


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class  lowerCAmelCase				(				_snake_case      ,				unittest.TestCase							):
	_lowerCamelCase   :					str			      = KandinskyInpaintPipeline
	_lowerCamelCase   :					Optional[Any]			      = ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
	_lowerCamelCase   :					Dict			      = [
	    """prompt""",
	    """negative_prompt""",
	    """image_embeds""",
	    """negative_image_embeds""",
	    """image""",
	    """mask_image""",
	]
	_lowerCamelCase   :					Dict			      = [
	    """generator""",
	    """height""",
	    """width""",
	    """latents""",
	    """guidance_scale""",
	    """negative_prompt""",
	    """num_inference_steps""",
	    """return_dict""",
	    """guidance_scale""",
	    """num_images_per_prompt""",
	    """output_type""",
	    """return_dict""",
	]
	_lowerCamelCase   :					Dict			      = False



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return 32



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim



	@property
	def 				lowercase     (					self    ):
		return self.time_input_dim * 4



	@property
	def 				lowercase     (					self    ):
		return 100



	@property
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
		return tokenizer



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Optional[int]      							= MCLIPConfig(
		    numDims=self.cross_attention_dim ,					transformerDimensions=self.text_embedder_hidden_size ,					hidden_size=self.text_embedder_hidden_size ,					intermediate_size=37 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					vocab_size=1005 ,					)

		lowerCAmelCase					:    Dict      							= MultilingualCLIP(lowerCAmelCase__    )
		lowerCAmelCase					:    Optional[int]      							= text_encoder.eval()

		return text_encoder



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )

		lowerCAmelCase					:    List[Any]      							= {
		    'in_channels': 9,
		    # Out channels is double in channels because predicts mean and variance
		    'out_channels': 8,
		    'addition_embed_type': 'text_image',
		    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
		    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
		    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
		    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
		    'layers_per_block': 1,
		    'encoder_hid_dim': self.text_embedder_hidden_size,
		    'encoder_hid_dim_type': 'text_image_proj',
		    'cross_attention_dim': self.cross_attention_dim,
		    'attention_head_dim': 4,
		    'resnet_time_scale_shift': 'scale_shift',
		    'class_embed_type': None,
		}

		lowerCAmelCase					:    Any      							= UNetaDConditionModel(**lowerCAmelCase__    )
		return model



	@property
	def 				lowercase     (					self    ):
		return {
		    "block_out_channels": [32, 64],
		    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
		    "in_channels": 3,
		    "latent_channels": 4,
		    "layers_per_block": 1,
		    "norm_num_groups": 8,
		    "norm_type": "spatial",
		    "num_vq_embeddings": 12,
		    "out_channels": 3,
		    "up_block_types": [
		        "AttnUpDecoderBlock2D",
		        "UpDecoderBlock2D",
		    ],
		    "vq_embed_dim": 4,
		}



	@property
	def 				lowercase     (					self    ):
		torch.manual_seed(0    )
		lowerCAmelCase					:    Optional[int]      							= VQModel(**self.dummy_movq_kwargs    )
		return model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Dict      							= self.dummy_text_encoder
		lowerCAmelCase					:    List[str]      							= self.dummy_tokenizer
		lowerCAmelCase					:    Any      							= self.dummy_unet
		lowerCAmelCase					:    Dict      							= self.dummy_movq

		lowerCAmelCase					:    Optional[int]      							= DDIMScheduler(
		    num_train_timesteps=1000 ,					beta_schedule='linear' ,					beta_start=0.0_0_0_8_5 ,					beta_end=0.0_1_2 ,					clip_sample=lowerCAmelCase__ ,					set_alpha_to_one=lowerCAmelCase__ ,					steps_offset=1 ,					prediction_type='epsilon' ,					thresholding=lowerCAmelCase__ ,					)

		lowerCAmelCase					:    Union[str, Any]      							= {
		    'text_encoder': text_encoder,
		    'tokenizer': tokenizer,
		    'unet': unet,
		    'scheduler': scheduler,
		    'movq': movq,
		}

		return components



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		lowerCAmelCase					:    Optional[int]      							= floats_tensor((1, self.cross_attention_dim) ,					rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
		lowerCAmelCase					:    List[str]      							= floats_tensor((1, self.cross_attention_dim) ,					rng=random.Random(seed + 1    )    ).to(lowerCAmelCase__    )
		# create init_image
		lowerCAmelCase					:    Union[str, Any]      							= floats_tensor((1, 3, 64, 64) ,					rng=random.Random(lowerCAmelCase__    )    ).to(lowerCAmelCase__    )
		lowerCAmelCase					:    Dict      							= image.cpu().permute(0 ,					2 ,					3 ,					1    )[0]
		lowerCAmelCase					:    Optional[Any]      							= Image.fromarray(np.uinta(lowerCAmelCase__    )    ).convert('RGB'    ).resize((256, 256)    )
		# create mask
		lowerCAmelCase					:    str      							= np.ones((64, 64) ,					dtype=np.floataa    )
		lowerCAmelCase					:    List[Any]      							= 0

		if str(lowerCAmelCase__    ).startswith('mps'    ):
			lowerCAmelCase					:    Optional[Any]      							= torch.manual_seed(lowerCAmelCase__    )
		else:
			lowerCAmelCase					:    Union[str, Any]      							= torch.Generator(device=lowerCAmelCase__    ).manual_seed(lowerCAmelCase__    )
		lowerCAmelCase					:    Union[str, Any]      							= {
		    'prompt': 'horse',
		    'image': init_image,
		    'mask_image': mask,
		    'image_embeds': image_embeds,
		    'negative_image_embeds': negative_image_embeds,
		    'generator': generator,
		    'height': 64,
		    'width': 64,
		    'num_inference_steps': 2,
		    'guidance_scale': 4.0,
		    'output_type': 'np',
		}
		return inputs



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= 'cpu'

		lowerCAmelCase					:    Union[str, Any]      							= self.get_dummy_components()

		lowerCAmelCase					:    List[str]      							= self.pipeline_class(**lowerCAmelCase__    )
		lowerCAmelCase					:    Union[str, Any]      							= pipe.to(lowerCAmelCase__    )

		pipe.set_progress_bar_config(disable=lowerCAmelCase__    )

		lowerCAmelCase					:    Tuple      							= pipe(**self.get_dummy_inputs(lowerCAmelCase__    )    )
		lowerCAmelCase					:    Optional[Any]      							= output.images

		lowerCAmelCase					:    Optional[int]      							= pipe(
		    **self.get_dummy_inputs(lowerCAmelCase__    ) ,					return_dict=lowerCAmelCase__ ,					)[0]

		lowerCAmelCase					:    str      							= image[0, -3:, -3:, -1]
		lowerCAmelCase					:    int      							= image_from_tuple[0, -3:, -3:, -1]

		print(f"image.shape {image.shape}"    )

		assert image.shape == (1, 64, 64, 3)

		lowerCAmelCase					:    Optional[Any]      							= np.array(
		    [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]    )

		assert (
		    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
		assert (
		    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
		), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"



	def 				lowercase     (					self    ):
		super().test_inference_batch_single_identical(expected_max_diff=3e-3    )



@slow
@require_torch_gpu
class  lowerCAmelCase				(				unittest.TestCase							):



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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'    )

		lowerCAmelCase					:    Optional[int]      							= load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
		lowerCAmelCase					:    Dict      							= np.ones((768, 768) ,					dtype=np.floataa    )
		lowerCAmelCase					:    Tuple      							= 0

		lowerCAmelCase					:    Dict      							= 'a hat'

		lowerCAmelCase					:    List[str]      							= KandinskyPriorPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-1-prior' ,					torch_dtype=torch.floataa    )
		pipe_prior.to(lowerCAmelCase__    )

		lowerCAmelCase					:    str      							= KandinskyInpaintPipeline.from_pretrained(
		    'kandinsky-community/kandinsky-2-1-inpaint' ,					torch_dtype=torch.floataa    )
		lowerCAmelCase					:    str      							= pipeline.to(lowerCAmelCase__    )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase__    )

		lowerCAmelCase					:    str      							= torch.Generator(device='cpu'    ).manual_seed(0    )
		lowerCAmelCase							,   lowerCAmelCase					:    int      							= pipe_prior(
		    lowerCAmelCase__ ,					generator=lowerCAmelCase__ ,					num_inference_steps=5 ,					negative_prompt='' ,					).to_tuple()

		lowerCAmelCase					:    Optional[int]      							= pipeline(
		    lowerCAmelCase__ ,					image=lowerCAmelCase__ ,					mask_image=lowerCAmelCase__ ,					image_embeds=lowerCAmelCase__ ,					negative_image_embeds=lowerCAmelCase__ ,					generator=lowerCAmelCase__ ,					num_inference_steps=100 ,					height=768 ,					width=768 ,					output_type='np' ,					)

		lowerCAmelCase					:    int      							= output.images[0]

		assert image.shape == (768, 768, 3)

		assert_mean_pixel_difference(lowerCAmelCase__ ,					lowerCAmelCase__    )




'''simple docstring'''




import copy

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


_lowerCAmelCase  :		Optional[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Dict		     =	{
    '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  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Any			      = """deformable_detr"""
	_lowerCamelCase   :					List[str]			      = {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	}



	def __init__(					self ,					snake_case__=True ,					snake_case__=None ,					snake_case__=3 ,					snake_case__=300 ,					snake_case__=1024 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=6 ,					snake_case__=1024 ,					snake_case__=8 ,					snake_case__=0.0 ,					snake_case__=True ,					snake_case__="relu" ,					snake_case__=256 ,					snake_case__=0.1 ,					snake_case__=0.0 ,					snake_case__=0.0 ,					snake_case__=0.0_2 ,					snake_case__=1.0 ,					snake_case__=True ,					snake_case__=False ,					snake_case__="sine" ,					snake_case__="resnet50" ,					snake_case__=True ,					snake_case__=False ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=4 ,					snake_case__=False ,					snake_case__=300 ,					snake_case__=False ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=1 ,					snake_case__=1 ,					snake_case__=5 ,					snake_case__=2 ,					snake_case__=0.1 ,					snake_case__=0.2_5 ,					snake_case__=False ,					**snake_case__ ,					):
		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.'    )
				lowerCAmelCase					:    Optional[int]      							= CONFIG_MAPPING['resnet'](out_features=['stage4']    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    List[str]      							= backbone_config.get('model_type'    )
				lowerCAmelCase					:    str      							= CONFIG_MAPPING[backbone_model_type]
				lowerCAmelCase					:    Optional[Any]      							= config_class.from_dict(snake_case__    )
		lowerCAmelCase					:    Union[str, Any]      							= use_timm_backbone
		lowerCAmelCase					:    List[Any]      							= backbone_config
		lowerCAmelCase					:    Any      							= num_channels
		lowerCAmelCase					:    Tuple      							= num_queries
		lowerCAmelCase					:    Dict      							= max_position_embeddings
		lowerCAmelCase					:    int      							= d_model
		lowerCAmelCase					:    List[str]      							= encoder_ffn_dim
		lowerCAmelCase					:    List[str]      							= encoder_layers
		lowerCAmelCase					:    int      							= encoder_attention_heads
		lowerCAmelCase					:    str      							= decoder_ffn_dim
		lowerCAmelCase					:    str      							= decoder_layers
		lowerCAmelCase					:    Dict      							= decoder_attention_heads
		lowerCAmelCase					:    str      							= dropout
		lowerCAmelCase					:    List[str]      							= attention_dropout
		lowerCAmelCase					:    Union[str, Any]      							= activation_dropout
		lowerCAmelCase					:    str      							= activation_function
		lowerCAmelCase					:    Any      							= init_std
		lowerCAmelCase					:    Any      							= init_xavier_std
		lowerCAmelCase					:    Dict      							= encoder_layerdrop
		lowerCAmelCase					:    int      							= auxiliary_loss
		lowerCAmelCase					:    Optional[Any]      							= position_embedding_type
		lowerCAmelCase					:    List[str]      							= backbone
		lowerCAmelCase					:    int      							= use_pretrained_backbone
		lowerCAmelCase					:    int      							= dilation
		# deformable attributes
		lowerCAmelCase					:    List[str]      							= num_feature_levels
		lowerCAmelCase					:    List[str]      							= encoder_n_points
		lowerCAmelCase					:    Union[str, Any]      							= decoder_n_points
		lowerCAmelCase					:    Tuple      							= two_stage
		lowerCAmelCase					:    Dict      							= two_stage_num_proposals
		lowerCAmelCase					:    Union[str, 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
		lowerCAmelCase					:    Union[str, Any]      							= class_cost
		lowerCAmelCase					:    Dict      							= bbox_cost
		lowerCAmelCase					:    List[Any]      							= giou_cost
		# Loss coefficients
		lowerCAmelCase					:    Dict      							= mask_loss_coefficient
		lowerCAmelCase					:    Any      							= dice_loss_coefficient
		lowerCAmelCase					:    str      							= bbox_loss_coefficient
		lowerCAmelCase					:    Tuple      							= giou_loss_coefficient
		lowerCAmelCase					:    List[str]      							= eos_coefficient
		lowerCAmelCase					:    Any      							= focal_alpha
		lowerCAmelCase					:    Dict      							= disable_custom_kernels
		super().__init__(is_encoder_decoder=snake_case__ ,					**snake_case__    )



	@property
	def 				lowercase     (					self    ):
		return self.encoder_attention_heads



	@property
	def 				lowercase     (					self    ):
		return self.d_model



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= copy.deepcopy(self.__dict__    )
		if self.backbone_config is not None:
			lowerCAmelCase					:    List[Any]      							= self.backbone_config.to_dict()
		lowerCAmelCase					:    str      							= self.__class__.model_type
		return output
