

"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A_							:			Optional[int]											= logging.get_logger(__name__)


A_							:			Union[str, Any]											= '▁'

A_							:			Optional[int]											= {'vocab_file': 'spiece.model'}

A_							:			int											= {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A_							:			List[str]											= {
    'google/reformer-crime-and-punishment': 524_288,
}





class    lowerCamelCase						(SCREAMING_SNAKE_CASE_			):
   lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
   lowerCamelCase__   :		Tuple							=							PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase__   :		Dict							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase__   :		Tuple							=							['input_ids', 'attention_mask']

   def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						str="</s>"    ,				__UpperCAmelCase				:						int="<unk>"    ,				__UpperCAmelCase				:						int=[]    ,				__UpperCAmelCase				:						Dict = None    ,				**__UpperCAmelCase				:						List[str]    ,				)   ->    Dict:
      SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=UpperCamelCase__    ,				unk_token=UpperCamelCase__    ,				additional_special_tokens=UpperCamelCase__    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**UpperCamelCase__    ,				)

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

   @property
   def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    int:
      return self.sp_model.get_piece_size()

   def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
      SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab

   def __getstate__(					self				:						str       )   ->    str:
      SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
      SCREAMING_SNAKE_CASE__								=      None
      return state

   def __setstate__(					self				:						str    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
      SCREAMING_SNAKE_CASE__								=      d

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

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

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Optional[Any]:
      return self.sp_model.encode(UpperCamelCase__    ,				out_type=UpperCamelCase__       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Optional[int]:
      return self.sp_model.piece_to_id(UpperCamelCase__       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]       )   ->    str:
      if index < self.sp_model.get_piece_size():
         SCREAMING_SNAKE_CASE__								=      self.sp_model.IdToPiece(UpperCamelCase__       )
      return token

   def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Optional[int]       )   ->    Optional[int]:
      SCREAMING_SNAKE_CASE__								=      []
      SCREAMING_SNAKE_CASE__								=      ''''''
      for token in tokens:
         # make sure that special tokens are not decoded using sentencepiece model
         if token in self.all_special_tokens:
            out_string += self.sp_model.decode(UpperCamelCase__       ) + token
            SCREAMING_SNAKE_CASE__								=      []
         else:
            current_sub_tokens.append(UpperCamelCase__       )
      out_string += self.sp_model.decode(UpperCamelCase__       )
      return out_string.strip()





   def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						str = None       )   ->    Union[str, Any]:
      if not os.path.isdir(UpperCamelCase__       ):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
         return
      SCREAMING_SNAKE_CASE__								=      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:
            SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
            fi.write(UpperCamelCase__       )

      return (out_vocab_file,)





"""simple docstring"""




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


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

def 	A       (     snake_case__    ):




			'''simple docstring'''


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

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

						SCREAMING_SNAKE_CASE__								=      [np.array(i.resize((w, h)		,				resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      np.array(snake_case__    ).astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      image.transpose(0		,				3		,				1		,				2    )
						SCREAMING_SNAKE_CASE__								=      2.0 * image - 1.0
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(image[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return image

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if isinstance(snake_case__		,				torch.Tensor    ):
						return mask
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [mask]

			if isinstance(mask[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      mask[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE__								=      [np.array(m.convert("""L"""    ).resize((w, h)		,				resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      mask.astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(mask[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return mask





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		UNetaDModel
			lowerCamelCase__   :		RePaintScheduler

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						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    ,				)   ->    Union[ImagePipelineOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      image

						SCREAMING_SNAKE_CASE__								=      _preprocess_image(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      original_image.to(device=self.device    ,				dtype=self.unet.dtype       )
						SCREAMING_SNAKE_CASE__								=      _preprocess_mask(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      mask_image.to(device=self.device    ,				dtype=self.unet.dtype       )

						SCREAMING_SNAKE_CASE__								=      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."""       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape
						SCREAMING_SNAKE_CASE__								=      randn_tensor(__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				device=self.device    ,				dtype=self.unet.dtype       )

						# set step values
						self.scheduler.set_timesteps(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.device       )
						SCREAMING_SNAKE_CASE__								=      eta

						SCREAMING_SNAKE_CASE__								=      self.scheduler.timesteps[0] + 1
						SCREAMING_SNAKE_CASE__								=      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
												SCREAMING_SNAKE_CASE__								=      self.unet(__UpperCAmelCase    ,				__UpperCAmelCase       ).sample
												# compute previous image: x_t -> x_t-1
												SCREAMING_SNAKE_CASE__								=      self.scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       ).prev_sample

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

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

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase       )


