






"""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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





		"""simple docstring"""






		_lowerCamelCase			  =       DanceDiffusionPipeline
		_lowerCamelCase			  =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
		_lowerCamelCase			  =       PipelineTesterMixin.required_optional_params - {
		    '''callback''',
		    '''latents''',
		    '''callback_steps''',
		    '''output_type''',
		    '''num_images_per_prompt''',
		}
		_lowerCamelCase			  =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
		_lowerCamelCase			  =       False
		_lowerCamelCase			  =       False





		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : 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=__UpperCamelCase							,    use_timestep_embedding=__UpperCamelCase							,    time_embedding_type="""fourier"""							,    mid_block_type="""UNetMidBlock1D"""							,    down_block_types=("""DownBlock1DNoSkip""", """DownBlock1D""", """AttnDownBlock1D""")							,    up_block_types=("""AttnUpBlock1D""", """UpBlock1D""", """UpBlock1DNoSkip""")							,    )
								snake_case_   : Dict						=			IPNDMScheduler()

								snake_case_   : Tuple						=			{
								    """unet""": unet,
								    """scheduler""": scheduler,
								}
								return components





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase=0   ) ->						Tuple:







								'''simple docstring'''

								if str(__UpperCamelCase   ).startswith("""mps"""   ):
														snake_case_   : Dict						=			torch.manual_seed(__UpperCamelCase   )
								else:
														snake_case_   : Tuple						=			torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
								snake_case_   : Optional[int]						=			{
								    """batch_size""": 1,
								    """generator""": generator,
								    """num_inference_steps""": 4,
								}
								return inputs





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

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

								snake_case_   : Optional[Any]						=			self.get_dummy_inputs(__UpperCamelCase   )
								snake_case_   : str						=			pipe(**__UpperCamelCase   )
								snake_case_   : Dict						=			output.audios

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

								assert audio.shape == (1, 2, components["unet"].sample_size)
								snake_case_   : str						=			np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]   )
								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   ) ->						str:







								'''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   ) ->						Tuple:







								'''simple docstring'''

								return super().test_attention_slicing_forward_pass()





		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

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




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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								snake_case_   : Tuple						=			torch_device

								snake_case_   : Union[str, Any]						=			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""   )
								snake_case_   : List[Any]						=			pipe.to(__UpperCamelCase   )
								pipe.set_progress_bar_config(disable=__UpperCamelCase   )

								snake_case_   : Optional[Any]						=			torch.manual_seed(0   )
								snake_case_   : Optional[Any]						=			pipe(generator=__UpperCamelCase							,    num_inference_steps=1_0_0							,    audio_length_in_s=4.096   )
								snake_case_   : Optional[int]						=			output.audios

								snake_case_   : Union[str, Any]						=			audio[0, -3:, -3:]

								assert audio.shape == (1, 2, pipe.unet.sample_size)
								snake_case_   : Optional[int]						=			np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]   )

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





		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								snake_case_   : List[str]						=			torch_device

								snake_case_   : Any						=			DanceDiffusionPipeline.from_pretrained("""harmonai/maestro-150k"""							,    torch_dtype=torch.floataa   )
								snake_case_   : Optional[int]						=			pipe.to(__UpperCamelCase   )
								pipe.set_progress_bar_config(disable=__UpperCamelCase   )

								snake_case_   : List[Any]						=			torch.manual_seed(0   )
								snake_case_   : str						=			pipe(generator=__UpperCamelCase							,    num_inference_steps=1_0_0							,    audio_length_in_s=4.096   )
								snake_case_   : int						=			output.audios

								snake_case_   : List[Any]						=			audio[0, -3:, -3:]

								assert audio.shape == (1, 2, pipe.unet.sample_size)
								snake_case_   : List[Any]						=			np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]   )

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






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			int					, __UpperCamelCase      :			bool = False    ):


						'''simple docstring'''






						if n == 2:
												return True
						if not n % 2 or n < 2:
												return False
						if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
												return False
						if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
												raise ValueError(
												    """Warning: upper bound of deterministic test is exceeded. """
												    """Pass allow_probable=True to allow probabilistic test. """
												    """A return value of True indicates a probable prime."""    )
						# array bounds provided by analysis
						snake_case_   : List[Any]						=			[
						    2_0_4_7,
						    1_3_7_3_6_5_3,
						    2_5_3_2_6_0_0_1,
						    3_2_1_5_0_3_1_7_5_1,
						    2_1_5_2_3_0_2_8_9_8_7_4_7,
						    3_4_7_4_7_4_9_6_6_0_3_8_3,
						    3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
						    1,
						    3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
						    1,
						    1,
						    3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
						    3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
						]

						snake_case_   : Dict						=			[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
						for idx, _p in enumerate(__UpperCamelCase					, 1    ):
												if n < _p:
																		# then we have our last prime to check
																		snake_case_   : Optional[int]						=			primes[:idx]
																		break
						snake_case_			,		snake_case_   : Tuple						=			n - 1, 0
						# break up n -1 into a power of 2 (s) and
						# remaining odd component
						# essentially, solve for d * 2 ** s == n - 1
						while d % 2 == 0:
												d //= 2
												s += 1
						for prime in plist:
												snake_case_   : List[str]						=			False
												for r in range(__UpperCamelCase    ):
																		snake_case_   : int						=			pow(__UpperCamelCase					, d * 2**r					, __UpperCamelCase    )
																		# see article for analysis explanation for m
																		if (r == 0 and m == 1) or ((m + 1) % n == 0):
																								snake_case_   : Optional[Any]						=			True
																								# this loop will not determine compositeness
																								break
												if pr:
																		continue
												# if pr is False, then the above loop never evaluated to true,
												# and the n MUST be composite
												return False
						return True
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						assert not miller_rabin(5_6_1    )
						assert miller_rabin(5_6_3    )
						# 2047

						assert not miller_rabin(8_3_8_2_0_1    )
						assert miller_rabin(8_3_8_2_0_7    )
						# 1_373_653

						assert not miller_rabin(1_7_3_1_6_0_0_1    )
						assert miller_rabin(1_7_3_1_6_0_1_7    )
						# 25_326_001

						assert not miller_rabin(3_0_7_8_3_8_6_6_4_1    )
						assert miller_rabin(3_0_7_8_3_8_6_6_5_3    )
						# 3_215_031_751

						assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1    )
						assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9    )
						# 2_152_302_898_747

						assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7    )
						assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7    )
						# 3_474_749_660_383

						assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1    )
						assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7    )
						# 341_550_071_728_321

						assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1    )
						assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1    )
						# 3_825_123_056_546_413_051

						assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7    )
						assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1    )
						# 318_665_857_834_031_151_167_461

						assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3    )
						assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9    )
						# 3_317_044_064_679_887_385_961_981
						# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()

