
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
				import timm


if is_torch_available():
				from torch import Tensor






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'pixel_values'
							_snake_case     		=  False
							_snake_case     		=  TimmBackboneConfig



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													requires_backends(self     ,	'''timm'''			)
													super().__init__(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			config

													if config.backbone is None:
																			raise ValueError('''backbone is not set in the config. Please set it to a timm model name.'''			)

													if config.backbone not in timm.list_models():
																			raise ValueError(F"backbone {config.backbone} is not supported by timm."			)

													if hasattr(SCREAMING_SNAKE_CASE_     ,	'''out_features'''			) and config.out_features is not None:
																			raise ValueError('''out_features is not supported by TimmBackbone. Please use out_indices instead.'''			)

													__UpperCamelCase       =			getattr(SCREAMING_SNAKE_CASE_     ,	'''use_pretrained_backbone'''     ,	SCREAMING_SNAKE_CASE_			)
													if pretrained is None:
																			raise ValueError('''use_pretrained_backbone is not set in the config. Please set it to True or False.'''			)

													# We just take the final layer by default. This matches the default for the transformers models.
													__UpperCamelCase       =			config.out_indices if getattr(SCREAMING_SNAKE_CASE_     ,	'''out_indices'''     ,	SCREAMING_SNAKE_CASE_			) is not None else (-1,)

													__UpperCamelCase       =			timm.create_model(
													    config.backbone     ,	pretrained=SCREAMING_SNAKE_CASE_     ,	features_only=config.features_only     ,	in_chans=config.num_channels     ,	out_indices=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_     ,	)
													# These are used to control the output of the model when called. If output_hidden_states is True, then
													# return_layers is modified to include all layers.
													__UpperCamelCase       =			self._backbone.return_layers
													__UpperCamelCase       =			{layer['''module''']: str(SCREAMING_SNAKE_CASE_			) for i, layer in enumerate(self._backbone.feature_info.info			)}
													super()._init_backbone(SCREAMING_SNAKE_CASE_			)



							@classmethod
							def     A__		(  cls     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													requires_backends(cls     ,	['''vision''', '''timm''']			)
													from ...models.timm_backbone import TimmBackboneConfig

													__UpperCamelCase       =			kwargs.pop('''config'''     ,	TimmBackboneConfig()			)

													__UpperCamelCase       =			kwargs.pop('''use_timm_backbone'''     ,	SCREAMING_SNAKE_CASE_			)
													if not use_timm:
																			raise ValueError('''use_timm_backbone must be True for timm backbones'''			)

													__UpperCamelCase       =			kwargs.pop('''num_channels'''     ,	config.num_channels			)
													__UpperCamelCase       =			kwargs.pop('''features_only'''     ,	config.features_only			)
													__UpperCamelCase       =			kwargs.pop('''use_pretrained_backbone'''     ,	config.use_pretrained_backbone			)
													__UpperCamelCase       =			kwargs.pop('''out_indices'''     ,	config.out_indices			)
													__UpperCamelCase       =			TimmBackboneConfig(
													    backbone=SCREAMING_SNAKE_CASE_     ,	num_channels=SCREAMING_SNAKE_CASE_     ,	features_only=SCREAMING_SNAKE_CASE_     ,	use_pretrained_backbone=SCREAMING_SNAKE_CASE_     ,	out_indices=SCREAMING_SNAKE_CASE_     ,	)
													return super()._from_config(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													pass



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_			)->   Union[BackboneOutput, Tuple[Tensor, ...]]:





													'''simple docstring'''






													__UpperCamelCase       =			return_dict if return_dict is not None else self.config.use_return_dict
													__UpperCamelCase       =			(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													__UpperCamelCase       =			output_attentions if output_attentions is not None else self.config.output_attentions

													if output_attentions:
																			raise ValueError('''Cannot output attentions for timm backbones at the moment'''			)

													if output_hidden_states:
																			# We modify the return layers to include all the stages of the backbone
																			__UpperCamelCase       =			self._all_layers
																			__UpperCamelCase       =			self._backbone(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			self._return_layers
																			__UpperCamelCase       =			tuple(hidden_states[i] for i in self.out_indices			)
													else:
																			__UpperCamelCase       =			self._backbone(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)
																			__UpperCamelCase       =			None

													__UpperCamelCase       =			tuple(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			tuple(SCREAMING_SNAKE_CASE_			) if hidden_states is not None else None

													if not return_dict:
																			__UpperCamelCase       =			(feature_maps,)
																			if output_hidden_states:
																									__UpperCamelCase       =			output + (hidden_states,)
																			return output

													return BackboneOutput(feature_maps=SCREAMING_SNAKE_CASE_     ,	hidden_states=SCREAMING_SNAKE_CASE_     ,	attentions=SCREAMING_SNAKE_CASE_			)






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													super().__init__()

													# make sure scheduler can always be converted to DDIM
													__UpperCamelCase       =			DDIMScheduler.from_config(scheduler.config			)

													self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_			)



							@torch.no_grad()
							def __call__(  self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = 50     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "pil"     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[ImagePipelineOutput, Tuple]:





													'''simple docstring'''






													if isinstance(self.unet.config.sample_size     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			(
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			__UpperCamelCase       =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													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."			)

													__UpperCamelCase       =			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_			)

													for t in self.progress_bar(self.scheduler.timesteps			):
																			# 1. predict noise model_output
																			__UpperCamelCase       =			self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			).sample

																			# 2. predict previous mean of image x_t-1 and add variance depending on eta
																			# eta corresponds to η in paper and should be between [0, 1]
																			# do x_t -> x_t-1
																			__UpperCamelCase       =			self.scheduler.step(
																			    SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	eta=SCREAMING_SNAKE_CASE_     ,	use_clipped_model_output=SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_			).prev_sample

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE_			)




