





'''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


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




def 				_lowerCAmelCase  (			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			):
						__lowercase			  =							[image]

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

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Union[List, PIL.Image.Image, torch.Tensor]			):
			if isinstance(lowerCamelCase_						,    torch.Tensor			):
						return mask
			elif isinstance(lowerCamelCase_						,    PIL.Image.Image			):
						__lowercase			  =							[mask]

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






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			UNetaDModel
	a      :			RePaintScheduler


	def __init__(self       ,_lowerCamelCase       ,_lowerCamelCase	):







				'''simple docstring'''





				super().__init__()
				self.register_modules(unet=_lowerCamelCase       ,scheduler=_lowerCamelCase	)


	@torch.no_grad()
	def __call__(self       ,_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase = 250       ,_lowerCamelCase = 0.0       ,_lowerCamelCase = 10       ,_lowerCamelCase = 10       ,_lowerCamelCase = None       ,_lowerCamelCase = "pil"       ,_lowerCamelCase = True       ,):







				'''simple docstring'''





				__lowercase			  =							image

				__lowercase			  =							_preprocess_image(_lowerCamelCase	)
				__lowercase			  =							original_image.to(device=self.device       ,dtype=self.unet.dtype	)
				__lowercase			  =							_preprocess_mask(_lowerCamelCase	)
				__lowercase			  =							mask_image.to(device=self.device       ,dtype=self.unet.dtype	)

				__lowercase			  =							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."	)

				__lowercase			  =							original_image.shape
				__lowercase			  =							randn_tensor(_lowerCamelCase       ,generator=_lowerCamelCase       ,device=self.device       ,dtype=self.unet.dtype	)

				# set step values
				self.scheduler.set_timesteps(_lowerCamelCase       ,_lowerCamelCase       ,_lowerCamelCase       ,self.device	)
				__lowercase			  =							eta

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

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

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

				if not return_dict:
							return (image,)

				return ImagePipelineOutput(images=_lowerCamelCase	)












'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

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


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

_SCREAMING_SNAKE_CASE   =      {
    '''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'''
        ),
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/electra-small-generator''': 5_1_2,
    '''google/electra-base-generator''': 5_1_2,
    '''google/electra-large-generator''': 5_1_2,
    '''google/electra-small-discriminator''': 5_1_2,
    '''google/electra-base-discriminator''': 5_1_2,
    '''google/electra-large-discriminator''': 5_1_2,
}

_SCREAMING_SNAKE_CASE   =      {
    '''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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			str     =    VOCAB_FILES_NAMES
	a      :			Dict     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			int     =    PRETRAINED_INIT_CONFIGURATION
	a      :			Tuple     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			List[Any]     =    ElectraTokenizer


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase="[UNK]"       ,_lowerCamelCase="[SEP]"       ,_lowerCamelCase="[PAD]"       ,_lowerCamelCase="[CLS]"       ,_lowerCamelCase="[MASK]"       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							List[Any]:







				'''simple docstring'''





				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       ,)

				__lowercase			  =							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
				):
							__lowercase			  =							getattr(_lowerCamelCase       ,normalizer_state.pop('''type'''	)	)
							__lowercase			  =							do_lower_case
							__lowercase			  =							strip_accents
							__lowercase			  =							tokenize_chinese_chars
							__lowercase			  =							normalizer_class(**_lowerCamelCase	)

				__lowercase			  =							do_lower_case


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							[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   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__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	) * [0] + len(token_ids_a + sep	) * [1]


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)





