



"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    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  __SCREAMING_SNAKE_CASE			(   lowerCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							KandinskyVaaImgaImgPipeline
					_a         =							['image_embeds', 'negative_image_embeds', 'image']
					_a         =							[
					    'image_embeds',
					    'negative_image_embeds',
					    'image',
					]
					_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 	snake_case ( self							:						Optional[Any]  )->      List[Any]:
								return 32



					@property
					def 	snake_case ( self							:						Optional[Any]  )->      int:
								return 32



					@property
					def 	snake_case ( self							:						List[str]  )->      Any:
								return self.time_input_dim



					@property
					def 	snake_case ( self							:						Tuple  )->      Dict:
								return self.time_input_dim * 4



					@property
					def 	snake_case ( self							:						int  )->      Dict:
								return 100



					@property
					def 	snake_case ( self							:						str  )->      Optional[int]:
								torch.manual_seed(0  )

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

								lowerCamelCase__	:	Union[str, Any]				=UNetaDConditionModel(**lowerCamelCase  )
								return model



					@property
					def 	snake_case ( self							:						Tuple  )->      Optional[Any]:
								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 	snake_case ( self							:						Dict  )->      Any:
								torch.manual_seed(0  )
								lowerCamelCase__	:	Dict				=VQModel(**self.dummy_movq_kwargs  )
								return model



					def 	snake_case ( self							:						Dict  )->      Any:
								lowerCamelCase__	:	Dict				=self.dummy_unet
								lowerCamelCase__	:	int				=self.dummy_movq

								lowerCamelCase__	:	str				={
								    '''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,
								}

								lowerCamelCase__	:	Dict				=DDIMScheduler(**lowerCamelCase  )

								lowerCamelCase__	:	int				={
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components



					def 	snake_case ( self							:						Optional[int],						lowerCamelCase							:						Optional[int],						lowerCamelCase							:						Optional[int]=0  )->      List[Any]:
								lowerCamelCase__	:	str				=floats_tensor((1, self.text_embedder_hidden_size),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowerCamelCase__	:	List[Any]				=floats_tensor((1, self.text_embedder_hidden_size),						rng=random.Random(seed + 1  )  ).to(
								    lowerCamelCase  )
								# create init_image
								lowerCamelCase__	:	Optional[int]				=floats_tensor((1, 3, 64, 64),						rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowerCamelCase__	:	Dict				=image.cpu().permute(0,						2,						3,						1  )[0]
								lowerCamelCase__	:	str				=Image.fromarray(np.uinta(lowerCamelCase  )  ).convert('''RGB'''  ).resize((256, 256)  )

								if str(lowerCamelCase  ).startswith('''mps'''  ):
											lowerCamelCase__	:	Optional[Any]				=torch.manual_seed(lowerCamelCase  )
								else:
											lowerCamelCase__	:	List[str]				=torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowerCamelCase__	:	int				={
								    '''image''': init_image,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''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							:						Optional[Any]  )->      Optional[Any]:
								lowerCamelCase__	:	str				='''cpu'''

								lowerCamelCase__	:	str				=self.get_dummy_components()

								lowerCamelCase__	:	List[str]				=self.pipeline_class(**lowerCamelCase  )
								lowerCamelCase__	:	Dict				=pipe.to(lowerCamelCase  )

								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Union[str, Any]				=pipe(**self.get_dummy_inputs(lowerCamelCase  )  )
								lowerCamelCase__	:	int				=output.images

								lowerCamelCase__	:	Optional[Any]				=pipe(
								    **self.get_dummy_inputs(lowerCamelCase  ),						return_dict=lowerCamelCase,						)[0]

								lowerCamelCase__	:	List[Any]				=image[0, -3:, -3:, -1]
								lowerCamelCase__	:	Optional[int]				=image_from_tuple[0, -3:, -3:, -1]

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

								lowerCamelCase__	:	Any				=np.array(
								    [0.6_199_778, 0.63_984_406, 0.46_145_785, 0.62_944_984, 0.5_622_215, 0.47_306_132, 0.47_441_456, 0.4_607_606, 0.48_719_263]  )
								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  __SCREAMING_SNAKE_CASE			(   unittest.TestCase     ):


					'''simple docstring'''





					def 	snake_case ( self							:						int  )->      Dict:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




					def 	snake_case ( self							:						Optional[int]  )->      List[Any]:
								lowerCamelCase__	:	Any				=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''  )

								lowerCamelCase__	:	List[str]				=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''  )
								lowerCamelCase__	:	Union[str, Any]				='''A red cartoon frog, 4k'''

								lowerCamelCase__	:	Union[str, Any]				=KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior''',						torch_dtype=torch.floataa  )
								pipe_prior.to(lowerCamelCase  )

								lowerCamelCase__	:	Any				=KandinskyVaaImgaImgPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder''',						torch_dtype=torch.floataa  )
								lowerCamelCase__	:	List[Any]				=pipeline.to(lowerCamelCase  )

								pipeline.set_progress_bar_config(disable=lowerCamelCase  )

								lowerCamelCase__	:	Optional[int]				=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(
								    image=lowerCamelCase,						image_embeds=lowerCamelCase,						negative_image_embeds=lowerCamelCase,						generator=lowerCamelCase,						num_inference_steps=100,						height=768,						width=768,						strength=0.2,						output_type='''np''',						)

								lowerCamelCase__	:	Tuple				=output.images[0]

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

								assert_mean_pixel_difference(lowerCamelCase,						lowerCamelCase  )









"""simple docstring"""






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,
)


_lowercase				:				Optional[Any]   		=		{
    "configuration_clip": [
        "CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "CLIPConfig",
        "CLIPOnnxConfig",
        "CLIPTextConfig",
        "CLIPVisionConfig",
    ],
    "processing_clip": ["CLIPProcessor"],
    "tokenization_clip": ["CLIPTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				str   		=		["CLIPTokenizerFast"]

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Any   		=		["CLIPFeatureExtractor"]
		_lowercase				:				int   		=		["CLIPImageProcessor"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Optional[Any]   		=		[
		    "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CLIPModel",
		    "CLIPPreTrainedModel",
		    "CLIPTextModel",
		    "CLIPTextModelWithProjection",
		    "CLIPVisionModel",
		    "CLIPVisionModelWithProjection",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Dict   		=		[
		    "TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFCLIPModel",
		    "TFCLIPPreTrainedModel",
		    "TFCLIPTextModel",
		    "TFCLIPVisionModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				:				Union[str, Any]   		=		[
		    "FlaxCLIPModel",
		    "FlaxCLIPPreTrainedModel",
		    "FlaxCLIPTextModel",
		    "FlaxCLIPTextPreTrainedModel",
		    "FlaxCLIPVisionModel",
		    "FlaxCLIPVisionPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_clip import (
		    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    CLIPConfig,
		    CLIPOnnxConfig,
		    CLIPTextConfig,
		    CLIPVisionConfig,
		)
		from .processing_clip import CLIPProcessor
		from .tokenization_clip import CLIPTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_clip_fast import CLIPTokenizerFast

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_clip import CLIPFeatureExtractor
				from .image_processing_clip import CLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_clip import (
				    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CLIPModel,
				    CLIPPreTrainedModel,
				    CLIPTextModel,
				    CLIPTextModelWithProjection,
				    CLIPVisionModel,
				    CLIPVisionModelWithProjection,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_clip import (
				    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFCLIPModel,
				    TFCLIPPreTrainedModel,
				    TFCLIPTextModel,
				    TFCLIPVisionModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_clip import (
				    FlaxCLIPModel,
				    FlaxCLIPPreTrainedModel,
				    FlaxCLIPTextModel,
				    FlaxCLIPTextPreTrainedModel,
				    FlaxCLIPVisionModel,
				    FlaxCLIPVisionPreTrainedModel,
				)


else:
		import sys

		_lowercase				:				Union[str, Any]   		=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




