





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_snake_case  :   str						   =  {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case  :   Union[str, Any]						   =  {
    'vocab_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'
        ),
        'google/electra-base-generator': 'https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt',
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'google/electra-small-generator': (
            'https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-generator': (
            'https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-generator': (
            'https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'
        ),
        'google/electra-small-discriminator': (
            'https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-base-discriminator': (
            'https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'
        ),
        'google/electra-large-discriminator': (
            'https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case  :   Any						   =  {
    'google/electra-small-generator': 512,
    'google/electra-base-generator': 512,
    'google/electra-large-generator': 512,
    'google/electra-small-discriminator': 512,
    'google/electra-base-discriminator': 512,
    'google/electra-large-discriminator': 512,
}

_snake_case  :   Optional[Any]						   =  {
    'google/electra-small-generator': {'do_lower_case': True},
    'google/electra-base-generator': {'do_lower_case': True},
    'google/electra-large-generator': {'do_lower_case': True},
    'google/electra-small-discriminator': {'do_lower_case': True},
    'google/electra-base-discriminator': {'do_lower_case': True},
    'google/electra-large-discriminator': {'do_lower_case': True},
}



class     _UpperCAmelCase							(   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			VOCAB_FILES_NAMES
  a_      =			PRETRAINED_VOCAB_FILES_MAP
  a_      =			PRETRAINED_INIT_CONFIGURATION
  a_      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_      =			ElectraTokenizer






  def __init__(    self			:		List[Any] ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		int=None ,  lowerCAmelCase_			:		List[str]=True ,  lowerCAmelCase_			:		Tuple="[UNK]" ,  lowerCAmelCase_			:		int="[SEP]" ,  lowerCAmelCase_			:		Optional[Any]="[PAD]" ,  lowerCAmelCase_			:		Dict="[CLS]" ,  lowerCAmelCase_			:		Optional[Any]="[MASK]" ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		List[Any]=None ,  **lowerCAmelCase_			:		List[Any] ,  )							->						Dict:
     super().__init__(
         lowerCAmelCase_ ,  tokenizer_file=lowerCAmelCase_ ,  do_lower_case=lowerCAmelCase_ ,  unk_token=lowerCAmelCase_ ,  sep_token=lowerCAmelCase_ ,  pad_token=lowerCAmelCase_ ,  cls_token=lowerCAmelCase_ ,  mask_token=lowerCAmelCase_ ,  tokenize_chinese_chars=lowerCAmelCase_ ,  strip_accents=lowerCAmelCase_ ,  **lowerCAmelCase_ ,  )

     __lowerCAmelCase							     =     json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
     if (
         normalizer_state.get('lowercase' ,  lowerCAmelCase_					) != do_lower_case
         or normalizer_state.get('strip_accents' ,  lowerCAmelCase_					) != strip_accents
         or normalizer_state.get('handle_chinese_chars' ,  lowerCAmelCase_					) != tokenize_chinese_chars
     ):
        __lowerCAmelCase							     =     getattr(lowerCAmelCase_ ,  normalizer_state.pop('type'					)					)
        __lowerCAmelCase							     =     do_lower_case
        __lowerCAmelCase							     =     strip_accents
        __lowerCAmelCase							     =     tokenize_chinese_chars
        __lowerCAmelCase							     =     normalizer_class(**lowerCAmelCase_					)

     __lowerCAmelCase							     =     do_lower_case






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Any ,  lowerCAmelCase_			:		int=None					)							->						Dict:
     __lowerCAmelCase							     =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

     return output






  def     lowercase		(    self			:		str ,  lowerCAmelCase_			:		List[int] ,  lowerCAmelCase_			:		Optional[List[int]] = None					)							->						List[int]:
     __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					) * [0] + len(token_ids_a + sep					) * [1]






  def     lowercase		(    self			:		int ,  lowerCAmelCase_			:		str ,  lowerCAmelCase_			:		Optional[str] = None					)							->						Tuple[str]:
     __lowerCAmelCase							     =     self._tokenizer.model.save(lowerCAmelCase_ ,  name=lowerCAmelCase_					)
     return tuple(lowerCAmelCase_					)








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


_snake_case  :   Union[str, Any]						   =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   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     ):
      __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(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     np.array(lowerCAmelCase_     ).astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     image.transpose(0,     3,     1,     2     )
      __lowerCAmelCase							     =     2.0 * image - 1.0
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(image[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return image

def   a_   (			lowerCAmelCase_				:      Union[List, PIL.Image.Image, torch.Tensor]     ):
   if isinstance(lowerCAmelCase_,     torch.Tensor     ):
      return mask
   elif isinstance(lowerCAmelCase_,     PIL.Image.Image     ):
      __lowerCAmelCase							     =     [mask]

   if isinstance(mask[0],     PIL.Image.Image     ):
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     mask[0].size
      __lowerCAmelCase     ,     __lowerCAmelCase							     =     (x - x % 32 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(lowerCAmelCase_,     axis=0     )
      __lowerCAmelCase							     =     mask.astype(np.floataa     ) / 255.0
      __lowerCAmelCase							     =     0
      __lowerCAmelCase							     =     1
      __lowerCAmelCase							     =     torch.from_numpy(lowerCAmelCase_     )
   elif isinstance(mask[0],     torch.Tensor     ):
      __lowerCAmelCase							     =     torch.cat(lowerCAmelCase_,     dim=0     )
   return mask




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			42
  a_      =			42






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






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

     __lowerCAmelCase							     =     _preprocess_image(lowerCAmelCase_					)
     __lowerCAmelCase							     =     original_image.to(device=self.device ,  dtype=self.unet.dtype					)
     __lowerCAmelCase							     =     _preprocess_mask(lowerCAmelCase_					)
     __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(lowerCAmelCase_ ,  lowerCAmelCase_					) and len(lowerCAmelCase_					) != batch_size:
        raise ValueError(
            f"""You have passed a list of generators of length {len(lowerCAmelCase_					)}, 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(lowerCAmelCase_ ,  generator=lowerCAmelCase_ ,  device=self.device ,  dtype=self.unet.dtype					)

     # set step values
     self.scheduler.set_timesteps(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_ ,  self.device					)
     __lowerCAmelCase							     =     eta

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

        else:
           # compute the reverse: x_t-1 -> x_t
           __lowerCAmelCase							     =     self.scheduler.undo_step(lowerCAmelCase_ ,  lowerCAmelCase_ ,  lowerCAmelCase_					)
        __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(lowerCAmelCase_					)

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=lowerCAmelCase_					)

