




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import SwinaSRImageProcessor
		from transformers.image_transforms import get_image_size




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				def __init__( self							:	Union[str, Any]   , __lowerCamelCase							:	List[str]   , __lowerCamelCase							:	List[str]=7   , __lowerCamelCase							:	int=3   , __lowerCamelCase							:	Any=18   , __lowerCamelCase							:	Optional[int]=30   , __lowerCamelCase							:	Union[str, Any]=4_00   , __lowerCamelCase							:	Optional[int]=True   , __lowerCamelCase							:	List[str]=1 / 2_55   , __lowerCamelCase							:	Any=True   , __lowerCamelCase							:	Optional[int]=8   , )   ->	Optional[Any]:
							a       =     parent
							a       =     batch_size
							a       =     num_channels
							a       =     image_size
							a       =     min_resolution
							a       =     max_resolution
							a       =     do_rescale
							a       =     rescale_factor
							a       =     do_pad
							a       =     pad_size





				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Optional[int]:
							return {
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							    "pad_size": self.pad_size,
							}





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	Optional[int]=False   , __lowerCamelCase							:	Any=False   , __lowerCamelCase							:	List[Any]=False				)   ->	List[Any]:
							assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

							if equal_resolution:
										a       =     []
										for i in range(self.batch_size				):
													image_inputs.append(
													    np.random.randint(
													        2_55   , size=(self.num_channels, self.max_resolution, self.max_resolution)   , dtype=np.uinta				)				)
							else:
										a       =     []
										for i in range(self.batch_size				):
													a       ,						a       =     np.random.choice(np.arange(self.min_resolution   , self.max_resolution				)   , 2				)
													image_inputs.append(np.random.randint(2_55   , size=(self.num_channels, width, height)   , dtype=np.uinta				)				)

							if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										a       =     [Image.fromarray(np.moveaxis(__lowerCamelCase   , 0   , -1				)				) for x in image_inputs]

							if torchify:
										a       =     [torch.from_numpy(__lowerCamelCase				) for x in image_inputs]

							return image_inputs




@require_torch
@require_vision
class        snake_case__				(_UpperCamelCase       ,			unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[Any]									=   SwinaSRImageProcessor if is_vision_available() else None





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     SwinaSRImageProcessingTester(self				)





				@property
				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	List[Any]:
							return self.image_processor_tester.prepare_image_processor_dict()





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	Tuple:
							a       =     self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_rescale"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "rescale_factor"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "do_pad"				)				)
							self.assertTrue(hasattr(__lowerCamelCase   , "pad_size"				)				)





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





				def 				__UpperCAmelCase				( self							:	int   , __lowerCamelCase							:	int				)   ->	Dict:
							a       ,						a       =     get_image_size(__lowerCamelCase				)
							a       =     self.image_processor_tester.pad_size

							a       =     (old_height // size + 1) * size - old_height
							a       =     (old_width // size + 1) * size - old_width
							return old_height + pad_height, old_width + pad_width





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , Image.Image				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(np.array(image_inputs[0]				)				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Union[str, Any]:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , numpify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , np.ndarray				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	int:
							# Initialize image_processor
							a       =     self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							a       =     self.image_processor_tester.prepare_inputs(equal_resolution=__lowerCamelCase   , torchify=__lowerCamelCase				)
							for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase   , torch.Tensor				)

							# Test not batched input
							a       =     image_processor(image_inputs[0]   , return_tensors="pt"				).pixel_values
							a       ,						a       =     self.calculate_expected_size(image_inputs[0]				)
							self.assertEqual(
							    encoded_images.shape   , (
							        1,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   , )












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()


__lowerCAmelCase						:							Optional[int]			     =						'./tests/fixtures/elise_format0.mid'




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



				SCREAMING_SNAKE_CASE_			:				Tuple									=   SpectrogramDiffusionPipeline
				SCREAMING_SNAKE_CASE_			:				Dict									=   PipelineTesterMixin.required_optional_params - {
				    """callback""",
				    """latents""",
				    """callback_steps""",
				    """output_type""",
				    """num_images_per_prompt""",
				}
				SCREAMING_SNAKE_CASE_			:				int									=   False
				SCREAMING_SNAKE_CASE_			:				Tuple									=   False
				SCREAMING_SNAKE_CASE_			:				str									=   TOKENS_TO_AUDIO_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE_			:				List[str]									=   TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS





				def 				__UpperCAmelCase				( self							:	Dict				)   ->	str:
							torch.manual_seed(0				)
							a       =     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"   , )

							a       =     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"   , )

							a       =     TaFilmDecoder(
							    input_dims=1_28   , targets_length=2_56   , max_decoder_noise_time=20_000.0   , d_model=7_68   , num_layers=1   , num_heads=1   , d_kv=4   , d_ff=20_48   , dropout_rate=0.1   , )

							a       =     DDPMScheduler()

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





				def 				__UpperCAmelCase				( self							:	Any   , __lowerCamelCase							:	Optional[Any]   , __lowerCamelCase							:	Union[str, Any]=0				)   ->	Dict:
							if str(__lowerCamelCase				).startswith("mps"				):
										a       =     torch.manual_seed(__lowerCamelCase				)
							else:
										a       =     torch.Generator(device=__lowerCamelCase				).manual_seed(__lowerCamelCase				)
							a       =     {
							    "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 				__UpperCAmelCase				( self							:	Dict				)   ->	Union[str, Any]:
							a       =     "cpu"  # ensure determinism for the device-dependent torch.Generator
							a       =     self.get_dummy_components()
							a       =     SpectrogramDiffusionPipeline(**__lowerCamelCase				)
							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							a       =     self.get_dummy_inputs(__lowerCamelCase				)
							a       =     pipe(**__lowerCamelCase				)
							a       =     output.audios

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

							assert mel_slice.shape == (3, 3)
							a       =     np.array(
							    [-11.512_925, -4.788_215, -0.46_172_905, -2.051_715, -10.539_147, -10.970_963, -9.091_634, 4.0, 4.0]				)
							assert np.abs(mel_slice.flatten() - expected_slice				).max() < 1e-2





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[Any]				)   ->	Tuple:
							return super().test_save_load_local()





				@skip_mps
				def 				__UpperCAmelCase				( self							:	str				)   ->	List[Any]:
							return super().test_dict_tuple_outputs_equivalent()





				@skip_mps
				def 				__UpperCAmelCase				( self							:	int				)   ->	Any:
							return super().test_save_load_optional_components()





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





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





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Tuple:
							pass





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




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



				def 				__UpperCAmelCase				( self							:	Union[str, Any]				)   ->	Dict:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





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

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

							a       =     pipe.to(__lowerCamelCase				)
							pipe.set_progress_bar_config(disable=__lowerCamelCase				)

							def callback(__lowerCamelCase							:	List[str]   , __lowerCamelCase							:	Union[str, Any]				):
										# decode mel to audio
										a       =     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

							a       =     MidiProcessor()
							a       =     processor(__lowerCamelCase				)

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





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Union[str, Any]:
							a       =     torch_device

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

							a       =     processor(__lowerCamelCase				)
							# just run two denoising loops
							a       =     input_tokens[:2]

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=2   , generator=__lowerCamelCase				)

							a       =     output.audios[0]

							assert abs(np.abs(__lowerCamelCase				).sum() - 3_612.841				) < 1e-1





				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	str:
							a       =     torch_device

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

							a       =     MidiProcessor()

							a       =     processor(__lowerCamelCase				)

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

							a       =     torch.manual_seed(0				)
							a       =     pipe(__lowerCamelCase   , num_inference_steps=1_00   , generator=__lowerCamelCase				)

							a       =     output.audios[0]
							assert abs(np.abs(__lowerCamelCase				).sum() - 9_389.1_111				) < 5e-2






