


"""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


UpperCamelCase						:				List[str]             =      logging.get_logger(__name__)  # pylint: disable=invalid-name







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[int]:







		"""simple docstring"""





		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	):
				A					       = [image]

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

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







def  __snake_case       (   UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





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

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



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     42
						lowerCAmelCase       =     42







						def __init__(							self   :    Optional[int]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Tuple		):
								super().__init__()
								self.register_modules(unet=_lowercase			,			scheduler=_lowercase		)







						@torch.no_grad()
						def __call__(							self   :    Any			,			_lowercase   :    Union[torch.Tensor, PIL.Image.Image]			,			_lowercase   :    Union[torch.Tensor, PIL.Image.Image]			,			_lowercase   :    int = 250			,			_lowercase   :    float = 0.0			,			_lowercase   :    int = 10			,			_lowercase   :    int = 10			,			_lowercase   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,			_lowercase   :    Optional[str] = "pil"			,			_lowercase   :    bool = True			,			):
								A					       = image

								A					       = _preprocess_image(_lowercase		)
								A					       = original_image.to(device=self.device			,			dtype=self.unet.dtype		)
								A					       = _preprocess_mask(_lowercase		)
								A					       = mask_image.to(device=self.device			,			dtype=self.unet.dtype		)

								A					       = original_image.shape[0]

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

								A					       = original_image.shape
								A					       = randn_tensor(_lowercase			,			generator=_lowercase			,			device=self.device			,			dtype=self.unet.dtype		)

								# set step values
								self.scheduler.set_timesteps(_lowercase			,			_lowercase			,			_lowercase			,			self.device		)
								A					       = eta

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

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

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

								if not return_dict:
										return (image,)

								return ImagePipelineOutput(images=_lowercase		)









"""simple docstring"""


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """ClapFeatureExtractor"""
						lowerCAmelCase       =     ("""RobertaTokenizer""", """RobertaTokenizerFast""")







						def __init__(							self   :    Dict			,			_lowercase   :    List[str]			,			_lowercase   :    Optional[int]		):
								super().__init__(_lowercase			,			_lowercase		)







						def __call__(							self   :    Optional[int]			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Dict=None			,			_lowercase   :    str=None			,			**_lowercase   :    Union[str, Any]		):
								A					       = kwargs.pop('sampling_rate'			,			_lowercase		)

								if text is None and audios is None:
										raise ValueError('You have to specify either text or audios. Both cannot be none.'		)

								if text is not None:
										A					       = self.tokenizer(_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if audios is not None:
										A					       = self.feature_extractor(
										    _lowercase			,			sampling_rate=_lowercase			,			return_tensors=_lowercase			,			**_lowercase		)

								if text is not None and audios is not None:
										A					       = audio_features.input_features
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**_lowercase		)			,			tensor_type=_lowercase		)







						def __a						(							self   :    List[Any]			,			*_lowercase   :    Dict			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.batch_decode(*_lowercase			,			**_lowercase		)







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Optional[int]		):
								return self.tokenizer.decode(*_lowercase			,			**_lowercase		)







						@property
						def __a						(							self   :    Tuple		):
								A					       = self.tokenizer.model_input_names
								A					       = self.feature_extractor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names		)		)





