


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 						_UpperCAmelCase      (     lowercase		,		unittest.TestCase							):
		lowerCamelCase_	:			Union[str, Any]       			=  KandinskyVaaControlnetImgaImgPipeline
		lowerCamelCase_	:			Tuple       			=  ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
		lowerCamelCase_	:			Dict       			=  ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
		lowerCamelCase_	:			str       			=  [
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		lowerCamelCase_	:			Union[str, Any]       			=  False






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






		@property
		def _snake_case		(						self				:     int):
				return 32






		@property
		def _snake_case		(						self				:     Tuple):
				return self.time_input_dim






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






		@property
		def _snake_case		(						self				:     Dict):
				return 1_00






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

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{
				    "in_channels": 8,
				    # Out channels is double in channels because predicts mean and variance
				    "out_channels": 8,
				    "addition_embed_type": "image_hint",
				    "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": "image_proj",
				    "cross_attention_dim": self.cross_attention_dim,
				    "attention_head_dim": 4,
				    "resnet_time_scale_shift": "scale_shift",
				    "class_embed_type": None,
				}

				SCREAMING_SNAKE_CASE_				:List[str]				     =						UNetaDConditionModel(**UpperCAmelCase)
				return model






		@property
		def _snake_case		(						self				:     Any):
				return {
				    "block_out_channels": [32, 32, 64, 64],
				    "down_block_types": [
				        "DownEncoderBlock2D",
				        "DownEncoderBlock2D",
				        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
				    "vq_embed_dim": 4,
				}






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






		def _snake_case		(						self				:     Dict):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.dummy_unet
				SCREAMING_SNAKE_CASE_				:Dict				     =						self.dummy_movq

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{
				    "num_train_timesteps": 10_00,
				    "beta_schedule": "linear",
				    "beta_start": 0.00085,
				    "beta_end": 0.012,
				    "clip_sample": False,
				    "set_alpha_to_one": False,
				    "steps_offset": 0,
				    "prediction_type": "epsilon",
				    "thresholding": False,
				}

				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(**UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{
				    "unet": unet,
				    "scheduler": scheduler,
				    "movq": movq,
				}

				return components






		def _snake_case		(						self				:     Optional[int]   ,  UpperCAmelCase				:     str   ,  UpperCAmelCase				:     Any=0):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(UpperCAmelCase)).to(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						floats_tensor((1, self.text_embedder_hidden_size)   ,  rng=random.Random(seed + 1)).to(
				    UpperCAmelCase)
				# create init_image
				SCREAMING_SNAKE_CASE_				:Tuple				     =						floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(UpperCAmelCase)).to(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image.cpu().permute(0   ,  2   ,  3   ,  1)[0]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Image.fromarray(np.uinta(UpperCAmelCase)).convert("RGB").resize((2_56, 2_56))
				# create hint
				SCREAMING_SNAKE_CASE_				:Dict				     =						floats_tensor((1, 3, 64, 64)   ,  rng=random.Random(UpperCAmelCase)).to(UpperCAmelCase)

				if str(UpperCAmelCase).startswith("mps"):
						SCREAMING_SNAKE_CASE_				:Any				     =						torch.manual_seed(UpperCAmelCase)
				else:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.Generator(device=UpperCAmelCase).manual_seed(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						{
				    "image": init_image,
				    "image_embeds": image_embeds,
				    "negative_image_embeds": negative_image_embeds,
				    "hint": hint,
				    "generator": generator,
				    "height": 64,
				    "width": 64,
				    "num_inference_steps": 10,
				    "guidance_scale": 7.0,
				    "strength": 0.2,
				    "output_type": "np",
				}
				return inputs







		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:int				     =						"cpu"

				SCREAMING_SNAKE_CASE_				:int				     =						self.get_dummy_components()

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.pipeline_class(**UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Tuple				     =						pipe.to(UpperCAmelCase)

				pipe.set_progress_bar_config(disable=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						pipe(**self.get_dummy_inputs(UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Dict				     =						output.images

				SCREAMING_SNAKE_CASE_				:str				     =						pipe(
				    **self.get_dummy_inputs(UpperCAmelCase)   ,  return_dict=UpperCAmelCase   ,  )[0]

				SCREAMING_SNAKE_CASE_				:str				     =						image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						image_from_tuple[0, -3:, -3:, -1]

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

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.array(
				    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736])
				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()}"




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






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







		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Any				     =						load_numpy(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy")

				SCREAMING_SNAKE_CASE_				:Any				     =						load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png")
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						init_image.resize((5_12, 5_12))

				SCREAMING_SNAKE_CASE_				:List[str]				     =						load_image(
				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
				    "/kandinskyv22/hint_image_cat.png")
				SCREAMING_SNAKE_CASE_				:List[str]				     =						torch.from_numpy(np.array(UpperCAmelCase)).float() / 255.0
				SCREAMING_SNAKE_CASE_				:List[str]				     =						hint.permute(2   ,  0   ,  1).unsqueeze(0)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						"A robot, 4k photo"

				SCREAMING_SNAKE_CASE_				:Any				     =						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-prior"   ,  torch_dtype=torch.floataa)
				pipe_prior.to(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Tuple				     =						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
				    "kandinsky-community/kandinsky-2-2-controlnet-depth"   ,  torch_dtype=torch.floataa)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						pipeline.to(UpperCAmelCase)

				pipeline.set_progress_bar_config(disable=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						torch.Generator(device="cpu").manual_seed(0)

				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						pipe_prior(
				    UpperCAmelCase   ,  image=UpperCAmelCase   ,  strength=0.85   ,  generator=UpperCAmelCase   ,  negative_prompt=""   ,  ).to_tuple()

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						pipeline(
				    image=UpperCAmelCase   ,  image_embeds=UpperCAmelCase   ,  negative_image_embeds=UpperCAmelCase   ,  hint=UpperCAmelCase   ,  generator=UpperCAmelCase   ,  num_inference_steps=1_00   ,  height=5_12   ,  width=5_12   ,  strength=0.5   ,  output_type="np"   ,  )

				SCREAMING_SNAKE_CASE_				:str				     =						output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				assert_mean_pixel_difference(UpperCAmelCase   ,  UpperCAmelCase)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE__     					=			{
    "configuration_owlvit": [
        "OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "OwlViTConfig",
        "OwlViTOnnxConfig",
        "OwlViTTextConfig",
        "OwlViTVisionConfig",
    ],
    "processing_owlvit": ["OwlViTProcessor"],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			["OwlViTFeatureExtractor"]
		SCREAMING_SNAKE_CASE__     					=			["OwlViTImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		SCREAMING_SNAKE_CASE__     					=			[
		    "OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "OwlViTModel",
		    "OwlViTPreTrainedModel",
		    "OwlViTTextModel",
		    "OwlViTVisionModel",
		    "OwlViTForObjectDetection",
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		SCREAMING_SNAKE_CASE__     					=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






