
"""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        UpperCAmelCase_  (	snake_case__ ,			unittest.TestCase						):
		UpperCamelCase									=SpectrogramDiffusionPipeline
		UpperCamelCase									=PipelineTesterMixin.required_optional_params - {
		    '''callback''',
		    '''latents''',
		    '''callback_steps''',
		    '''output_type''',
		    '''num_images_per_prompt''',
		}
		UpperCamelCase									=False
		UpperCamelCase									=False
		UpperCamelCase									=TOKENS_TO_AUDIO_GENERATION_PARAMS
		UpperCamelCase									=TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					torch.manual_seed(0   )
					__lowercase	:       Dict				=   SpectrogramNotesEncoder(
					    max_length=20_48							,		vocab_size=15_36							,		d_model=7_68							,		dropout_rate=0.1							,		num_layers=1							,		num_heads=1							,		d_kv=4							,		d_ff=20_48							,		feed_forward_proj='''gated-gelu'''							,		)

					__lowercase	:       Optional[int]				=   SpectrogramContEncoder(
					    input_dims=1_28							,		targets_context_length=2_56							,		d_model=7_68							,		dropout_rate=0.1							,		num_layers=1							,		num_heads=1							,		d_kv=4							,		d_ff=20_48							,		feed_forward_proj='''gated-gelu'''							,		)

					__lowercase	:       Optional[Any]				=   TaFilmDecoder(
					    input_dims=1_28							,		targets_length=2_56							,		max_decoder_noise_time=2_00_00.0							,		d_model=7_68							,		num_layers=1							,		num_heads=1							,		d_kv=4							,		d_ff=20_48							,		dropout_rate=0.1							,		)

					__lowercase	:       Dict				=   DDPMScheduler()

					__lowercase	:       List[str]				=   {
					    'notes_encoder': notes_encoder.eval(),
					    'continuous_encoder': continuous_encoder.eval(),
					    'decoder': decoder.eval(),
					    'scheduler': scheduler,
					    'melgan': None,
					}
					return components
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_=0   )		->   Any:
					if str(_A   ).startswith('''mps'''   ):
								__lowercase	:       Optional[int]				=   torch.manual_seed(_A   )
					else:
								__lowercase	:       Optional[int]				=   torch.Generator(device=_A   ).manual_seed(_A   )
					__lowercase	:       Dict				=   {
					    'input_tokens': [
					        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
					    ],
					    'generator': generator,
					    'num_inference_steps': 4,
					    'output_type': 'mel',
					}
					return inputs
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       List[Any]				=   'cpu'  # ensure determinism for the device-dependent torch.Generator
					__lowercase	:       Dict				=   self.get_dummy_components()
					__lowercase	:       List[Any]				=   SpectrogramDiffusionPipeline(**_A   )
					__lowercase	:       List[Any]				=   pipe.to(_A   )
					pipe.set_progress_bar_config(disable=_A   )

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

					__lowercase	:       Dict				=   mel[0, -3:, -3:]

					assert mel_slice.shape == (3, 3)
					__lowercase	:       str				=   np.array(
					    [-11.51_29_25, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -10.53_91_47, -10.97_09_63, -9.0_9_1_6_3_4, 4.0, 4.0]   )
					assert np.abs(mel_slice.flatten() - expected_slice   ).max() < 1E-2
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Dict:
					return super().test_save_load_local()
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					return super().test_dict_tuple_outputs_equivalent()
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					return super().test_save_load_optional_components()
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Any:
					return super().test_attention_slicing_forward_pass()
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					pass
		def 					_lowerCamelCase    (		self   )		->   Any:
					pass
		@skip_mps
		def 					_lowerCamelCase    (		self   )		->   Union[str, Any]:
					return super().test_progress_bar()


@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					# clean up the VRAM after each test
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					# TODO - test that pipeline can decode tokens in a callback
					# so that music can be played live
					__lowercase	:       List[Any]				=   torch_device

					__lowercase	:       Union[str, Any]				=   SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''   )
					__lowercase	:       str				=   pipe.melgan
					__lowercase	:       Optional[int]				=   None

					__lowercase	:       Optional[int]				=   pipe.to(_A   )
					pipe.set_progress_bar_config(disable=_A   )

					def callback(UpperCamelCase_							,		UpperCamelCase_   ):
								# decode mel to audio
								__lowercase	:       Union[str, Any]				=   melgan(input_features=mel_output.astype(np.floataa   )   )[0]
								assert len(audio[0]   ) == 8_19_20 * (step + 1)
								# simulate that audio is played
								return audio

					__lowercase	:       List[str]				=   MidiProcessor()
					__lowercase	:       Any				=   processor(_A   )

					__lowercase	:       List[Any]				=   input_tokens[:3]
					__lowercase	:       Optional[int]				=   torch.manual_seed(0   )
					pipe(_A							,		num_inference_steps=5							,		generator=_A							,		callback=_A							,		output_type='''mel'''   )
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       str				=   torch_device

					__lowercase	:       Union[str, Any]				=   SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''   )
					__lowercase	:       Optional[int]				=   pipe.to(_A   )
					pipe.set_progress_bar_config(disable=_A   )
					__lowercase	:       Tuple				=   MidiProcessor()

					__lowercase	:       int				=   processor(_A   )
					# just run two denoising loops
					__lowercase	:       Union[str, Any]				=   input_tokens[:2]

					__lowercase	:       Dict				=   torch.manual_seed(0   )
					__lowercase	:       Any				=   pipe(_A							,		num_inference_steps=2							,		generator=_A   )

					__lowercase	:       Any				=   output.audios[0]

					assert abs(np.abs(_A   ).sum() - 36_12.8_41   ) < 1E-1
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       List[Any]				=   torch_device

					__lowercase	:       Tuple				=   SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion'''   )
					__lowercase	:       Union[str, Any]				=   pipe.to(_A   )
					pipe.set_progress_bar_config(disable=_A   )

					__lowercase	:       Union[str, Any]				=   MidiProcessor()

					__lowercase	:       List[str]				=   processor(_A   )

					# just run 4 denoising loops
					__lowercase	:       Optional[int]				=   input_tokens[:4]

					__lowercase	:       List[Any]				=   torch.manual_seed(0   )
					__lowercase	:       List[Any]				=   pipe(_A							,		num_inference_steps=1_00							,		generator=_A   )

					__lowercase	:       Union[str, Any]				=   output.audios[0]
					assert abs(np.abs(_A   ).sum() - 93_89.11_11   ) < 5E-2









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
			# Adjacency List of graph
			print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



