






'''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      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    Any			  =    'pixel_values'
 __a      :    str			  =    False
 __a      :    Optional[Any]			  =    TimmBackboneConfig
 def __init__(				self      ,  __lowerCAmelCase      ,  **__lowerCAmelCase			):
       requires_backends(self      ,  """timm"""			)
       super().__init__(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				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."""			)

       UpperCamelCase_					: Optional[int]            =				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.
       UpperCamelCase_					: Optional[Any]            =				config.out_indices if getattr(__lowerCAmelCase      ,  """out_indices"""      ,  __lowerCAmelCase			) is not None else (-1,)

       UpperCamelCase_					: Any            =				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.
       UpperCamelCase_					: List[Any]            =				self._backbone.return_layers
       UpperCamelCase_					: Optional[int]            =				{layer["module"]: str(__lowerCAmelCase			) for i, layer in enumerate(self._backbone.feature_info.info			)}
       super()._init_backbone(__lowerCAmelCase			)
 @classmethod
 def 	_UpperCAmelCase							(				cls      ,  __lowerCAmelCase      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase			):
       requires_backends(cls      ,  ["""vision""", """timm"""]			)
       from ...models.timm_backbone import TimmBackboneConfig

       UpperCamelCase_					: List[str]            =				kwargs.pop("""config"""      ,  TimmBackboneConfig()			)

       UpperCamelCase_					: Optional[Any]            =				kwargs.pop("""use_timm_backbone"""      ,  __lowerCAmelCase			)
       if not use_timm:
             raise ValueError("""use_timm_backbone must be True for timm backbones"""			)

       UpperCamelCase_					: int            =				kwargs.pop("""num_channels"""      ,  config.num_channels			)
       UpperCamelCase_					: List[str]            =				kwargs.pop("""features_only"""      ,  config.features_only			)
       UpperCamelCase_					: Optional[int]            =				kwargs.pop("""use_pretrained_backbone"""      ,  config.use_pretrained_backbone			)
       UpperCamelCase_					: Optional[Any]            =				kwargs.pop("""out_indices"""      ,  config.out_indices			)
       UpperCamelCase_					: int            =				TimmBackboneConfig(
           backbone=__lowerCAmelCase      ,  num_channels=__lowerCAmelCase      ,  features_only=__lowerCAmelCase      ,  use_pretrained_backbone=__lowerCAmelCase      ,  out_indices=__lowerCAmelCase      ,  )
       return super()._from_config(__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       pass





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

       UpperCamelCase_					: str            =				tuple(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				tuple(__lowerCAmelCase			) if hidden_states is not None else None

       if not return_dict:
             UpperCamelCase_					: Any            =				(feature_maps,)
             if output_hidden_states:
                   UpperCamelCase_					: List[Any]            =				output + (hidden_states,)
             return output

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














'''simple docstring'''
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      ):




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return (
           "CUDAExecutionProvider",
           {
               "gpu_mem_limit": "15000000000",  # 15GB
               "arena_extend_strategy": "kSameAsRequested",
           },
       )




    @property
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  ort.SessionOptions()
       lowercase_						:     Optional[int]							     =  False
       return options





    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo.png"      )
       lowercase_						:     Tuple							     =  load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"      )
       lowercase_						:     List[str]							     =  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
       lowercase_						:     List[Any]							     =  OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
           "CompVis/stable-diffusion-v1-4"	,  revision="onnx"	,  safety_checker=a	,  feature_extractor=a	,  provider=self.gpu_provider	,  sess_options=self.gpu_options	,  )
       pipe.set_progress_bar_config(disable=a      )

       lowercase_						:     List[str]							     =  "A red cat sitting on a park bench"

       lowercase_						:     int							     =  np.random.RandomState(0      )
       lowercase_						:     int							     =  pipe(
           prompt=a	,  image=a	,  mask_image=a	,  strength=0.75	,  guidance_scale=7.5	,  num_inference_steps=1_5	,  generator=a	,  output_type="np"	,  )

       lowercase_						:     Tuple							     =  output.images[0]

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




