






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 : List[Any]						   =       logging.get_logger(__name__)  # pylint: disable=invalid-name
def       snake_case_		(     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      :							int				=     [image]

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

														__lowercase      :							Dict				=     [np.array(i.resize((w, h)       ,       resample=PIL_INTERPOLATION["""lanczos"""]      )      )[None, :] for i in image]
														__lowercase      :							Any				=     np.concatenate(lowerCAmelCase_       ,       axis=0      )
														__lowercase      :							List[str]				=     np.array(lowerCAmelCase_      ).astype(np.floataa      ) / 255.0
														__lowercase      :							List[str]				=     image.transpose(0       ,       3       ,       1       ,       2      )
														__lowercase      :							Dict				=     2.0 * image - 1.0
														__lowercase      :							Tuple				=     torch.from_numpy(lowerCAmelCase_      )
							elif isinstance(image[0]       ,       torch.Tensor      ):
														__lowercase      :							Tuple				=     torch.cat(lowerCAmelCase_       ,       dim=0      )
							return image
def       snake_case_		(     lowerCAmelCase_       :						Union[List, PIL.Image.Image, torch.Tensor]      ):
							if isinstance(lowerCAmelCase_       ,       torch.Tensor      ):
														return mask
							elif isinstance(lowerCAmelCase_       ,       PIL.Image.Image      ):
														__lowercase      :							List[Any]				=     [mask]

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



class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			UNetaDModel
					_A						:			RePaintScheduler



					def __init__(  self				:	Tuple		,     __a				:	int		,     __a				:	Any							)			->  Optional[int]:


												"""simple docstring"""

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



					@torch.no_grad()
					def __call__(  self				:	str		,     __a				:	Union[torch.Tensor, PIL.Image.Image]		,     __a				:	Union[torch.Tensor, PIL.Image.Image]		,     __a				:	int = 250		,     __a				:	float = 0.0		,     __a				:	int = 10		,     __a				:	int = 10		,     __a				:	Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     __a				:	Optional[str] = "pil"		,     __a				:	bool = True		,     )			->  Union[ImagePipelineOutput, Tuple]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     image

												__lowercase      :							List[Any]				=     _preprocess_image(__a							)
												__lowercase      :							Tuple				=     original_image.to(device=self.device		,     dtype=self.unet.dtype							)
												__lowercase      :							Optional[int]				=     _preprocess_mask(__a							)
												__lowercase      :							List[Any]				=     mask_image.to(device=self.device		,     dtype=self.unet.dtype							)

												__lowercase      :							Dict				=     original_image.shape[0]

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

												__lowercase      :							Any				=     original_image.shape
												__lowercase      :							str				=     randn_tensor(__a		,     generator=__a		,     device=self.device		,     dtype=self.unet.dtype							)

												# set step values
												self.scheduler.set_timesteps(__a		,     __a		,     __a		,     self.device							)
												__lowercase      :							Any				=     eta

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

																			else:
																										# compute the reverse: x_t-1 -> x_t
																										__lowercase      :							Optional[Any]				=     self.scheduler.undo_step(__a		,     __a		,     __a							)
																			__lowercase      :							Tuple				=     t

												__lowercase      :							Optional[Any]				=     (image / 2 + 0.5).clamp(0		,     1							)
												__lowercase      :							Tuple				=     image.cpu().permute(0		,     2		,     3		,     1							).numpy()
												if output_type == "pil":
																			__lowercase      :							Tuple				=     self.numpy_to_pil(__a							)

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__a							)







from typing import TYPE_CHECKING

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


lowerCamelCase : int						   =       {
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : Union[str, Any]						   =       [
	    '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''FunnelBaseModel''',
	    '''FunnelForMaskedLM''',
	    '''FunnelForMultipleChoice''',
	    '''FunnelForPreTraining''',
	    '''FunnelForQuestionAnswering''',
	    '''FunnelForSequenceClassification''',
	    '''FunnelForTokenClassification''',
	    '''FunnelModel''',
	    '''FunnelPreTrainedModel''',
	    '''load_tf_weights_in_funnel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase : str						   =       [
	    '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFFunnelBaseModel''',
	    '''TFFunnelForMaskedLM''',
	    '''TFFunnelForMultipleChoice''',
	    '''TFFunnelForPreTraining''',
	    '''TFFunnelForQuestionAnswering''',
	    '''TFFunnelForSequenceClassification''',
	    '''TFFunnelForTokenClassification''',
	    '''TFFunnelModel''',
	    '''TFFunnelPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

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