

'''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


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


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[List, PIL.Image.Image, torch.Tensor]				)     ->					List[str]:


	"""simple docstring"""





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

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

		UpperCAmelCase_     :      int						=  [np.array(i.resize((w, h)	,				resample=PIL_INTERPOLATION["lanczos"]				)				)[None, :] for i in image]
		UpperCAmelCase_     :      Any						=  np.concatenate(_SCREAMING_SNAKE_CASE	,				axis=0				)
		UpperCAmelCase_     :      Dict						=  np.array(_SCREAMING_SNAKE_CASE				).astype(np.floataa				) / 255.0
		UpperCAmelCase_     :      Any						=  image.transpose(0	,				3	,				1	,				2				)
		UpperCAmelCase_     :      List[str]						=  2.0 * image - 1.0
		UpperCAmelCase_     :      List[str]						=  torch.from_numpy(_SCREAMING_SNAKE_CASE				)
	elif isinstance(image[0]	,				torch.Tensor				):
		UpperCAmelCase_     :      List[str]						=  torch.cat(_SCREAMING_SNAKE_CASE	,				dim=0				)
	return image


def  a__       (    _SCREAMING_SNAKE_CASE   :       Union[List, PIL.Image.Image, torch.Tensor]				)     ->					Union[str, Any]:


	"""simple docstring"""





	if isinstance(_SCREAMING_SNAKE_CASE	,				torch.Tensor				):
		return mask
	elif isinstance(_SCREAMING_SNAKE_CASE	,				PIL.Image.Image				):
		UpperCAmelCase_     :      Optional[int]						=  [mask]

	if isinstance(mask[0]	,				PIL.Image.Image				):
		UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  mask[0].size
		UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		UpperCAmelCase_     :      Tuple						=  [np.array(m.convert("L"				).resize((w, h)	,				resample=PIL_INTERPOLATION["nearest"]				)				)[None, :] for m in mask]
		UpperCAmelCase_     :      Union[str, Any]						=  np.concatenate(_SCREAMING_SNAKE_CASE	,				axis=0				)
		UpperCAmelCase_     :      List[str]						=  mask.astype(np.floataa				) / 255.0
		UpperCAmelCase_     :      Any						=  0
		UpperCAmelCase_     :      Optional[Any]						=  1
		UpperCAmelCase_     :      Any						=  torch.from_numpy(_SCREAMING_SNAKE_CASE				)
	elif isinstance(mask[0]	,				torch.Tensor				):
		UpperCAmelCase_     :      Tuple						=  torch.cat(_SCREAMING_SNAKE_CASE	,				dim=0				)
	return mask





class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	UNetaDModel
			__A  :	RePaintScheduler






			def __init__(  self			,_snake_case			,_snake_case     ):
				super().__init__()
				self.register_modules(unet=_snake_case			,scheduler=_snake_case     )






			@torch.no_grad()
			def __call__(  self			,_snake_case			,_snake_case			,_snake_case = 2_50			,_snake_case = 0.0			,_snake_case = 10			,_snake_case = 10			,_snake_case = None			,_snake_case = "pil"			,_snake_case = True			,):
				UpperCAmelCase_     :      List[str]						=  image

				UpperCAmelCase_     :      Tuple						=  _preprocess_image(_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  original_image.to(device=self.device			,dtype=self.unet.dtype     )
				UpperCAmelCase_     :      List[str]						=  _preprocess_mask(_snake_case     )
				UpperCAmelCase_     :      Dict						=  mask_image.to(device=self.device			,dtype=self.unet.dtype     )

				UpperCAmelCase_     :      Dict						=  original_image.shape[0]

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

				UpperCAmelCase_     :      List[Any]						=  original_image.shape
				UpperCAmelCase_     :      Optional[Any]						=  randn_tensor(_snake_case			,generator=_snake_case			,device=self.device			,dtype=self.unet.dtype     )

				# set step values
				self.scheduler.set_timesteps(_snake_case			,_snake_case			,_snake_case			,self.device     )
				UpperCAmelCase_     :      List[str]						=  eta

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

					else:
						# compute the reverse: x_t-1 -> x_t
						UpperCAmelCase_     :      Optional[int]						=  self.scheduler.undo_step(_snake_case			,_snake_case			,_snake_case     )
					UpperCAmelCase_     :      Dict						=  t

				UpperCAmelCase_     :      Union[str, Any]						=  (image / 2 + 0.5).clamp(0			,1     )
				UpperCAmelCase_     :      Optional[int]						=  image.cpu().permute(0			,2			,3			,1     ).numpy()
				if output_type == "pil":
					UpperCAmelCase_     :      Optional[int]						=  self.numpy_to_pil(_snake_case     )

				if not return_dict:
					return (image,)

				return ImagePipelineOutput(images=_snake_case     )










'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_snake_case : str  				=						{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[str]  				=						['LayoutLMv3TokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Optional[int]  				=						[
				    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LayoutLMv3ForQuestionAnswering',
				    'LayoutLMv3ForSequenceClassification',
				    'LayoutLMv3ForTokenClassification',
				    'LayoutLMv3Model',
				    'LayoutLMv3PreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : Tuple  				=						[
				    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFLayoutLMv3ForQuestionAnswering',
				    'TFLayoutLMv3ForSequenceClassification',
				    'TFLayoutLMv3ForTokenClassification',
				    'TFLayoutLMv3Model',
				    'TFLayoutLMv3PreTrainedModel',
				]

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case : List[Any]  				=						['LayoutLMv3FeatureExtractor']
				_snake_case : Tuple  				=						['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
				from .configuration_layoutlmva import (
				    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    LayoutLMvaConfig,
				    LayoutLMvaOnnxConfig,
				)
				from .processing_layoutlmva import LayoutLMvaProcessor
				from .tokenization_layoutlmva import LayoutLMvaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_layoutlmva import (
								    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LayoutLMvaForQuestionAnswering,
								    LayoutLMvaForSequenceClassification,
								    LayoutLMvaForTokenClassification,
								    LayoutLMvaModel,
								    LayoutLMvaPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_layoutlmva import (
								    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFLayoutLMvaForQuestionAnswering,
								    TFLayoutLMvaForSequenceClassification,
								    TFLayoutLMvaForTokenClassification,
								    TFLayoutLMvaModel,
								    TFLayoutLMvaPreTrainedModel,
								)

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
								from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
				import sys

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

