



'''simple docstring'''
def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[Any]			    =       len(UpperCAmelCase_							)
						print('The following activities are selected:'							)

						# The first activity is always selected
						_UpperCamelCase :		Dict			    =       0
						print(UpperCAmelCase_  ,  end=','							)

						# Consider rest of the activities
						for j in range(UpperCAmelCase_							):
												# If this activity has start time greater than
												# or equal to the finish time of previously
												# selected activity, then select it
												if start[j] >= finish[i]:
																		print(UpperCAmelCase_  ,  end=','							)
																		_UpperCamelCase :		str			    =       j


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					snake_case_			:     Any  	=  [1, 3, 0, 5, 8, 5]
					snake_case_			:     Dict  	=  [2, 4, 6, 7, 9, 9]
					print_max_activities(start, finish)







'''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__     (							lowercase						, unittest.TestCase       ):
	lowercase__      =      DanceDiffusionPipeline
	lowercase__      =      UNCONDITIONAL_AUDIO_GENERATION_PARAMS
	lowercase__      =      PipelineTesterMixin.required_optional_params - {
	    """callback""",
	    """latents""",
	    """callback_steps""",
	    """output_type""",
	    """num_images_per_prompt""",
	}
	lowercase__      =      UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
	lowercase__      =      False
	lowercase__      =      False





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		str			    =       UNetaDModel(
							    block_out_channels=(32, 32, 64)			,extra_in_channels=16			,sample_size=512			,sample_rate=16000			,in_channels=2			,out_channels=2			,flip_sin_to_cos=lowerCamelCase__			,use_timestep_embedding=lowerCamelCase__			,time_embedding_type='fourier'			,mid_block_type='UNetMidBlock1D'			,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')			,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')			,)
							_UpperCamelCase :		int			    =       IPNDMScheduler()

							_UpperCamelCase :		List[str]			    =       {
							    'unet': unet,
							    'scheduler': scheduler,
							}
							return components





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    int=0			):







							'''simple docstring'''
							if str(lowerCamelCase__			).startswith('mps'			):
													_UpperCamelCase :		Union[str, Any]			    =       torch.manual_seed(lowerCamelCase__			)
							else:
													_UpperCamelCase :		str			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		str			    =       {
							    'batch_size': 1,
							    'generator': generator,
							    'num_inference_steps': 4,
							}
							return inputs





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase :		List[str]			    =       self.get_dummy_components()
							_UpperCamelCase :		int			    =       DanceDiffusionPipeline(**lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       self.get_dummy_inputs(lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       pipe(**lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       output.audios

							_UpperCamelCase :		List[str]			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							_UpperCamelCase :		Dict			    =       np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]			)
							assert np.abs(audio_slice.flatten() - expected_slice			).max() < 1E-2





	@skip_mps
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return super().test_save_load_local()





	@skip_mps
	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)





	@skip_mps
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return super().test_save_load_optional_components()





	@skip_mps
	def      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							return super().test_attention_slicing_forward_pass()





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=3E-3			)






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





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       torch_device

							_UpperCamelCase :		Dict			    =       DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'			)
							_UpperCamelCase :		str			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		int			    =       torch.manual_seed(0			)
							_UpperCamelCase :		Optional[Any]			    =       pipe(generator=lowerCamelCase__			,num_inference_steps=100			,audio_length_in_s=4.0_9_6			)
							_UpperCamelCase :		Optional[int]			    =       output.audios

							_UpperCamelCase :		int			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							_UpperCamelCase :		Tuple			    =       np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]			)

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





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       torch_device

							_UpperCamelCase :		int			    =       DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'			,torch_dtype=torch.floataa			)
							_UpperCamelCase :		Any			    =       pipe.to(lowerCamelCase__			)
							pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Union[str, Any]			    =       torch.manual_seed(0			)
							_UpperCamelCase :		Union[str, Any]			    =       pipe(generator=lowerCamelCase__			,num_inference_steps=100			,audio_length_in_s=4.0_9_6			)
							_UpperCamelCase :		Any			    =       output.audios

							_UpperCamelCase :		str			    =       audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							_UpperCamelCase :		Any			    =       np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]			)

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


