
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   =	logging.get_logger(__name__)

lowercase   =	{'''vocab_file''': '''sentencepiece.bpe.model'''}

lowercase   =	{
    '''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   =	{
    '''moussaKam/mbarthez''': 1_0_2_4,
    '''moussaKam/barthez''': 1_0_2_4,
    '''moussaKam/barthez-orangesum-title''': 1_0_2_4,
}

lowercase   =	'''▁'''





class    __A(					UpperCAmelCase							):
					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''']

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

												lowerCamelCase_					=       {} 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 ,       )

												lowerCamelCase_					=       vocab_file
												lowerCamelCase_					=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
												self.sp_model.Load(str(__UpperCamelCase ) )

												lowerCamelCase_					=       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

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

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):

												if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowerCamelCase_					=       [self.cls_token_id]
												lowerCamelCase_					=       [self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep

					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ,       __UpperCamelCase       :  bool = False ):
												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  lowercase__   (						self       :  List[str] ,       __UpperCamelCase       :  List[int] ,       __UpperCamelCase       :  Optional[List[int]] = None ):
												lowerCamelCase_					=       [self.sep_token_id]
												lowerCamelCase_					=       [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  lowercase__   (						self       :  Any ):
												return len(self.sp_model )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       {self.convert_ids_to_tokens(__UpperCamelCase ): i for i in range(self.vocab_size )}
												vocab.update(self.added_tokens_encoder )
												return vocab

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  str ):
												return self.sp_model.encode(__UpperCamelCase ,       out_type=__UpperCamelCase )

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  Optional[int] ):
												if token in self.fairseq_tokens_to_ids:
																			return self.fairseq_tokens_to_ids[token]
												lowerCamelCase_					=       self.sp_model.PieceToId(__UpperCamelCase )

												return spm_id if spm_id else self.unk_token_id

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  List[str] ):
												if index in self.fairseq_ids_to_tokens:
																			return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(__UpperCamelCase )

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  Optional[Any] ):
												lowerCamelCase_					=       []
												lowerCamelCase_					=       """"""
												lowerCamelCase_					=       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(__UpperCamelCase ) + token
																										lowerCamelCase_					=       True
																										lowerCamelCase_					=       []
																			else:
																										current_sub_tokens.append(__UpperCamelCase )
																										lowerCamelCase_					=       False
												out_string += self.sp_model.decode(__UpperCamelCase )
												return out_string.strip()

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

					def __setstate__(						self       :  Optional[Any] ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       d

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

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



					def  lowercase__   (						self       :  Tuple ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Optional[str] = None ):
												if not os.path.isdir(__UpperCamelCase ):
																			logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																			return
												lowerCamelCase_					=       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:
																										lowerCamelCase_					=       self.sp_model.serialized_model_proto()
																										fi.write(__UpperCamelCase )

												return (out_vocab_file,)







import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase   =	logging.get_logger(__name__)  # pylint: disable=invalid-name



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							warnings.warn(
							    """The preprocess method is deprecated and will be removed in a future version. Please"""
							    """ use VaeImageProcessor.preprocess instead"""      ,					UpperCAmelCase__      ,					)
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return image
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [image]

							if isinstance(image[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       image[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														lowerCamelCase_					=       [np.array(i.resize((w, h)      ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       np.array(UpperCAmelCase__							).astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       image.transpose(0      ,					3      ,					1      ,					2							)
														lowerCamelCase_					=       2.0 * image - 1.0
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(image[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



					@torch.no_grad()
					def __call__(						self       :  List[str] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  int = 2_5_0 ,       __UpperCamelCase       :  float = 0.0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       __UpperCamelCase       :  Optional[str] = "pil" ,       __UpperCamelCase       :  bool = True ,       ):
												lowerCamelCase_					=       image

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       original_image.shape[0]

												# sample gaussian noise to begin the loop
												if isinstance(__UpperCamelCase ,       __UpperCamelCase ) and len(__UpperCamelCase ) != batch_size:
																			raise ValueError(
																			    F'''You have passed a list of generators of length {len(__UpperCamelCase )}, but requested an effective batch'''
																			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

												lowerCamelCase_					=       self.scheduler.timesteps[0] + 1
												lowerCamelCase_					=       generator[0] if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else generator
												for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
																			if t < t_last:
																										# predict the noise residual
																										lowerCamelCase_					=       self.unet(__UpperCamelCase ,       __UpperCamelCase ).sample
																										# compute previous image: x_t -> x_t-1
																										lowerCamelCase_					=       self.scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ).prev_sample

																			else:
																										# compute the reverse: x_t-1 -> x_t
																										lowerCamelCase_					=       self.scheduler.undo_step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       t

												lowerCamelCase_					=       (image / 2 + 0.5).clamp(0 ,       1 )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 ).numpy()
												if output_type == "pil":
																			lowerCamelCase_					=       self.numpy_to_pil(__UpperCamelCase )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





