
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_xlnet import XLNetTokenizer
else:
		lowercase						:      Tuple									= None


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

lowercase						:      Optional[int]									= {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowercase						:      Dict									= {
    'vocab_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json',
        'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json',
    },
}

lowercase						:      Union[str, Any]									= {
    'xlnet-base-cased': None,
    'xlnet-large-cased': None,
}

lowercase						:      List[Any]									= '▁'

# Segments (not really needed)
lowercase						:      Any									= 0
lowercase						:      Dict									= 1
lowercase						:      int									= 2
lowercase						:      Dict									= 3
lowercase						:      Tuple									= 4




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  VOCAB_FILES_NAMES
	_A			=  PRETRAINED_VOCAB_FILES_MAP
	_A			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A			=  'left'
	_A			=  XLNetTokenizer







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

								super().__init__(
								    vocab_file=a  ,     tokenizer_file=a  ,     do_lower_case=a  ,     remove_space=a  ,     keep_accents=a  ,     bos_token=a  ,     eos_token=a  ,     unk_token=a  ,     sep_token=a  ,     pad_token=a  ,     cls_token=a  ,     mask_token=a  ,     additional_special_tokens=a  ,     **a  ,     )

								__UpperCamelCase			:     int         =      3
								__UpperCamelCase			:     int         =      do_lower_case
								__UpperCamelCase			:     Any         =      remove_space
								__UpperCamelCase			:     List[Any]         =      keep_accents
								__UpperCamelCase			:     Optional[Any]         =      vocab_file
								__UpperCamelCase			:     List[str]         =      False if not self.vocab_file else True







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:List[int]  ,     a	:Optional[List[int]] = None		)	->				List[int]:
								__UpperCamelCase			:     Optional[int]         =      [self.sep_token_id]
								__UpperCamelCase			:     List[Any]         =      [self.cls_token_id]
								if token_ids_a is None:
															return token_ids_a + sep + cls
								return token_ids_a + sep + token_ids_a + sep + cls







	def      _lowerCamelCase       (      self	:List[str]  ,     a	:List[int]  ,     a	:Optional[List[int]] = None		)	->				List[int]:
								__UpperCamelCase			:     List[str]         =      [self.sep_token_id]
								__UpperCamelCase			:     Dict         =      [2]

								if token_ids_a is None:
															return len(token_ids_a + sep		) * [0] + cls_segment_id
								return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id







	def      _lowerCamelCase       (      self	:str  ,     a	:str  ,     a	:Optional[str] = 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(a		):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
															return
								__UpperCamelCase			:     Optional[int]         =      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,)

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Tuple:






							'''simple docstring'''





							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"		,						_lowerCamelCase		,						)
							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return image
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     int         =      [image]

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

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Union[List, PIL.Image.Image, torch.Tensor])      ->					Optional[int]:






							'''simple docstring'''





							if isinstance(_lowerCamelCase		,						torch.Tensor):
														return mask
							elif isinstance(_lowerCamelCase		,						PIL.Image.Image):
														__UpperCamelCase			:     Any         =      [mask]

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




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  42
	_A			=  42







	def __init__(      self	:Optional[Any]  ,     a	:str  ,     a	:List[str]		)	->				Union[str, Any]:
								super().__init__()
								self.register_modules(unet=a  ,     scheduler=a		)







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

								__UpperCamelCase			:     Optional[Any]         =      _preprocess_image(a		)
								__UpperCamelCase			:     Optional[int]         =      original_image.to(device=self.device  ,     dtype=self.unet.dtype		)
								__UpperCamelCase			:     Dict         =      _preprocess_mask(a		)
								__UpperCamelCase			:     Any         =      mask_image.to(device=self.device  ,     dtype=self.unet.dtype		)

								__UpperCamelCase			:     str         =      original_image.shape[0]

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

								__UpperCamelCase			:     List[str]         =      original_image.shape
								__UpperCamelCase			:     List[Any]         =      randn_tensor(a  ,     generator=a  ,     device=self.device  ,     dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(a  ,     a  ,     a  ,     self.device		)
								__UpperCamelCase			:     Tuple         =      eta

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

															else:
																						# compute the reverse: x_t-1 -> x_t
																						__UpperCamelCase			:     List[str]         =      self.scheduler.undo_step(a  ,     a  ,     a		)
															__UpperCamelCase			:     Dict         =      t

								__UpperCamelCase			:     Union[str, Any]         =      (image / 2 + 0.5).clamp(0  ,     1		)
								__UpperCamelCase			:     Tuple         =      image.cpu().permute(0  ,     2  ,     3  ,     1		).numpy()
								if output_type == "pil":
															__UpperCamelCase			:     str         =      self.numpy_to_pil(a		)

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a		)