






"""simple docstring"""



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						(				UpperCamelCase       ,		UpperCamelCase		):
       snake_case_     =							'pixel_values'
       snake_case_     =							False
       snake_case_     =							TimmBackboneConfig


       def __init__(			self      :						Union[str, Any]			,    snake_case      :						List[str]			,    **snake_case      :						List[Any]							):
             '''simple docstring'''





             requires_backends(self			,    """timm"""							)
             super().__init__(snake_case							)
             A__						:  Optional[int]		  =  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(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."""							)

             A__						:  Optional[Any]		  =  getattr(snake_case			,    """use_pretrained_backbone"""			,    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.
             A__						:  str		  =  config.out_indices if getattr(snake_case			,    """out_indices"""			,    snake_case							) is not None else (-1,)

             A__						:  int		  =  timm.create_model(
                 config.backbone			,    pretrained=snake_case			,    features_only=config.features_only			,    in_chans=config.num_channels			,    out_indices=snake_case			,    **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.
             A__						:  Optional[int]		  =  self._backbone.return_layers
             A__						:  int		  =  {layer["""module"""]: str(snake_case							) for i, layer in enumerate(self._backbone.feature_info.info							)}
             super()._init_backbone(snake_case							)


       @classmethod
       def 						_UpperCamelCase       (			cls      :						Any			,    snake_case      :						Union[str, Any]			,    *snake_case      :						Tuple			,    **snake_case      :						Optional[int]							):
             '''simple docstring'''





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

             A__						:  Optional[Any]		  =  kwargs.pop("""config"""			,    TimmBackboneConfig()							)

             A__						:  Dict		  =  kwargs.pop("""use_timm_backbone"""			,    snake_case							)
             if not use_timm:
                   raise ValueError("""use_timm_backbone must be True for timm backbones"""							)

             A__						:  Union[str, Any]		  =  kwargs.pop("""num_channels"""			,    config.num_channels							)
             A__						:  Optional[int]		  =  kwargs.pop("""features_only"""			,    config.features_only							)
             A__						:  int		  =  kwargs.pop("""use_pretrained_backbone"""			,    config.use_pretrained_backbone							)
             A__						:  int		  =  kwargs.pop("""out_indices"""			,    config.out_indices							)
             A__						:  List[Any]		  =  TimmBackboneConfig(
                 backbone=snake_case			,    num_channels=snake_case			,    features_only=snake_case			,    use_pretrained_backbone=snake_case			,    out_indices=snake_case			,    )
             return super()._from_config(snake_case			,    **snake_case							)


       def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Union[str, Any]							):
             '''simple docstring'''





             pass


       def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Optional[Any]			,    snake_case      :						Dict=None			,    snake_case      :						str=None			,    snake_case      :						Union[str, Any]=None			,    **snake_case      :						str							):
             '''simple docstring'''





             A__						:  Dict		  =  return_dict if return_dict is not None else self.config.use_return_dict
             A__						:  Optional[int]		  =  (
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             A__						:  List[Any]		  =  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
                   A__						:  int		  =  self._all_layers
                   A__						:  Dict		  =  self._backbone(snake_case			,    **snake_case							)
                   A__						:  int		  =  self._return_layers
                   A__						:  Optional[Any]		  =  tuple(hidden_states[i] for i in self.out_indices							)
             else:
                   A__						:  Optional[int]		  =  self._backbone(snake_case			,    **snake_case							)
                   A__						:  Union[str, Any]		  =  None

             A__						:  int		  =  tuple(snake_case							)
             A__						:  int		  =  tuple(snake_case							) if hidden_states is not None else None

             if not return_dict:
                   A__						:  Optional[Any]		  =  (feature_maps,)
                   if output_hidden_states:
                         A__						:  int		  =  output + (hidden_states,)
                   return output

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














"""simple docstring"""



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		):


							@property
							def 						_UpperCamelCase       (			self      :						Dict							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Optional[Any]		  =  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 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  List[Any]		  =  self.dummy_uncond_unet
													A__						:  Dict		  =  ScoreSdeVeScheduler()

													A__						:  str		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  torch.manual_seed(0							)
													A__						:  Any		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  List[str]		  =  torch.manual_seed(0							)
													A__						:  int		  =  sde_ve(num_inference_steps=2			,    output_type="""numpy"""			,    generator=snake_case			,    return_dict=snake_case							)[
													    0
													]

													A__						:  int		  =  image[0, -3:, -3:, -1]
													A__						:  Optional[Any]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A__						:  Optional[Any]		  =  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		):


							def 						_UpperCamelCase       (			self      :						Tuple							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """google/ncsnpp-church-256"""
													A__						:  Tuple		  =  UNetaDModel.from_pretrained(snake_case							)

													A__						:  int		  =  ScoreSdeVeScheduler.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  ScoreSdeVePipeline(unet=snake_case			,    scheduler=snake_case							)
													sde_ve.to(snake_case							)
													sde_ve.set_progress_bar_config(disable=snake_case							)

													A__						:  Dict		  =  torch.manual_seed(0							)
													A__						:  Optional[int]		  =  sde_ve(num_inference_steps=10			,    output_type="""numpy"""			,    generator=snake_case							).images

													A__						:  Optional[int]		  =  image[0, -3:, -3:, -1]

													assert image.shape == (1, 256, 256, 3)

													A__						:  Dict		  =  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






