


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 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					KandinskyVaaControlnetImgaImgPipeline
					A__     =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
					A__     =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
					A__     =					[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					A__     =					False

					@property
					def     lowerCamelCase__	(self       :		Tuple						)		->  int:




							"""simple docstring"""


							return 32

					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  List[Any]:




							"""simple docstring"""


							return 32

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




							"""simple docstring"""


							return self.time_input_dim

					@property
					def     lowerCamelCase__	(self       :		Any						)		->  List[Any]:




							"""simple docstring"""


							return self.time_input_dim * 4

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




							"""simple docstring"""


							return 100

					@property
					def     lowerCamelCase__	(self       :		Optional[int]						)		->  str:




							"""simple docstring"""


							torch.manual_seed(0						)

							lowercase__	     =						{
							    """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,
							}

							lowercase__	     =						UNetaDConditionModel(**_UpperCAmelCase						)
							return model

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




							"""simple docstring"""


							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     lowerCamelCase__	(self       :		Any						)		->  Dict:




							"""simple docstring"""


							torch.manual_seed(0						)
							lowercase__	     =						VQModel(**self.dummy_movq_kwargs						)
							return model

					def     lowerCamelCase__	(self       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.dummy_unet
							lowercase__	     =						self.dummy_movq

							lowercase__	     =						{
							    """num_train_timesteps""": 1000,
							    """beta_schedule""": """linear""",
							    """beta_start""": 0.00_085,
							    """beta_end""": 0.012,
							    """clip_sample""": False,
							    """set_alpha_to_one""": False,
							    """steps_offset""": 0,
							    """prediction_type""": """epsilon""",
							    """thresholding""": False,
							}

							lowercase__	     =						DDIMScheduler(**_UpperCAmelCase						)

							lowercase__	     =						{
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components

					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		int=0						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
							lowercase__	     =						floats_tensor((1, self.text_embedder_hidden_size) ,   rng=random.Random(seed + 1						)						).to(
							    _UpperCAmelCase						)
							# create init_image
							lowercase__	     =						floats_tensor((1, 3, 64, 64) ,   rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)
							lowercase__	     =						image.cpu().permute(0 ,   2 ,   3 ,   1						)[0]
							lowercase__	     =						Image.fromarray(np.uinta(_UpperCAmelCase						)						).convert("""RGB"""						).resize((256, 256)						)
							# create hint
							lowercase__	     =						floats_tensor((1, 3, 64, 64) ,   rng=random.Random(_UpperCAmelCase						)						).to(_UpperCAmelCase						)

							if str(_UpperCAmelCase						).startswith("""mps"""						):
									lowercase__	     =						torch.manual_seed(_UpperCAmelCase						)
							else:
									lowercase__	     =						torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
							lowercase__	     =						{
							    """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     lowerCamelCase__	(self       :		int						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						"""cpu"""

							lowercase__	     =						self.get_dummy_components()

							lowercase__	     =						self.pipeline_class(**_UpperCAmelCase						)
							lowercase__	     =						pipe.to(_UpperCAmelCase						)

							pipe.set_progress_bar_config(disable=_UpperCAmelCase						)

							lowercase__	     =						pipe(**self.get_dummy_inputs(_UpperCAmelCase						)						)
							lowercase__	     =						output.images

							lowercase__	     =						pipe(
							    **self.get_dummy_inputs(_UpperCAmelCase						) ,   return_dict=_UpperCAmelCase ,   )[0]

							lowercase__	     =						image[0, -3:, -3:, -1]
							lowercase__	     =						image_from_tuple[0, -3:, -3:, -1]

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

							lowercase__	     =						np.array(
							    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]						)
							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 				A       (			unittest.TestCase				):
					'''simple docstring'''






					def     lowerCamelCase__	(self       :		List[str]						)		->  Any:




							"""simple docstring"""


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


					def     lowerCamelCase__	(self       :		List[Any]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""						)

							lowercase__	     =						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""						)
							lowercase__	     =						init_image.resize((512, 512)						)

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

							lowercase__	     =						"""A robot, 4k photo"""

							lowercase__	     =						KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-prior""" ,   torch_dtype=torch.floataa						)
							pipe_prior.to(_UpperCAmelCase						)

							lowercase__	     =						KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-2-controlnet-depth""" ,   torch_dtype=torch.floataa						)
							lowercase__	     =						pipeline.to(_UpperCAmelCase						)

							pipeline.set_progress_bar_config(disable=_UpperCAmelCase						)

							lowercase__	     =						torch.Generator(device="""cpu"""						).manual_seed(0						)

							lowercase__     ,      lowercase__	     =						pipe_prior(
							    _UpperCAmelCase ,   image=_UpperCAmelCase ,   strength=0.85 ,   generator=_UpperCAmelCase ,   negative_prompt="""""" ,   ).to_tuple()

							lowercase__	     =						pipeline(
							    image=_UpperCAmelCase ,   image_embeds=_UpperCAmelCase ,   negative_image_embeds=_UpperCAmelCase ,   hint=_UpperCAmelCase ,   generator=_UpperCAmelCase ,   num_inference_steps=100 ,   height=512 ,   width=512 ,   strength=0.5 ,   output_type="""np""" ,   )

							lowercase__	     =						output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(_UpperCAmelCase ,   _UpperCAmelCase						)





from typing import TYPE_CHECKING

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


A			:						Union[str, Any]        =  {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  ['ConvNextFeatureExtractor']
	A			:						Optional[Any]        =  ['ConvNextImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						str        =  [
	    'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ConvNextForImageClassification',
	    'ConvNextModel',
	    'ConvNextPreTrainedModel',
	    'ConvNextBackbone',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A			:						List[str]        =  [
	    'TFConvNextForImageClassification',
	    'TFConvNextModel',
	    'TFConvNextPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_convnext import ConvNextFeatureExtractor
		from .image_processing_convnext import ConvNextImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_convnext import (
		    CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ConvNextBackbone,
		    ConvNextForImageClassification,
		    ConvNextModel,
		    ConvNextPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
	import sys

	A			:						int        =  _LazyModule(__name__, globals()['__file__'], _import_structure)

