


'''simple docstring'''

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
							import librosa  # noqa: E402

							lowerCAmelCase   :Any								=    True
							lowerCAmelCase   :Tuple								=    ''''''
except Exception as e:
							lowerCAmelCase   :Tuple								=    False
							lowerCAmelCase   :str								=    (
							    F'Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'
							)


from PIL import Image  # noqa: E402
class _lowerCamelCase (		lowercase__	,  lowercase__							):



		'''simple docstring'''







		A_			:    str   					=       """mel_config.json"""







		@register_to_config
		def __init__(      self	:		Union[str, Any]						,  _A	:		int = 256						,  _A	:		int = 256						,  _A	:		int = 22050						,  _A	:		int = 2048						,  _A	:		int = 512						,  _A	:		int = 80						,  _A	:		int = 32						,  )						->				Any:
			__magic_name__ :					Optional[int]  			=  hop_length
			__magic_name__ :					List[Any]  			=  sample_rate
			__magic_name__ :					Optional[Any]  			=  n_fft
			__magic_name__ :					Dict  			=  top_db
			__magic_name__ :					List[str]  			=  n_iter
			self.set_resolution(_A						,  _A    )
			__magic_name__ :					Tuple  			=  None

			if not _librosa_can_be_imported:
				raise ValueError(_import_error    )







		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		int						,  _A	:		int    )						->				Tuple:
			__magic_name__ :					Dict  			=  x_res
			__magic_name__ :					int  			=  y_res
			__magic_name__ :					Tuple  			=  self.y_res
			__magic_name__ :					Dict  			=  self.x_res * self.hop_length - 1







		def      __lowerCAmelCase	(      self	:		List[str]						,  _A	:		str = None						,  _A	:		np.ndarray = None    )						->				Optional[int]:
			if audio_file is not None:
				__magic_name__			,       __magic_name__ :					List[str]  			=  librosa.load(_A						,  mono=_A						,  sr=self.sr    )
			else:
				__magic_name__ :					Union[str, Any]  			=  raw_audio

			# Pad with silence if necessary.
			if len(self.audio    ) < self.x_res * self.hop_length:
				__magic_name__ :					Any  			=  np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio    ),)    )]    )







		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				int:
			return len(self.audio    ) // self.slice_size







		def      __lowerCAmelCase	(      self	:		Optional[Any]						,  _A	:		int = 0    )						->				np.ndarray:
			return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]







		def      __lowerCAmelCase	(      self	:		int    )						->				int:
			return self.sr







		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		int    )						->				Image.Image:
			__magic_name__ :					Optional[Any]  			=  librosa.feature.melspectrogram(
			    y=self.get_audio_slice(_A    )						,  sr=self.sr						,  n_fft=self.n_fft						,  hop_length=self.hop_length						,  n_mels=self.n_mels    )
			__magic_name__ :					Tuple  			=  librosa.power_to_db(_A						,  ref=np.max						,  top_db=self.top_db    )
			__magic_name__ :					List[Any]  			=  (((log_S + self.top_db) * 255 / self.top_db).clip(0						,  255    ) + 0.5).astype(np.uinta    )
			__magic_name__ :					Union[str, Any]  			=  Image.fromarray(_A    )
			return image







		def      __lowerCAmelCase	(      self	:		List[Any]						,  _A	:		Image.Image    )						->				np.ndarray:
			__magic_name__ :					List[str]  			=  np.frombuffer(image.tobytes()						,  dtype='uint8'    ).reshape((image.height, image.width)    )
			__magic_name__ :					Optional[Any]  			=  bytedata.astype('float'    ) * self.top_db / 255 - self.top_db
			__magic_name__ :					Optional[int]  			=  librosa.db_to_power(_A    )
			__magic_name__ :					Union[str, Any]  			=  librosa.feature.inverse.mel_to_audio(
			    _A						,  sr=self.sr						,  n_fft=self.n_fft						,  hop_length=self.hop_length						,  n_iter=self.n_iter    )
			return audio



'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		@property
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  1
			__magic_name__ :					str  			=  3
			__magic_name__ :					Optional[Any]  			=  (32, 32)

			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
			return image







		@property
		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
			torch.manual_seed(0    )
			__magic_name__ :					Dict  			=  UNetaDModel(
			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
			return model







		@property
		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			torch.manual_seed(0    )
			__magic_name__ :					Union[str, Any]  			=  VQModel(
			    block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,  latent_channels=3						,  )
			return model







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
			__magic_name__ :					Dict  			=  'cpu'
			__magic_name__ :					str  			=  self.dummy_uncond_unet
			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model

			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )

			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )

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







		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
			__magic_name__ :					Tuple  			=  DDIMScheduler()
			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model

			# put models in fp16
			__magic_name__ :					Optional[int]  			=  unet.half()
			__magic_name__ :					int  			=  vqvae.half()

			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					str  			=  self.dummy_image.to(_A    )

			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			assert image.shape == (1, 64, 64, 3)







@slow
@require_torch
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
			__magic_name__ :					Tuple  			=  load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool.png'    )
			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )

			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					int  			=  torch.manual_seed(0    )
			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images

			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )

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