





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     _UpperCAmelCase   (   _UpperCamelCase		,   _UpperCamelCase   ):

  """simple docstring"""


  a_      =			"""pixel_values"""
  a_      =			False
  a_      =			TimmBackboneConfig






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		Optional[Any] ,  **lowerCAmelCase_			:		int					)							->						Union[str, Any]:
     requires_backends(self ,  'timm'					)
     super().__init__(lowerCAmelCase_					)
     __lowerCAmelCase							     =     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(lowerCAmelCase_ ,  'out_features'					) and config.out_features is not None:
        raise ValueError('out_features is not supported by TimmBackbone. Please use out_indices instead.'					)

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

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






  @classmethod
  def     lowercase		(    cls			:		Any ,  lowerCAmelCase_			:		List[Any] ,  *lowerCAmelCase_			:		str ,  **lowerCAmelCase_			:		Optional[int]					)							->						List[Any]:
     requires_backends(cls ,  ['vision', 'timm']					)
     from ...models.timm_backbone import TimmBackboneConfig

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

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

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






  def     lowercase		(    self			:		List[Any] ,  lowerCAmelCase_			:		Union[str, Any]					)							->						Union[str, Any]:
     pass






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int=None ,  lowerCAmelCase_			:		int=None ,  lowerCAmelCase_			:		str=None ,  **lowerCAmelCase_			:		List[str]					)							->						Union[BackboneOutput, Tuple[Tensor, ...]]:
     __lowerCAmelCase							     =     return_dict if return_dict is not None else self.config.use_return_dict
     __lowerCAmelCase							     =     (
         output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
     )
     __lowerCAmelCase							     =     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
        __lowerCAmelCase							     =     self._all_layers
        __lowerCAmelCase							     =     self._backbone(lowerCAmelCase_ ,  **lowerCAmelCase_					)
        __lowerCAmelCase							     =     self._return_layers
        __lowerCAmelCase							     =     tuple(hidden_states[i] for i in self.out_indices					)
     else:
        __lowerCAmelCase							     =     self._backbone(lowerCAmelCase_ ,  **lowerCAmelCase_					)
        __lowerCAmelCase							     =     None

     __lowerCAmelCase							     =     tuple(lowerCAmelCase_					)
     __lowerCAmelCase							     =     tuple(lowerCAmelCase_					) if hidden_states is not None else None

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

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








import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
     import onnxruntime as ort



@nightly
@require_onnxruntime
@require_torch_gpu
class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  @property
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )






  @property
  def     lowercase		(    self			:		List[Any]					)							->						int:
     __lowerCAmelCase							     =     ort.SessionOptions()
     __lowerCAmelCase							     =     False
     return options






  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo.png'					)
     __lowerCAmelCase							     =     load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'					)
     __lowerCAmelCase							     =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'					)

     # using the PNDM scheduler by default
     __lowerCAmelCase							     =     OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
         'CompVis/stable-diffusion-v1-4' ,  revision='onnx' ,  safety_checker=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_ ,  provider=self.gpu_provider ,  sess_options=self.gpu_options ,  )
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     'A red cat sitting on a park bench'

     __lowerCAmelCase							     =     np.random.RandomState(0					)
     __lowerCAmelCase							     =     pipe(
         prompt=lowerCAmelCase_ ,  image=lowerCAmelCase_ ,  mask_image=lowerCAmelCase_ ,  strength=0.75 ,  guidance_scale=7.5 ,  num_inference_steps=1_5 ,  generator=lowerCAmelCase_ ,  output_type='np' ,  )

     __lowerCAmelCase							     =     output.images[0]

     assert image.shape == (5_1_2, 5_1_2, 3)
     assert np.abs(expected_image - image					).max() < 1e-2

