




import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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       _A				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       KandinskyImgaImgPipeline
		__a					    =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
		__a					    =       [
		    """prompt""",
		    """negative_prompt""",
		    """image_embeds""",
		    """negative_image_embeds""",
		    """image""",
		]
		__a					    =       [
		    """generator""",
		    """height""",
		    """width""",
		    """strength""",
		    """guidance_scale""",
		    """negative_prompt""",
		    """num_inference_steps""",
		    """return_dict""",
		    """guidance_scale""",
		    """num_images_per_prompt""",
		    """output_type""",
		    """return_dict""",
		]
		__a					    =       False






		@property
		def 				UpperCAmelCase (    self			):
								return 32






		@property
		def 				UpperCAmelCase (    self			):
								return 32






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






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






		@property
		def 				UpperCAmelCase (    self			):
								return 100






		@property
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
								return tokenizer






		@property
		def 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)
								_UpperCAmelCase      =			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						,		)

								_UpperCAmelCase      =			MultilingualCLIP(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			text_encoder.eval()

								return text_encoder






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

								_UpperCAmelCase      =			{
								    """in_channels""": 4,
								    # 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,
								}

								_UpperCAmelCase      =			UNetaDConditionModel(**_SCREAMING_SNAKE_CASE			)
								return model






		@property
		def 				UpperCAmelCase (    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 				UpperCAmelCase (    self			):
								torch.manual_seed(0			)
								_UpperCAmelCase      =			VQModel(**self.dummy_movq_kwargs			)
								return model






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.dummy_text_encoder
								_UpperCAmelCase      =			self.dummy_tokenizer
								_UpperCAmelCase      =			self.dummy_unet
								_UpperCAmelCase      =			self.dummy_movq

								_UpperCAmelCase      =			{
								    """num_train_timesteps""": 1000,
								    """beta_schedule""": """linear""",
								    """beta_start""": 0.0_0085,
								    """beta_end""": 0.012,
								    """clip_sample""": False,
								    """set_alpha_to_one""": False,
								    """steps_offset""": 0,
								    """prediction_type""": """epsilon""",
								    """thresholding""": False,
								}

								_UpperCAmelCase      =			DDIMScheduler(**_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			{
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """movq""": movq,
								}

								return components






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

								if str(_SCREAMING_SNAKE_CASE			).startswith("""mps"""			):
														_UpperCAmelCase      =			torch.manual_seed(_SCREAMING_SNAKE_CASE			)
								else:
														_UpperCAmelCase      =			torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			{
								    """prompt""": """horse""",
								    """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 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""cpu"""

								_UpperCAmelCase      =			self.get_dummy_components()

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

								pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

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

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

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

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

								_UpperCAmelCase      =			np.array(
								    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]			)
								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     ):






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/kandinsky/kandinsky_img2img_frog.npy"""			)

								_UpperCAmelCase      =			load_image(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
								_UpperCAmelCase      =			"""A red cartoon frog, 4k"""

								_UpperCAmelCase      =			KandinskyPriorPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1-prior"""						,		torch_dtype=torch.floataa			)
								pipe_prior.to(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			KandinskyImgaImgPipeline.from_pretrained(
								    """kandinsky-community/kandinsky-2-1"""						,		torch_dtype=torch.floataa			)
								_UpperCAmelCase      =			pipeline.to(_SCREAMING_SNAKE_CASE			)

								pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			torch.Generator(device="""cpu"""			).manual_seed(0			)
								_UpperCAmelCase    ,					_UpperCAmelCase      =			pipe_prior(
								    _SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE						,		num_inference_steps=5						,		negative_prompt=""""""						,		).to_tuple()

								_UpperCAmelCase      =			pipeline(
								    _SCREAMING_SNAKE_CASE						,		image=_SCREAMING_SNAKE_CASE						,		image_embeds=_SCREAMING_SNAKE_CASE						,		negative_image_embeds=_SCREAMING_SNAKE_CASE						,		generator=_SCREAMING_SNAKE_CASE						,		num_inference_steps=100						,		height=768						,		width=768						,		strength=0.2						,		output_type="""np"""						,		)

								_UpperCAmelCase      =			output.images[0]

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

								assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)





import unittest

import numpy as np




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case = None			,     )						->				np.ndarray:
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )
						_UpperCAmelCase      =			np.shape(snake_case       )

						if shape_a[0] != shape_b[0]:
												_UpperCAmelCase      =			(
												    """Expected the same number of rows for A and B. """
												    f"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(snake_case       )

						if shape_b[1] != shape_c[1]:
												_UpperCAmelCase      =			(
												    """Expected the same number of columns for B and C. """
												    f"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(snake_case       )

						_UpperCAmelCase      =			pseudo_inv
						if a_inv is None:
												try:
																		_UpperCAmelCase      =			np.linalg.inv(snake_case       )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""       )

						return mat_c - mat_b.T @ a_inv @ mat_b




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								_UpperCAmelCase      =			schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			np.block([[a, b], [b.T, c]]			)

								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			np.linalg.det(_SCREAMING_SNAKE_CASE			)

								self.assertAlmostEqual(_SCREAMING_SNAKE_CASE						,		det_a * det_s			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1], [6, 3]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
								_UpperCAmelCase      =			np.array([[0, 3], [3, 0], [2, 3]]			)
								_UpperCAmelCase      =			np.array([[2, 1, 3], [6, 3, 5]]			)

								with self.assertRaises(_SCREAMING_SNAKE_CASE			):
														schur_complement(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				unittest.main()