

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  (		__snake_case							,						__snake_case							):






			"""simple docstring"""




			__A						  =  """pixel_values"""
			__A						  =  False
			__A						  =  TimmBackboneConfig


			def __init__(    self ,				__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


				requires_backends(self ,				'timm'							)
				super().__init__(__UpperCamelCase							)
				snake_case_      		= 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(__UpperCamelCase ,				'out_features'							) and config.out_features is not None:
					raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'							)

				snake_case_      		= getattr(__UpperCamelCase ,				'use_pretrained_backbone' ,				__UpperCamelCase							)
				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.
				snake_case_      		= config.out_indices if getattr(__UpperCamelCase ,				'out_indices' ,				__UpperCamelCase							) is not None else (-1,)

				snake_case_      		= timm.create_model(
				    config.backbone ,				pretrained=__UpperCamelCase ,				features_only=config.features_only ,				in_chans=config.num_channels ,				out_indices=__UpperCamelCase ,				**__UpperCamelCase ,				)
				# 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.
				snake_case_      		= self._backbone.return_layers
				snake_case_      		= {layer['module']: str(__UpperCamelCase							) for i, layer in enumerate(self._backbone.feature_info.info							)}
				super()._init_backbone(__UpperCamelCase							)


			@classmethod
			def 			__lowerCAmelCase (    cls ,				__UpperCamelCase ,				*__UpperCamelCase ,				**__UpperCamelCase							):

				"""simple docstring"""


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

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

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

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				pass







			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				__UpperCamelCase=None ,				**__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= return_dict if return_dict is not None else self.config.use_return_dict
				snake_case_      		= (
				    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
				)
				snake_case_      		= 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
					snake_case_      		= self._all_layers
					snake_case_      		= self._backbone(__UpperCamelCase ,				**__UpperCamelCase							)
					snake_case_      		= self._return_layers
					snake_case_      		= tuple(hidden_states[i] for i in self.out_indices							)
				else:
					snake_case_      		= self._backbone(__UpperCamelCase ,				**__UpperCamelCase							)
					snake_case_      		= None

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

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

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



import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()


class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				torch.manual_seed(0							)
				snake_case_      		= UNetaDModel(
				    block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=3 ,				out_channels=3 ,				down_block_types=('DownBlock2D', 'AttnDownBlock2D') ,				up_block_types=('AttnUpBlock2D', 'UpBlock2D') ,				)
				return model







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.dummy_uncond_unet
				snake_case_      		= ScoreSdeVeScheduler()

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=2 ,				output_type='numpy' ,				generator=__UpperCamelCase ,				return_dict=__UpperCamelCase							)[
				    0
				]

				snake_case_      		= image[0, -3:, -3:, -1]
				snake_case_      		= image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				snake_case_      		= np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2







@slow
@require_torch
class SCREAMING_SNAKE_CASE  (		unittest.TestCase							):






			"""simple docstring"""




			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= 'google/ncsnpp-church-256'
				snake_case_      		= UNetaDModel.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVeScheduler.from_pretrained(__UpperCamelCase							)

				snake_case_      		= ScoreSdeVePipeline(unet=__UpperCamelCase ,				scheduler=__UpperCamelCase							)
				sde_ve.to(__UpperCamelCase							)
				sde_ve.set_progress_bar_config(disable=__UpperCamelCase							)

				snake_case_      		= torch.manual_seed(0							)
				snake_case_      		= sde_ve(num_inference_steps=10 ,				output_type='numpy' ,				generator=__UpperCamelCase							).images

				snake_case_      		= image[0, -3:, -3:, -1]

				assert image.shape == (1, 2_56, 2_56, 3)

				snake_case_      		= np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
