

from __future__ import annotations

from collections.abc import Callable

def 						UpperCamelCase (lowercase_:			Callable[[int | float], int | float]   ,				lowercase_:			int | float   ,				lowercase_:			int | float   ,				lowercase_:			int = 100   ,				)					->				Optional[int]:
	A__							:   Any					       =		x_start
	A__							:   Union[str, Any]					       =		fnc(lowercase_ )
	A__							:   List[Any]					       =		0.0
	for _ in range(lowercase_ ):
		# Approximates small segments of curve as linear and solve
		# for trapezoidal area
		A__							:   List[Any]					       =		(x_end - x_start) / steps + xa
		A__							:   Any					       =		fnc(lowercase_ )
		area += abs(fxa + fxa ) * (xa - xa) / 2
		# Increment step
		A__							:   Dict					       =		xa
		A__							:   str					       =		fxa
	return area


if __name__ == "__main__":

	def 						UpperCamelCase (lowercase_:			Union[str, Any] )					->				List[str]:
		return x**3 + x**2

	print('f(x) = x^3 + x^2')
	print('The area between the curve, x = -5, x = 5 and the x axis is:')
	A_	:			Union[str, Any]  	=      10
	while i <= 10_0000:
		print(f'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
		i *= 10









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    _a						(__magic_name__		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Any  							=	KandinskyVaaImgaImgPipeline
					UpperCAmelCase__:       Optional[Any]  							=	['''image_embeds''', '''negative_image_embeds''', '''image''']
					UpperCAmelCase__:       str  							=	[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					UpperCAmelCase__:       int  							=	[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					UpperCAmelCase__:       Union[str, Any]  							=	False






					@property
					def   __A      (			self      ):
						return 32






					@property
					def   __A      (			self      ):
						return 32






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






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






					@property
					def   __A      (			self      ):
						return 100






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

						A__							:   Dict					       =		{
						    """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,
						}

						A__							:   List[str]					       =		UNetaDConditionModel(**A__      )
						return model






					@property
					def   __A      (			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   __A      (			self      ):
						torch.manual_seed(0      )
						A__							:   Tuple					       =		VQModel(**self.dummy_movq_kwargs      )
						return model






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.dummy_unet
						A__							:   Dict					       =		self.dummy_movq

						A__							:   List[Any]					       =		{
						    """num_train_timesteps""": 1000,
						    """beta_schedule""": """linear""",
						    """beta_start""": 0.0_0_0_8_5,
						    """beta_end""": 0.0_1_2,
						    """clip_sample""": False,
						    """set_alpha_to_one""": False,
						    """steps_offset""": 0,
						    """prediction_type""": """epsilon""",
						    """thresholding""": False,
						}

						A__							:   List[str]					       =		DDIMScheduler(**A__      )

						A__							:   List[str]					       =		{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






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

						if str(A__      ).startswith("""mps"""      ):
							A__							:   Any					       =		torch.manual_seed(A__      )
						else:
							A__							:   List[Any]					       =		torch.Generator(device=A__      ).manual_seed(A__      )
						A__							:   Optional[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   __A      (			self      ):
						A__							:   str					       =		"""cpu"""

						A__							:   Any					       =		self.get_dummy_components()

						A__							:   Union[str, Any]					       =		self.pipeline_class(**A__      )
						A__							:   List[str]					       =		pipe.to(A__      )

						pipe.set_progress_bar_config(disable=A__      )

						A__							:   Dict					       =		pipe(**self.get_dummy_inputs(A__      )      )
						A__							:   Any					       =		output.images

						A__							:   List[str]					       =		pipe(
						    **self.get_dummy_inputs(A__      )   ,	return_dict=A__   ,	)[0]

						A__							:   Optional[int]					       =		image[0, -3:, -3:, -1]
						A__							:   str					       =		image_from_tuple[0, -3:, -3:, -1]

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

						A__							:   str					       =		np.array(
						    [0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3]      )
						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   __A      (			self      ):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinskyv22/kandinskyv22_img2img_frog.npy"""      )

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

						A__							:   int					       =		KandinskyVaaPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-prior"""   ,	torch_dtype=torch.floataa      )
						pipe_prior.to(A__      )

						A__							:   List[Any]					       =		KandinskyVaaImgaImgPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-2-decoder"""   ,	torch_dtype=torch.floataa      )
						A__							:   List[str]					       =		pipeline.to(A__      )

						pipeline.set_progress_bar_config(disable=A__      )

						A__							:   Tuple					       =		torch.Generator(device="""cpu"""      ).manual_seed(0      )
						A__       ,       A__							:   Optional[Any]					       =		pipe_prior(
						    A__   ,	generator=A__   ,	num_inference_steps=5   ,	negative_prompt=""""""   ,	).to_tuple()

						A__							:   str					       =		pipeline(
						    image=A__   ,	image_embeds=A__   ,	negative_image_embeds=A__   ,	generator=A__   ,	num_inference_steps=100   ,	height=768   ,	width=768   ,	strength=0.2   ,	output_type="""np"""   ,	)

						A__							:   Optional[int]					       =		output.images[0]

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

						assert_mean_pixel_difference(A__   ,	A__      )



