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




def 							a		(				snake_case__:   Union[List, PIL.Image.Image, torch.Tensor]   ):


				'''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   ):
								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 							a		(				snake_case__:   Union[List, PIL.Image.Image, torch.Tensor]   ):


				'''simple docstring'''





				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 % 32 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    :     Any			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Dict			)			->	str:
					super().__init__()
					self.register_modules(unet=__SCREAMING_SNAKE_CASE			,				scheduler=__SCREAMING_SNAKE_CASE			)




	@torch.no_grad()
	def __call__(			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int = 2_5_0			,				SCREAMING_SNAKE_CASE_    :     List[str] = 0.0			,				SCREAMING_SNAKE_CASE_    :     List[str] = 1_0			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = 1_0			,				SCREAMING_SNAKE_CASE_    :     Any = None			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any] = "pil"			,				SCREAMING_SNAKE_CASE_    :     List[str] = True			,				)			->	Union[ImagePipelineOutput, Tuple]:
					lowercase_	    =   image

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

					# set step values
					self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE			,				self.device			)
					lowercase_	    =   eta

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

									else:
													# compute the reverse: x_t-1 -> x_t
													lowercase_	    =   self.scheduler.undo_step(__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE			,				__SCREAMING_SNAKE_CASE			)
									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(__SCREAMING_SNAKE_CASE			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE			)










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_							:	List[str]	     =		{
    '''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase_							:	int	     =		[
         '''GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''GPTBigCodeForSequenceClassification''',
         '''GPTBigCodeForTokenClassification''',
         '''GPTBigCodeForCausalLM''',
         '''GPTBigCodeModel''',
         '''GPTBigCodePreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_gpt_bigcode import (
              GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTBigCodeForCausalLM,
              GPTBigCodeForSequenceClassification,
              GPTBigCodeForTokenClassification,
              GPTBigCodeModel,
              GPTBigCodePreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase_							:	Any	     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






