





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__:  List[Any]        =     logging.get_logger(__name__)

A__:  Union[str, Any]        =     {
    'SenseTime/deformable-detr': 'https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json',
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}




class     _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__   						=    """deformable_detr"""
      UpperCamelCase__   						=    {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }






      def __init__(						self:       Optional[Any]     ,  __lowerCamelCase:       Dict=True     ,  __lowerCamelCase:       Dict=None     ,  __lowerCamelCase:       Any=3     ,  __lowerCamelCase:       Tuple=300     ,  __lowerCamelCase:       int=1024     ,  __lowerCamelCase:       List[str]=6     ,  __lowerCamelCase:       str=1024     ,  __lowerCamelCase:       Tuple=8     ,  __lowerCamelCase:       str=6     ,  __lowerCamelCase:       List[str]=1024     ,  __lowerCamelCase:       Tuple=8     ,  __lowerCamelCase:       Dict=0.0     ,  __lowerCamelCase:       Dict=True     ,  __lowerCamelCase:       List[Any]="relu"     ,  __lowerCamelCase:       List[str]=256     ,  __lowerCamelCase:       List[str]=0.1     ,  __lowerCamelCase:       Tuple=0.0     ,  __lowerCamelCase:       Union[str, Any]=0.0     ,  __lowerCamelCase:       int=0.02     ,  __lowerCamelCase:       List[str]=1.0     ,  __lowerCamelCase:       int=True     ,  __lowerCamelCase:       int=False     ,  __lowerCamelCase:       Union[str, Any]="sine"     ,  __lowerCamelCase:       Optional[Any]="resnet50"     ,  __lowerCamelCase:       Dict=True     ,  __lowerCamelCase:       Optional[Any]=False     ,  __lowerCamelCase:       Optional[int]=4     ,  __lowerCamelCase:       List[str]=4     ,  __lowerCamelCase:       List[Any]=4     ,  __lowerCamelCase:       Union[str, Any]=False     ,  __lowerCamelCase:       Optional[Any]=300     ,  __lowerCamelCase:       str=False     ,  __lowerCamelCase:       Any=1     ,  __lowerCamelCase:       str=5     ,  __lowerCamelCase:       Any=2     ,  __lowerCamelCase:       Union[str, Any]=1     ,  __lowerCamelCase:       str=1     ,  __lowerCamelCase:       int=5     ,  __lowerCamelCase:       List[Any]=2     ,  __lowerCamelCase:       Any=0.1     ,  __lowerCamelCase:       List[Any]=0.25     ,  __lowerCamelCase:       Dict=False     ,  **__lowerCamelCase:       List[Any]     ,  ):
          '''simple docstring'''


          if backbone_config is not None and use_timm_backbone:
              raise ValueError("You can\'t specify both `backbone_config` and `use_timm_backbone`." )

          if not use_timm_backbone:
              if backbone_config is None:
                  logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
                  UpperCamelCase__:     int     							=			CONFIG_MAPPING["resnet"](out_features=["stage4"] )
              elif isinstance(_snake_case     ,  _snake_case ):
                  UpperCamelCase__:     int     							=			backbone_config.get("model_type" )
                  UpperCamelCase__:     List[Any]     							=			CONFIG_MAPPING[backbone_model_type]
                  UpperCamelCase__:     str     							=			config_class.from_dict(_snake_case )
          UpperCamelCase__:     int     							=			use_timm_backbone
          UpperCamelCase__:     Optional[int]     							=			backbone_config
          UpperCamelCase__:     List[str]     							=			num_channels
          UpperCamelCase__:     Tuple     							=			num_queries
          UpperCamelCase__:     Optional[int]     							=			max_position_embeddings
          UpperCamelCase__:     Tuple     							=			d_model
          UpperCamelCase__:     Dict     							=			encoder_ffn_dim
          UpperCamelCase__:     Union[str, Any]     							=			encoder_layers
          UpperCamelCase__:     Tuple     							=			encoder_attention_heads
          UpperCamelCase__:     Optional[Any]     							=			decoder_ffn_dim
          UpperCamelCase__:     List[Any]     							=			decoder_layers
          UpperCamelCase__:     Any     							=			decoder_attention_heads
          UpperCamelCase__:     int     							=			dropout
          UpperCamelCase__:     str     							=			attention_dropout
          UpperCamelCase__:     Any     							=			activation_dropout
          UpperCamelCase__:     Dict     							=			activation_function
          UpperCamelCase__:     Union[str, Any]     							=			init_std
          UpperCamelCase__:     Tuple     							=			init_xavier_std
          UpperCamelCase__:     List[Any]     							=			encoder_layerdrop
          UpperCamelCase__:     int     							=			auxiliary_loss
          UpperCamelCase__:     str     							=			position_embedding_type
          UpperCamelCase__:     Dict     							=			backbone
          UpperCamelCase__:     Optional[Any]     							=			use_pretrained_backbone
          UpperCamelCase__:     List[Any]     							=			dilation
          # deformable attributes
          UpperCamelCase__:     List[str]     							=			num_feature_levels
          UpperCamelCase__:     Tuple     							=			encoder_n_points
          UpperCamelCase__:     Tuple     							=			decoder_n_points
          UpperCamelCase__:     str     							=			two_stage
          UpperCamelCase__:     str     							=			two_stage_num_proposals
          UpperCamelCase__:     Optional[Any]     							=			with_box_refine
          if two_stage is True and with_box_refine is False:
              raise ValueError("If two_stage is True, with_box_refine must be True." )
          # Hungarian matcher
          UpperCamelCase__:     List[str]     							=			class_cost
          UpperCamelCase__:     List[Any]     							=			bbox_cost
          UpperCamelCase__:     List[Any]     							=			giou_cost
          # Loss coefficients
          UpperCamelCase__:     int     							=			mask_loss_coefficient
          UpperCamelCase__:     int     							=			dice_loss_coefficient
          UpperCamelCase__:     int     							=			bbox_loss_coefficient
          UpperCamelCase__:     List[str]     							=			giou_loss_coefficient
          UpperCamelCase__:     Optional[int]     							=			eos_coefficient
          UpperCamelCase__:     List[str]     							=			focal_alpha
          UpperCamelCase__:     str     							=			disable_custom_kernels
          super().__init__(is_encoder_decoder=_snake_case     ,  **_snake_case )






      @property
      def        UpperCAmelCase_    (						self:       Optional[int] ):
          '''simple docstring'''


          return self.encoder_attention_heads






      @property
      def        UpperCAmelCase_    (						self:       List[Any] ):
          '''simple docstring'''


          return self.d_model






      def        UpperCAmelCase_    (						self:       Optional[Any] ):
          '''simple docstring'''


          UpperCamelCase__:     Tuple     							=			copy.deepcopy(self.__dict__ )
          if self.backbone_config is not None:
              UpperCamelCase__:     int     							=			self.backbone_config.to_dict()
          UpperCamelCase__:     Optional[Any]     							=			self.__class__.model_type
          return output



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort







class 			__A( a  ,   unittest.TestCase			):
				# FIXME: add fast tests
				pass







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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




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




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union[str, Any]:


								'''simple docstring'''




								__a										=    ort.SessionOptions()
								__a										=    False
								return options




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=10		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
								__a										=    load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
								__a										=    LMSDiscreteScheduler.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     subfolder='''scheduler'''		,     revision='''onnx'''				)
								__a										=    OnnxStableDiffusionInpaintPipeline.from_pretrained(
								    '''runwayml/stable-diffusion-inpainting'''		,     revision='''onnx'''		,     scheduler=_snake_case		,     safety_checker=_snake_case		,     feature_extractor=_snake_case		,     provider=self.gpu_provider		,     sess_options=self.gpu_options		,     )
								pipe.set_progress_bar_config(disable=_snake_case				)

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

								__a										=    np.random.RandomState(0				)
								__a										=    pipe(
								    prompt=_snake_case		,     image=_snake_case		,     mask_image=_snake_case		,     guidance_scale=7.5		,     num_inference_steps=20		,     generator=_snake_case		,     output_type='''np'''		,     )
								__a										=    output.images
								__a										=    images[0, 255:258, 255:258, -1]

								assert images.shape == (1, 512, 512, 3)
								__a										=    np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]				)

								assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-3