





"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[Any]										=  DanceDiffusionPipeline
			__lowerCAmelCase     :				Any										=  UNCONDITIONAL_AUDIO_GENERATION_PARAMS
			__lowerCAmelCase     :				List[str]										=  PipelineTesterMixin.required_optional_params - {
			    """callback""",
			    """latents""",
			    """callback_steps""",
			    """output_type""",
			    """num_images_per_prompt""",
			}
			__lowerCAmelCase     :				List[str]										=  UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
			__lowerCAmelCase     :				int										=  False
			__lowerCAmelCase     :				List[Any]										=  False







			def   _a      (      self    ):






									'''simple docstring'''







									torch.manual_seed(0    )
									UpperCamelCase				:		List[str]				   =			UNetaDModel(
									    block_out_channels=(3_2, 3_2, 6_4)						,   extra_in_channels=1_6						,   sample_size=5_1_2						,   sample_rate=1_6_0_0_0						,   in_channels=2						,   out_channels=2						,   flip_sin_to_cos=_A						,   use_timestep_embedding=_A						,   time_embedding_type="""fourier"""						,   mid_block_type="""UNetMidBlock1D"""						,   down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""")						,   up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""")						,   )
									UpperCamelCase				:		Tuple				   =			IPNDMScheduler()

									UpperCamelCase				:		int				   =			{
									    """unet""": unet,
									    """scheduler""": scheduler,
									}
									return components







			def   _a      (      self						,   _A						,   _A=0    ):






									'''simple docstring'''







									if str(_A    ).startswith("""mps"""    ):
															UpperCamelCase				:		Tuple				   =			torch.manual_seed(_A    )
									else:
															UpperCamelCase				:		Union[str, Any]				   =			torch.Generator(device=_A    ).manual_seed(_A    )
									UpperCamelCase				:		List[Any]				   =			{
									    """batch_size""": 1,
									    """generator""": generator,
									    """num_inference_steps""": 4,
									}
									return inputs







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									UpperCamelCase				:		Optional[int]				   =			self.get_dummy_components()
									UpperCamelCase				:		Optional[int]				   =			DanceDiffusionPipeline(**_A    )
									UpperCamelCase				:		List[Any]				   =			pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		str				   =			self.get_dummy_inputs(_A    )
									UpperCamelCase				:		Any				   =			pipe(**_A    )
									UpperCamelCase				:		List[str]				   =			output.audios

									UpperCamelCase				:		Dict				   =			audio[0, -3:, -3:]

									assert audio.shape == (1, 2, components["unet"].sample_size)
									UpperCamelCase				:		Any				   =			np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]    )
									assert np.abs(audio_slice.flatten() - expected_slice    ).max() < 1e-2







			@skip_mps
			def   _a      (      self    ):






									'''simple docstring'''







									return super().test_save_load_local()







			@skip_mps
			def   _a      (      self    ):






									'''simple docstring'''







									return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3    )







			@skip_mps
			def   _a      (      self    ):






									'''simple docstring'''







									return super().test_save_load_optional_components()







			@skip_mps
			def   _a      (      self    ):






									'''simple docstring'''







									return super().test_attention_slicing_forward_pass()







			def   _a      (      self    ):






									'''simple docstring'''







									super().test_inference_batch_single_identical(expected_max_diff=3e-3    )






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

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			torch_device

									UpperCamelCase				:		str				   =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""    )
									UpperCamelCase				:		Optional[int]				   =			pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		List[str]				   =			torch.manual_seed(0    )
									UpperCamelCase				:		Optional[Any]				   =			pipe(generator=_A						,   num_inference_steps=1_0_0						,   audio_length_in_s=4.0_96    )
									UpperCamelCase				:		Union[str, Any]				   =			output.audios

									UpperCamelCase				:		Dict				   =			audio[0, -3:, -3:]

									assert audio.shape == (1, 2, pipe.unet.sample_size)
									UpperCamelCase				:		Any				   =			np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]    )

									assert np.abs(audio_slice.flatten() - expected_slice    ).max() < 1e-2







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			torch_device

									UpperCamelCase				:		Any				   =			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""						,   torch_dtype=torch.floataa    )
									UpperCamelCase				:		Union[str, Any]				   =			pipe.to(_A    )
									pipe.set_progress_bar_config(disable=_A    )

									UpperCamelCase				:		Any				   =			torch.manual_seed(0    )
									UpperCamelCase				:		Union[str, Any]				   =			pipe(generator=_A						,   num_inference_steps=1_0_0						,   audio_length_in_s=4.0_96    )
									UpperCamelCase				:		int				   =			output.audios

									UpperCamelCase				:		Optional[int]				   =			audio[0, -3:, -3:]

									assert audio.shape == (1, 2, pipe.unet.sample_size)
									UpperCamelCase				:		Dict				   =			np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]    )

									assert np.abs(audio_slice.flatten() - expected_slice    ).max() < 1e-2










"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			x
						UpperCamelCase				:		str				   =			y
						for step in range(SCREAMING_SNAKE_CASE     ):  # noqa: B007
												UpperCamelCase				:		str				   =			a * a - b * b + x
												UpperCamelCase				:		Any				   =			2 * a * b + y
												UpperCamelCase				:		List[str]				   =			a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255     ) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE							,     1							,     1     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 800							,     SCREAMING_SNAKE_CASE = 600							,     SCREAMING_SNAKE_CASE = -0.6							,     SCREAMING_SNAKE_CASE = 0							,     SCREAMING_SNAKE_CASE = 3.2							,     SCREAMING_SNAKE_CASE = 50							,     SCREAMING_SNAKE_CASE = True							,     ):
						UpperCamelCase				:		Tuple				   =			Image.new("""RGB"""							,     (image_width, image_height)     )
						UpperCamelCase				:		Dict				   =			img.load()

						# loop through the image-coordinates
						for image_x in range(SCREAMING_SNAKE_CASE     ):
												for image_y in range(SCREAMING_SNAKE_CASE     ):
																		# determine the figure-coordinates based on the image-coordinates
																		UpperCamelCase				:		Dict				   =			figure_width / image_width * image_height
																		UpperCamelCase				:		Tuple				   =			figure_center_x + (image_x / image_width - 0.5) * figure_width
																		UpperCamelCase				:		Dict				   =			figure_center_y + (image_y / image_height - 0.5) * figure_height

																		UpperCamelCase				:		Union[str, Any]				   =			get_distance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								UpperCamelCase				:		int				   =			get_color_coded_rgb(SCREAMING_SNAKE_CASE     )
																		else:
																								UpperCamelCase				:		Optional[int]				   =			get_black_and_white_rgb(SCREAMING_SNAKE_CASE     )

						return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__magic_name__       :					int						=			get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()



