


'''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        lowercase     (							A__     ,		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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return 32




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return 32




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.time_input_dim




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.time_input_dim * 4




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return 100




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)

							UpperCamelCase__       :List[Any]							 =			{
							    '''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,
							}

							UpperCamelCase__       :str							 =			UNetaDConditionModel(**UpperCamelCase_							)
							return model




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			VQModel(**self.dummy_movq_kwargs							)
							return model




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.dummy_unet
							UpperCamelCase__       :Tuple							 =			self.dummy_movq

							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''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,
							}

							UpperCamelCase__       :List[Any]							 =			DDIMScheduler(**UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''movq''': movq,
							}

							return components




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


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

							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :List[Any]							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :List[str]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			{
							    '''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    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[Any]							 =			'''cpu'''

							UpperCamelCase__       :Tuple							 =			self.get_dummy_components()

							UpperCamelCase__       :Dict							 =			self.pipeline_class(**UpperCamelCase_							)
							UpperCamelCase__       :Dict							 =			pipe.to(UpperCamelCase_							)

							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :Tuple							 =			pipe(**self.get_dummy_inputs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			output.images

							UpperCamelCase__       :Any							 =			pipe(
							    **self.get_dummy_inputs(UpperCamelCase_							)   ,       return_dict=UpperCamelCase_   ,       )[0]

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

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

							UpperCamelCase__       :Optional[int]							 =			np.array(
							    [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]							)
							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        lowercase     (							unittest.TestCase ):

		"""simple docstring"""

		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''							)

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

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

							UpperCamelCase__       :Dict							 =			KandinskyVaaImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-decoder'''   ,       torch_dtype=torch.floataa							)
							UpperCamelCase__       :Union[str, Any]							 =			pipeline.to(UpperCamelCase_							)

							pipeline.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			torch.Generator(device='''cpu'''							).manual_seed(0							)
							UpperCamelCase__ ,    UpperCamelCase__       :int							 =			pipe_prior(
							    UpperCamelCase_   ,       generator=UpperCamelCase_   ,       num_inference_steps=5   ,       negative_prompt=''''''   ,       ).to_tuple()

							UpperCamelCase__       :Any							 =			pipeline(
							    image=UpperCamelCase_   ,       image_embeds=UpperCamelCase_   ,       negative_image_embeds=UpperCamelCase_   ,       generator=UpperCamelCase_   ,       num_inference_steps=100   ,       height=768   ,       width=768   ,       strength=0.2   ,       output_type='''np'''   ,       )

							UpperCamelCase__       :int							 =			output.images[0]

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

							assert_mean_pixel_difference(UpperCamelCase_   ,       UpperCamelCase_							)



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case											=					{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMSNModel''',
		    '''ViTMSNForImageClassification''',
		    '''ViTMSNPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)