

"""simple docstring"""




def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					A     =   len(lowercase__ )
					for i in range(lowercase__ ):
										A     =   []
										for j in range(lowercase__ ):
															A     =   0
															for k in range(lowercase__ ):
																				A     =   val + matrix_a[i][k] * matrix_b[k][j]
															list_a.append(lowercase__ )
										matrix_c.append(lowercase__ )
					return matrix_c
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					return [[int(row == column ) for column in range(lowercase__ )] for row in range(lowercase__ )]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if n <= 1:
										return n
					A     =   identity(2 )
					A     =   [[1, 1], [1, 0]]
					A     =   n - 1
					while n > 0:
										if n % 2 == 1:
															A     =   multiply(lowercase__     ,				lowercase__ )
										A     =   multiply(lowercase__     ,				lowercase__ )
										A     =   int(n / 2 )
					return res_matrix[0][0]
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					if n <= 1:
										return n
					A     =   0
					A     =   1
					for _ in range(2     ,				n + 1 ):
										A     ,       A     =   fiba, fiba + fiba
					return fiba
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
										A     =   int("".join(c for c in ordinal if c in "0123456789" ) )  # 1000th --> 1000
										print(
										    F"""{ordinal} fibonacci number using matrix exponentiation is """
										    F"""{nth_fibonacci_matrix(lowercase__ )} and using bruteforce is """
										    F"""{nth_fibonacci_bruteforce(lowercase__ )}\n""" )
					# from timeit import timeit
					# print(timeit("nth_fibonacci_matrix(1000000)",
					#              "from main import nth_fibonacci_matrix", number=5))
					# print(timeit("nth_fibonacci_bruteforce(1000000)",
					#              "from main import nth_fibonacci_bruteforce", number=5))
					# 2.3342058970001744
					# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()











'''simple docstring'''

import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


a__         =     '''./tests/fixtures/elise_format0.mid'''





@unittest.skip("""The note-seq package currently throws an error on import"""      )
class __magic_name__(						__lowerCAmelCase    ,		unittest.TestCase      ):
						UpperCAmelCase_		:					Tuple    =  SpectrogramDiffusionPipeline
						UpperCAmelCase_		:					List[str]    =  PipelineTesterMixin.required_optional_params - {
						    """callback""",
						    """latents""",
						    """callback_steps""",
						    """output_type""",
						    """num_images_per_prompt""",
						}
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					Dict    =  False
						UpperCAmelCase_		:					List[str]    =  TOKENS_TO_AUDIO_GENERATION_PARAMS
						UpperCAmelCase_		:					Union[str, Any]    =  TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									torch.manual_seed(0       )
									snake_case__          =						SpectrogramNotesEncoder(
									    max_length=2_0_4_8					,   vocab_size=1_5_3_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						SpectrogramContEncoder(
									    input_dims=1_2_8					,   targets_context_length=2_5_6					,   d_model=7_6_8					,   dropout_rate=0.1					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   feed_forward_proj="""gated-gelu"""					,   )

									snake_case__          =						TaFilmDecoder(
									    input_dims=1_2_8					,   targets_length=2_5_6					,   max_decoder_noise_time=2_00_00.0					,   d_model=7_6_8					,   num_layers=1					,   num_heads=1					,   d_kv=4					,   d_ff=2_0_4_8					,   dropout_rate=0.1					,   )

									snake_case__          =						DDPMScheduler()

									snake_case__          =						{
									    """notes_encoder""": notes_encoder.eval(),
									    """continuous_encoder""": continuous_encoder.eval(),
									    """decoder""": decoder.eval(),
									    """scheduler""": scheduler,
									    """melgan""": None,
									}
									return components






						def       __lowerCAmelCase(   self						:						Tuple					,   __UpperCamelCase						:						Any					,   __UpperCamelCase						:						str=0       ):



									'''simple docstring'''






									if str(__UpperCamelCase       ).startswith("""mps"""       ):
												snake_case__          =						torch.manual_seed(__UpperCamelCase       )
									else:
												snake_case__          =						torch.Generator(device=__UpperCamelCase       ).manual_seed(__UpperCamelCase       )
									snake_case__          =						{
									    """input_tokens""": [
									        [1_1_3_4, 9_0, 1_1_3_5, 1_1_3_3, 1_0_8_0, 1_1_2, 1_1_3_2, 1_0_8_0, 1_1_3_3, 1_0_7_9, 1_3_3, 1_1_3_2, 1_0_7_9, 1_1_3_3, 1] + [0] * 2_0_3_3
									    ],
									    """generator""": generator,
									    """num_inference_steps""": 4,
									    """output_type""": """mel""",
									}
									return inputs






						def       __lowerCAmelCase(   self						:						Optional[Any]       ):



									'''simple docstring'''






									snake_case__          =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									snake_case__          =						self.get_dummy_components()
									snake_case__          =						SpectrogramDiffusionPipeline(**__UpperCamelCase       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						self.get_dummy_inputs(__UpperCamelCase       )
									snake_case__          =						pipe(**__UpperCamelCase       )
									snake_case__          =						output.audios

									snake_case__          =						mel[0, -3:, -3:]

									assert mel_slice.shape == (3, 3)
									snake_case__          =						np.array(
									    [-11.51_29_25, -4.78_82_15, -0.46_17_29_05, -2.05_17_15, -10.53_91_47, -10.97_09_63, -9.09_16_34, 4.0, 4.0]       )
									assert np.abs(mel_slice.flatten() - expected_slice       ).max() < 1E-2






						@skip_mps
						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									return super().test_save_load_local()






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



									'''simple docstring'''






									return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Optional[int]       ):



									'''simple docstring'''






									return super().test_save_load_optional_components()






						@skip_mps
						def       __lowerCAmelCase(   self						:						Union[str, Any]       ):



									'''simple docstring'''






									return super().test_attention_slicing_forward_pass()






						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									pass






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									pass







						@skip_mps
						def       __lowerCAmelCase(   self						:						Dict       ):



									'''simple docstring'''






									return super().test_progress_bar()







@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class __magic_name__(						unittest.TestCase      ):






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						int       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.melgan
									snake_case__          =						None

									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									def callback(__UpperCamelCase						:						List[str]					,   __UpperCamelCase						:						List[str]       ):
												# decode mel to audio
												snake_case__          =						melgan(input_features=mel_output.astype(np.floataa       )       )[0]
												assert len(audio[0]       ) == 8_1_9_2_0 * (step + 1)
												# simulate that audio is played
												return audio

									snake_case__          =						MidiProcessor()
									snake_case__          =						processor(__UpperCamelCase       )

									snake_case__          =						input_tokens[:3]
									snake_case__          =						torch.manual_seed(0       )
									pipe(__UpperCamelCase					,   num_inference_steps=5					,   generator=__UpperCamelCase					,   callback=__UpperCamelCase					,   output_type="""mel"""       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )
									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )
									# just run two denoising loops
									snake_case__          =						input_tokens[:2]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=2					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]

									assert abs(np.abs(__UpperCamelCase       ).sum() - 36_12.8_41       ) < 1E-1







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



									'''simple docstring'''






									snake_case__          =						torch_device

									snake_case__          =						SpectrogramDiffusionPipeline.from_pretrained("""google/music-spectrogram-diffusion"""       )
									snake_case__          =						pipe.to(__UpperCamelCase       )
									pipe.set_progress_bar_config(disable=__UpperCamelCase       )

									snake_case__          =						MidiProcessor()

									snake_case__          =						processor(__UpperCamelCase       )

									# just run 4 denoising loops
									snake_case__          =						input_tokens[:4]

									snake_case__          =						torch.manual_seed(0       )
									snake_case__          =						pipe(__UpperCamelCase					,   num_inference_steps=1_0_0					,   generator=__UpperCamelCase       )

									snake_case__          =						output.audios[0]
									assert abs(np.abs(__UpperCamelCase       ).sum() - 93_89.11_11       ) < 5E-2