, Any]       =["input_ids", "attention_mask"]




		def __init__(       self     ,A__     ,A__="<s>"     ,A__="</s>"     ,A__="</s>"     ,A__="<s>"     ,A__="<unk>"     ,A__="<pad>"     ,A__="<mask>"     ,A__ = None     ,**A__     ,):
							lowercase					=		AddedToken(lowerCamelCase__     ,lstrip=lowerCamelCase__     ,rstrip=lowerCamelCase__) if isinstance(lowerCamelCase__     ,lowerCamelCase__) else mask_token

							lowercase					=		{} if sp_model_kwargs is None else sp_model_kwargs

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

							lowercase					=		vocab_file
							lowercase					=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(str(lowerCamelCase__))

							lowercase					=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

							lowercase					=		len(self.sp_model) - 1
							lowercase					=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}




		def 				A__   (       self     ,A__     ,A__ = None):






							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 				A__   (       self     ,A__     ,A__ = None     ,A__ = False):






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

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




		def 				A__   (       self     ,A__     ,A__ = None):
							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]




		@property
		def 				A__   (       self):
							return len(self.sp_model)




		def 				A__   (       self):
							lowercase					=		{self.convert_ids_to_tokens(lowerCamelCase__): i for i in range(self.vocab_size)}
							vocab.update(self.added_tokens_encoder)
							return vocab




		def 				A__   (       self     ,A__):
							return self.sp_model.encode(lowerCamelCase__     ,out_type=lowerCamelCase__)




		def 				A__   (       self     ,A__):






							if token in self.fairseq_tokens_to_ids:
												return self.fairseq_tokens_to_ids[token]
							lowercase					=		self.sp_model.PieceToId(lowerCamelCase__)

							return spm_id if spm_id else self.unk_token_id




		def 				A__   (       self     ,A__):






							if index in self.fairseq_ids_to_tokens:
												return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(lowerCamelCase__)




		def 				A__   (       self     ,A__):
							lowercase					=		[]
							lowercase					=		''''''
							lowercase					=		False
							for token in tokens:
												# make sure that special tokens are not decoded using sentencepiece model
												if token in self.all_special_tokens:
																	if not prev_is_special:
																						out_string += " "
																	out_string += self.sp_model.decode(lowerCamelCase__) + token
																	lowercase					=		True
																	lowercase					=		[]
												else:
																	current_sub_tokens.append(lowerCamelCase__)
																	lowercase					=		False
							out_string += self.sp_model.decode(lowerCamelCase__)
							return out_string.strip()




		def __getstate__(       self):
							lowercase					=		self.__dict__.copy()
							lowercase					=		None
							return state




		def __setstate__(       self     ,A__):
							lowercase					=		d

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

							lowercase					=		spm.SentencePieceProcessor(**self.sp_model_kwargs)
							self.sp_model.Load(self.vocab_file)





		def 				A__   (       self     ,A__     ,A__ = None):






							if not os.path.isdir(lowerCamelCase__):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory')
												return
							lowercase					=		os.path.join(
							    lowerCamelCase__     ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

							return (out_vocab_file,)






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

from ...utils import logging


lowercase__	:Union[str, Any]										=					logging.get_logger(__name__)







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : Union[str, Any]       =CLIPConfig

							lowercase_ : str       =['''CLIPEncoderLayer''']




							def __init__(       self     ,A__):
												super().__init__(A__)

												lowercase					=		CLIPVisionModelWithProjection(config.vision_config)

												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)
												lowercase					=		nn.Linear(config.vision_config.projection_dim     ,1)





							@torch.no_grad()
							def 				A__   (       self     ,A__     ,A__     ,A__=0.5     ,A__=0.5):
												lowercase					=		self.vision_model(A__)[0]

												lowercase					=		self.p_head(A__)
												lowercase					=		nsfw_detected.flatten()
												lowercase					=		nsfw_detected > p_threshold
												lowercase					=		nsfw_detected.tolist()

												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.''')

												for idx, nsfw_detected_ in enumerate(A__):
																	if nsfw_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												lowercase					=		self.w_head(A__)
												lowercase					=		watermark_detected.flatten()
												lowercase					=		watermark_detected > w_threshold
												lowercase					=		watermark_detected.tolist()

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

												for idx, watermark_detected_ in enumerate(A__):
																	if watermark_detected_:
																						lowercase					=		np.zeros(images[idx].shape)

												return images, nsfw_detected, watermark_detected




