




import copy

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


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={
    """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  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					"deformable_detr"
				SCREAMING_SNAKE_CASE__					:							Tuple	      =					{
				    "hidden_size": "d_model",
				    "num_attention_heads": "encoder_attention_heads",
				}


				def __init__(							self:   Union[str, Any]		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   Union[str, Any]=None		,					_lowerCamelCase:   str=3		,					_lowerCamelCase:   Optional[Any]=3_00		,					_lowerCamelCase:   Dict=10_24		,					_lowerCamelCase:   List[Any]=6		,					_lowerCamelCase:   Tuple=10_24		,					_lowerCamelCase:   Union[str, Any]=8		,					_lowerCamelCase:   Union[str, Any]=6		,					_lowerCamelCase:   List[str]=10_24		,					_lowerCamelCase:   Optional[Any]=8		,					_lowerCamelCase:   Any=0.0		,					_lowerCamelCase:   Union[str, Any]=True		,					_lowerCamelCase:   Optional[int]="relu"		,					_lowerCamelCase:   str=2_56		,					_lowerCamelCase:   str=0.1		,					_lowerCamelCase:   Union[str, Any]=0.0		,					_lowerCamelCase:   Optional[Any]=0.0		,					_lowerCamelCase:   Union[str, Any]=0.02		,					_lowerCamelCase:   Optional[Any]=1.0		,					_lowerCamelCase:   Any=True		,					_lowerCamelCase:   Any=False		,					_lowerCamelCase:   List[str]="sine"		,					_lowerCamelCase:   Any="resnet50"		,					_lowerCamelCase:   List[Any]=True		,					_lowerCamelCase:   Dict=False		,					_lowerCamelCase:   Optional[int]=4		,					_lowerCamelCase:   Dict=4		,					_lowerCamelCase:   List[str]=4		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   Dict=3_00		,					_lowerCamelCase:   Optional[int]=False		,					_lowerCamelCase:   Tuple=1		,					_lowerCamelCase:   Optional[Any]=5		,					_lowerCamelCase:   Union[str, Any]=2		,					_lowerCamelCase:   int=1		,					_lowerCamelCase:   List[Any]=1		,					_lowerCamelCase:   Optional[int]=5		,					_lowerCamelCase:   int=2		,					_lowerCamelCase:   int=0.1		,					_lowerCamelCase:   Tuple=0.25		,					_lowerCamelCase:   List[str]=False		,					**_lowerCamelCase:   Tuple		,					):
											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.'''		)
																									SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']		)
																		elif isinstance(_lowerCamelCase		,					_lowerCamelCase		):
																									SCREAMING_SNAKE_CASE_   	=    backbone_config.get('''model_type'''		)
																									SCREAMING_SNAKE_CASE_   	=    CONFIG_MAPPING[backbone_model_type]
																									SCREAMING_SNAKE_CASE_   	=    config_class.from_dict(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    use_timm_backbone
											SCREAMING_SNAKE_CASE_   	=    backbone_config
											SCREAMING_SNAKE_CASE_   	=    num_channels
											SCREAMING_SNAKE_CASE_   	=    num_queries
											SCREAMING_SNAKE_CASE_   	=    max_position_embeddings
											SCREAMING_SNAKE_CASE_   	=    d_model
											SCREAMING_SNAKE_CASE_   	=    encoder_ffn_dim
											SCREAMING_SNAKE_CASE_   	=    encoder_layers
											SCREAMING_SNAKE_CASE_   	=    encoder_attention_heads
											SCREAMING_SNAKE_CASE_   	=    decoder_ffn_dim
											SCREAMING_SNAKE_CASE_   	=    decoder_layers
											SCREAMING_SNAKE_CASE_   	=    decoder_attention_heads
											SCREAMING_SNAKE_CASE_   	=    dropout
											SCREAMING_SNAKE_CASE_   	=    attention_dropout
											SCREAMING_SNAKE_CASE_   	=    activation_dropout
											SCREAMING_SNAKE_CASE_   	=    activation_function
											SCREAMING_SNAKE_CASE_   	=    init_std
											SCREAMING_SNAKE_CASE_   	=    init_xavier_std
											SCREAMING_SNAKE_CASE_   	=    encoder_layerdrop
											SCREAMING_SNAKE_CASE_   	=    auxiliary_loss
											SCREAMING_SNAKE_CASE_   	=    position_embedding_type
											SCREAMING_SNAKE_CASE_   	=    backbone
											SCREAMING_SNAKE_CASE_   	=    use_pretrained_backbone
											SCREAMING_SNAKE_CASE_   	=    dilation
											# deformable attributes
											SCREAMING_SNAKE_CASE_   	=    num_feature_levels
											SCREAMING_SNAKE_CASE_   	=    encoder_n_points
											SCREAMING_SNAKE_CASE_   	=    decoder_n_points
											SCREAMING_SNAKE_CASE_   	=    two_stage
											SCREAMING_SNAKE_CASE_   	=    two_stage_num_proposals
											SCREAMING_SNAKE_CASE_   	=    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
											SCREAMING_SNAKE_CASE_   	=    class_cost
											SCREAMING_SNAKE_CASE_   	=    bbox_cost
											SCREAMING_SNAKE_CASE_   	=    giou_cost
											# Loss coefficients
											SCREAMING_SNAKE_CASE_   	=    mask_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    dice_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    bbox_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    giou_loss_coefficient
											SCREAMING_SNAKE_CASE_   	=    eos_coefficient
											SCREAMING_SNAKE_CASE_   	=    focal_alpha
											SCREAMING_SNAKE_CASE_   	=    disable_custom_kernels
											super().__init__(is_encoder_decoder=_lowerCamelCase		,					**_lowerCamelCase		)


				@property
				def     _A							(							self:   List[Any]		):
											return self.encoder_attention_heads


				@property
				def     _A							(							self:   int		):
											return self.d_model





				def     _A							(							self:   int		):
											SCREAMING_SNAKE_CASE_   	=    copy.deepcopy(self.__dict__		)
											if self.backbone_config is not None:
																		SCREAMING_SNAKE_CASE_   	=    self.backbone_config.to_dict()
											SCREAMING_SNAKE_CASE_   	=    self.__class__.model_type
											return output








import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class  __magic_name__						(  __UpperCAmelCase    ,  unittest.TestCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							List[str]	      =					ShapEImgaImgPipeline
				SCREAMING_SNAKE_CASE__					:							Dict	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					["image"]
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					[
				    "num_images_per_prompt",
				    "num_inference_steps",
				    "generator",
				    "latents",
				    "guidance_scale",
				    "frame_size",
				    "output_type",
				    "return_dict",
				]
				SCREAMING_SNAKE_CASE__					:							Optional[int]	      =					False


				@property
				def     _A							(							self:   Optional[Any]		):
											return 32


				@property
				def     _A							(							self:   Optional[int]		):
											return 32


				@property
				def     _A							(							self:   List[Any]		):
											return self.time_input_dim * 4


				@property
				def     _A							(							self:   Any		):
											return 8


				@property
				def     _A							(							self:   int		):
											torch.manual_seed(0		)
											SCREAMING_SNAKE_CASE_   	=    CLIPVisionConfig(
											    hidden_size=self.text_embedder_hidden_size		,					image_size=64		,					projection_dim=self.text_embedder_hidden_size		,					intermediate_size=37		,					num_attention_heads=4		,					num_channels=3		,					num_hidden_layers=5		,					patch_size=1		,					)

											SCREAMING_SNAKE_CASE_   	=    CLIPVisionModel(_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											SCREAMING_SNAKE_CASE_   	=    CLIPImageProcessor(
											    crop_size=2_24		,					do_center_crop=_lowerCamelCase		,					do_normalize=_lowerCamelCase		,					do_resize=_lowerCamelCase		,					image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]		,					image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]		,					resample=3		,					size=2_24		,					)

											return image_processor


				@property
				def     _A							(							self:   Dict		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''num_attention_heads''': 2,
											    '''attention_head_dim''': 16,
											    '''embedding_dim''': self.time_input_dim,
											    '''num_embeddings''': 32,
											    '''embedding_proj_dim''': self.text_embedder_hidden_size,
											    '''time_embed_dim''': self.time_embed_dim,
											    '''num_layers''': 1,
											    '''clip_embed_dim''': self.time_input_dim * 2,
											    '''additional_embeddings''': 0,
											    '''time_embed_act_fn''': '''gelu''',
											    '''norm_in_type''': '''layer''',
											    '''embedding_proj_norm_type''': '''layer''',
											    '''encoder_hid_proj_type''': None,
											    '''added_emb_type''': None,
											}

											SCREAMING_SNAKE_CASE_   	=    PriorTransformer(**_lowerCamelCase		)
											return model


				@property
				def     _A							(							self:   List[Any]		):
											torch.manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    {
											    '''param_shapes''': (
											        (self.renderer_dim, 93),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											        (self.renderer_dim, 8),
											    ),
											    '''d_latent''': self.time_input_dim,
											    '''d_hidden''': self.renderer_dim,
											    '''n_output''': 12,
											    '''background''': (
											        0.1,
											        0.1,
											        0.1,
											    ),
											}
											SCREAMING_SNAKE_CASE_   	=    ShapERenderer(**_lowerCamelCase		)
											return model


				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    self.dummy_prior
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_encoder
											SCREAMING_SNAKE_CASE_   	=    self.dummy_image_processor
											SCREAMING_SNAKE_CASE_   	=    self.dummy_renderer

											SCREAMING_SNAKE_CASE_   	=    HeunDiscreteScheduler(
											    beta_schedule='''exp'''		,					num_train_timesteps=10_24		,					prediction_type='''sample'''		,					use_karras_sigmas=_lowerCamelCase		,					clip_sample=_lowerCamelCase		,					clip_sample_range=1.0		,					)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''prior''': prior,
											    '''image_encoder''': image_encoder,
											    '''image_processor''': image_processor,
											    '''renderer''': renderer,
											    '''scheduler''': scheduler,
											}

											return components


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[Any]		,					_lowerCamelCase:   Optional[Any]=0		):
											SCREAMING_SNAKE_CASE_   	=    floats_tensor((1, 3, 64, 64)		,					rng=random.Random(_lowerCamelCase		)		).to(_lowerCamelCase		)

											if str(_lowerCamelCase		).startswith('''mps'''		):
																		SCREAMING_SNAKE_CASE_   	=    torch.manual_seed(_lowerCamelCase		)
											else:
																		SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    {
											    '''image''': input_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 1,
											    '''frame_size''': 32,
											    '''output_type''': '''np''',
											}
											return inputs


				def     _A							(							self:   Any		):
											SCREAMING_SNAKE_CASE_   	=    '''cpu'''

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()

											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)

											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    pipe(**self.get_dummy_inputs(_lowerCamelCase		)		)
											SCREAMING_SNAKE_CASE_   	=    output.images[0]
											SCREAMING_SNAKE_CASE_   	=    image[0, -3:, -3:, -1]

											assert image.shape == (20, 32, 32, 3)

											SCREAMING_SNAKE_CASE_   	=    np.array(
											    [
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											        0.00_03_92_16,
											    ]		)

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


				def     _A							(							self:   str		):
											# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
											self._test_inference_batch_consistent(batch_sizes=[1, 2]		)


				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    torch_device == '''cpu'''
											SCREAMING_SNAKE_CASE_   	=    True
											self._test_inference_batch_single_identical(
											    batch_size=2		,					test_max_difference=_lowerCamelCase		,					relax_max_difference=_lowerCamelCase		,					)





				def     _A							(							self:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_components()
											SCREAMING_SNAKE_CASE_   	=    self.pipeline_class(**_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    1
											SCREAMING_SNAKE_CASE_   	=    2

											SCREAMING_SNAKE_CASE_   	=    self.get_dummy_inputs(_lowerCamelCase		)

											for key in inputs.keys():
																		if key in self.batch_params:
																									SCREAMING_SNAKE_CASE_   	=    batch_size * [inputs[key]]

											SCREAMING_SNAKE_CASE_   	=    pipe(**_lowerCamelCase		,					num_images_per_prompt=_lowerCamelCase		)[0]

											assert images.shape[0] == batch_size * num_images_per_prompt






@slow
@require_torch_gpu
class  __magic_name__						(  unittest.TestCase):
				'''simple docstring'''







				def     _A							(							self:   Any		):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()





				def     _A							(							self:   Optional[int]		):
											SCREAMING_SNAKE_CASE_   	=    load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
											SCREAMING_SNAKE_CASE_   	=    load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/shap_e/test_shap_e_img2img_out.npy'''		)
											SCREAMING_SNAKE_CASE_   	=    ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
											SCREAMING_SNAKE_CASE_   	=    pipe.to(_lowerCamelCase		)
											pipe.set_progress_bar_config(disable=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    torch.Generator(device=_lowerCamelCase		).manual_seed(0		)

											SCREAMING_SNAKE_CASE_   	=    pipe(
											    _lowerCamelCase		,					generator=_lowerCamelCase		,					guidance_scale=3.0		,					num_inference_steps=64		,					frame_size=64		,					output_type='''np'''		,					).images[0]

											assert images.shape == (20, 64, 64, 3)

											assert_mean_pixel_difference(_lowerCamelCase		,					_lowerCamelCase		)


