



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_       :Union[str, Any]				  =				"./tests/fixtures/elise_format0.mid"




@unittest.skip("""The note-seq package currently throws an error on import"""					)
class  snake_case__						(	lowerCAmelCase_    ,					unittest.TestCase					):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     SpectrogramDiffusionPipeline
     _SCREAMING_SNAKE_CASE            =     PipelineTesterMixin.required_optional_params - {
         """callback""",
         """latents""",
         """callback_steps""",
         """output_type""",
         """num_images_per_prompt""",
     }
     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     False
     _SCREAMING_SNAKE_CASE            =     TOKENS_TO_AUDIO_GENERATION_PARAMS
     _SCREAMING_SNAKE_CASE            =     TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS






     def        lowercase_       (	self  :  Optional[int] )       ->int:
            torch.manual_seed(0 )
            snake_case__							:     Dict            =     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__							:     int            =     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__							:     Optional[int]            =     TaFilmDecoder(
                input_dims=1_2_8,			targets_length=2_5_6,			max_decoder_noise_time=2_0_0_0_0.0,			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__							:     str            =     DDPMScheduler()

            snake_case__							:     List[str]            =     {
                'notes_encoder': notes_encoder.eval(),
                'continuous_encoder': continuous_encoder.eval(),
                'decoder': decoder.eval(),
                'scheduler': scheduler,
                'melgan': None,
            }
            return components






     def        lowercase_       (	self  :  List[Any],			_snake_case  :  Union[str, Any],			_snake_case  :  int=0 )       ->int:
            if str(_snake_case ).startswith('mps' ):
                   snake_case__							:     Dict            =     torch.manual_seed(_snake_case )
            else:
                   snake_case__							:     Optional[Any]            =     torch.Generator(device=_snake_case ).manual_seed(_snake_case )
            snake_case__							:     Any            =     {
                '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        lowercase_       (	self  :  Optional[Any] )       ->Tuple:
            snake_case__							:     Union[str, Any]            =     'cpu'  # ensure determinism for the device-dependent torch.Generator
            snake_case__							:     Any            =     self.get_dummy_components()
            snake_case__							:     List[Any]            =     SpectrogramDiffusionPipeline(**_snake_case )
            snake_case__							:     Optional[Any]            =     pipe.to(_snake_case )
            pipe.set_progress_bar_config(disable=_snake_case )

            snake_case__							:     List[str]            =     self.get_dummy_inputs(_snake_case )
            snake_case__							:     List[str]            =     pipe(**_snake_case )
            snake_case__							:     List[str]            =     output.audios

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

            assert mel_slice.shape == (3, 3)
            snake_case__							:     List[Any]            =     np.array(
                [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -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        lowercase_       (	self  :  Optional[Any] )       ->Tuple:
            return super().test_save_load_local()






     @skip_mps
     def        lowercase_       (	self  :  List[str] )       ->Optional[int]:
            return super().test_dict_tuple_outputs_equivalent()






     @skip_mps
     def        lowercase_       (	self  :  List[str] )       ->Union[str, Any]:
            return super().test_save_load_optional_components()






     @skip_mps
     def        lowercase_       (	self  :  List[Any] )       ->int:
            return super().test_attention_slicing_forward_pass()






     def        lowercase_       (	self  :  Tuple )       ->Optional[Any]:
            pass






     def        lowercase_       (	self  :  Tuple )       ->Union[str, Any]:
            pass






     @skip_mps
     def        lowercase_       (	self  :  Any )       ->Optional[int]:
            return super().test_progress_bar()




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






     """simple docstring"""


     def        lowercase_       (	self  :  int )       ->Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






     def        lowercase_       (	self  :  Dict )       ->Dict:
            # TODO - test that pipeline can decode tokens in a callback
            # so that music can be played live
            snake_case__							:     Union[str, Any]            =     torch_device

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

            snake_case__							:     Optional[Any]            =     pipe.to(_snake_case )
            pipe.set_progress_bar_config(disable=_snake_case )

            def callback(_snake_case  :  Union[str, Any],			_snake_case  :  str ):
                   # decode mel to audio
                   snake_case__							:     int            =     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__							:     int            =     MidiProcessor()
            snake_case__							:     List[Any]            =     processor(_snake_case )

            snake_case__							:     Tuple            =     input_tokens[:3]
            snake_case__							:     Optional[int]            =     torch.manual_seed(0 )
            pipe(_snake_case,			num_inference_steps=5,			generator=_snake_case,			callback=_snake_case,			output_type='mel' )






     def        lowercase_       (	self  :  Union[str, Any] )       ->Dict:
            snake_case__							:     str            =     torch_device

            snake_case__							:     List[str]            =     SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion' )
            snake_case__							:     Union[str, Any]            =     pipe.to(_snake_case )
            pipe.set_progress_bar_config(disable=_snake_case )
            snake_case__							:     Dict            =     MidiProcessor()

            snake_case__							:     List[Any]            =     processor(_snake_case )
            # just run two denoising loops
            snake_case__							:     int            =     input_tokens[:2]

            snake_case__							:     str            =     torch.manual_seed(0 )
            snake_case__							:     Optional[int]            =     pipe(_snake_case,			num_inference_steps=2,			generator=_snake_case )

            snake_case__							:     Any            =     output.audios[0]

            assert abs(np.abs(_snake_case ).sum() - 3_6_1_2.8_4_1 ) < 1e-1






     def        lowercase_       (	self  :  str )       ->int:
            snake_case__							:     int            =     torch_device

            snake_case__							:     List[Any]            =     SpectrogramDiffusionPipeline.from_pretrained('google/music-spectrogram-diffusion' )
            snake_case__							:     List[str]            =     pipe.to(_snake_case )
            pipe.set_progress_bar_config(disable=_snake_case )

            snake_case__							:     Tuple            =     MidiProcessor()

            snake_case__							:     str            =     processor(_snake_case )

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

            snake_case__							:     Tuple            =     torch.manual_seed(0 )
            snake_case__							:     Any            =     pipe(_snake_case,			num_inference_steps=1_0_0,			generator=_snake_case )

            snake_case__							:     List[str]            =     output.audios[0]
            assert abs(np.abs(_snake_case ).sum() - 9_3_8_9.1_1_1_1 ) < 5e-2











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






