
'''simple docstring'''


from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__lowercase							:   str					=     299792458

# Symbols
__lowercase				,		__lowercase				,		__lowercase				,		__lowercase							:   int					=     symbols('''ct x y z''')




def lowercase_	(   _lowercase				)     ->							float:
					'''simple docstring'''

					if velocity > c:
										raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''				)
					elif velocity < 1:
										# Usually the speed should be much higher than 1 (c order of magnitude)
										raise ValueError('''Speed must be greater than or equal to 1!'''				)

					return velocity / c




def lowercase_	(   _lowercase				)     ->							float:
					'''simple docstring'''

					return 1 / sqrt(1 - beta(_lowercase				) ** 2				)




def lowercase_	(   _lowercase				)     ->							np.ndarray:
					'''simple docstring'''

					return np.array(
					    [
					        [gamma(_lowercase				), -gamma(_lowercase				) * beta(_lowercase				), 0, 0],
					        [-gamma(_lowercase				) * beta(_lowercase				), gamma(_lowercase				), 0, 0],
					        [0, 0, 1, 0],
					        [0, 0, 0, 1],
					    ]				)




def lowercase_	(   _lowercase			,       _lowercase = None				)     ->							np.ndarray:
					'''simple docstring'''

					if event is None:
										lowerCamelCase_					:      Optional[Any]     =		np.array([ct, x, y, z]				)  # Symbolic four vector
					else:
										event[0] *= c  # x0 is ct (speed of light * time)

					return transformation_matrix(_lowercase				) @ event


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Example of symbolic vector:
					__lowercase							:   int					=     transform(29979245)
					print('''Example of four vector: ''')
					print(f'ct\' = {four_vector[0]}')
					print(f'x\' = {four_vector[1]}')
					print(f'y\' = {four_vector[2]}')
					print(f'z\' = {four_vector[3]}')

					# Substitute symbols with numerical values
					__lowercase							:   Dict					=     {ct: c, x: 1, y: 1, z: 1}
					__lowercase							:   Optional[Any]					=     [four_vector[i].subs(sub_dict) for i in range(4)]

					print(f'\n{numerical_vector}')




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    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				(  _lowercase      ,     unittest.TestCase    ):
			lowerCamelCase			:		Optional[Any]	      =			KandinskyVaaInpaintPipeline
			lowerCamelCase			:		Tuple	      =			["image_embeds", "negative_image_embeds", "image", "mask_image"]
			lowerCamelCase			:		List[str]	      =			[
			    "image_embeds",
			    "negative_image_embeds",
			    "image",
			    "mask_image",
			]
			lowerCamelCase			:		Optional[int]	      =			[
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase			:		List[Any]	      =			False

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

			@property
			def        UpperCAmelCase__ (self      ):
								return 3_2

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

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

			@property
			def        UpperCAmelCase__ (self      ):
								return 1_0_0

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

								lowerCamelCase_					:      Optional[Any]     =		{
								    '''in_channels''': 9,
								    # 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_					:      str     =		UNetaDConditionModel(**A      )
								return model

			@property
			def        UpperCAmelCase__ (self      ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "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": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def        UpperCAmelCase__ (self      ):
								torch.manual_seed(0      )
								lowerCamelCase_					:      Optional[Any]     =		VQModel(**self.dummy_movq_kwargs      )
								return model

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      List[str]     =		self.dummy_unet
								lowerCamelCase_					:      Optional[int]     =		self.dummy_movq

								lowerCamelCase_					:      Union[str, Any]     =		DDIMScheduler(
								    num_train_timesteps=1_0_0_0			,   beta_schedule='''linear'''			,   beta_start=0.0_00_85			,   beta_end=0.0_12			,   clip_sample=A			,   set_alpha_to_one=A			,   steps_offset=1			,   prediction_type='''epsilon'''			,   thresholding=A			,   )

								lowerCamelCase_					:      Dict     =		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}

								return components

			def        UpperCAmelCase__ (self			,   A			,   A=0      ):
								lowerCamelCase_					:      List[Any]     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      Tuple     =		floats_tensor((1, self.text_embedder_hidden_size)			,   rng=random.Random(seed + 1      )      ).to(
								    A      )
								# create init_image
								lowerCamelCase_					:      Dict     =		floats_tensor((1, 3, 6_4, 6_4)			,   rng=random.Random(A      )      ).to(A      )
								lowerCamelCase_					:      List[str]     =		image.cpu().permute(0			,   2			,   3			,   1      )[0]
								lowerCamelCase_					:      Any     =		Image.fromarray(np.uinta(A      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
								# create mask
								lowerCamelCase_					:      List[str]     =		np.ones((6_4, 6_4)			,   dtype=np.floataa      )
								lowerCamelCase_					:      Union[str, Any]     =		0

								if str(A      ).startswith('''mps'''      ):
													lowerCamelCase_					:      int     =		torch.manual_seed(A      )
								else:
													lowerCamelCase_					:      Union[str, Any]     =		torch.Generator(device=A      ).manual_seed(A      )
								lowerCamelCase_					:      Optional[Any]     =		{
								    '''image''': init_image,
								    '''mask_image''': mask,
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 4.0,
								    '''output_type''': '''np''',
								}
								return inputs

			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Optional[Any]     =		'''cpu'''

								lowerCamelCase_					:      str     =		self.get_dummy_components()

								lowerCamelCase_					:      Tuple     =		self.pipeline_class(**A      )
								lowerCamelCase_					:      Union[str, Any]     =		pipe.to(A      )

								pipe.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      str     =		pipe(**self.get_dummy_inputs(A      )      )
								lowerCamelCase_					:      Any     =		output.images

								lowerCamelCase_					:      Union[str, Any]     =		pipe(
								    **self.get_dummy_inputs(A      )			,   return_dict=A			,   )[0]

								lowerCamelCase_					:      List[Any]     =		image[0, -3:, -3:, -1]
								lowerCamelCase_					:      Tuple     =		image_from_tuple[0, -3:, -3:, -1]

								print(F"""image.shape {image.shape}"""      )

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_					:      Any     =		np.array(
								    [0.50_77_59_03, 0.49_52_71_95, 0.48_82_45_43, 0.50_19_22_37, 0.48_64_49_06, 0.49_37_38_14, 0.4_78_05_98, 0.47_23_48_27, 0.48_32_78_48]      )

								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()}"""






			def        UpperCAmelCase__ (self      ):
								super().test_inference_batch_single_identical(expected_max_diff=3E-3      )

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

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






			def        UpperCAmelCase__ (self      ):
								lowerCamelCase_					:      Tuple     =		load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy'''      )

								lowerCamelCase_					:      Optional[Any]     =		load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
								lowerCamelCase_					:      List[str]     =		np.ones((7_6_8, 7_6_8)			,   dtype=np.floataa      )
								lowerCamelCase_					:      int     =		0

								lowerCamelCase_					:      Union[str, Any]     =		'''a hat'''

								lowerCamelCase_					:      Optional[int]     =		KandinskyVaaPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-prior'''			,   torch_dtype=torch.floataa      )
								pipe_prior.to(A      )

								lowerCamelCase_					:      Any     =		KandinskyVaaInpaintPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''			,   torch_dtype=torch.floataa      )
								lowerCamelCase_					:      Any     =		pipeline.to(A      )
								pipeline.set_progress_bar_config(disable=A      )

								lowerCamelCase_					:      Any     =		torch.Generator(device='''cpu'''      ).manual_seed(0      )
								lowerCamelCase_,      lowerCamelCase_					:      Tuple     =		pipe_prior(
								    A			,   generator=A			,   num_inference_steps=5			,   negative_prompt=''''''			,   ).to_tuple()

								lowerCamelCase_					:      Union[str, Any]     =		pipeline(
								    image=A			,   mask_image=A			,   image_embeds=A			,   negative_image_embeds=A			,   generator=A			,   num_inference_steps=1_0_0			,   height=7_6_8			,   width=7_6_8			,   output_type='''np'''			,   )

								lowerCamelCase_					:      List[str]     =		output.images[0]

								assert image.shape == (7_6_8, 7_6_8, 3)

								assert_mean_pixel_difference(A			,   A      )


