






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

	a_							=      True
	a_							=      ''''''
except Exception as e:
	a_							=      False
	a_							=      (
	    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        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase		):
	a_             ="""mel_config.json"""

	@register_to_config
	def __init__(							self						,      __UpperCAmelCase = 256						,      __UpperCAmelCase = 256						,      __UpperCAmelCase = 22050						,      __UpperCAmelCase = 2048						,      __UpperCAmelCase = 512						,      __UpperCAmelCase = 80						,      __UpperCAmelCase = 32						,      )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  hop_length
							lowerCAmelCase__	 =  sample_rate
							lowerCAmelCase__	 =  n_fft
							lowerCAmelCase__	 =  top_db
							lowerCAmelCase__	 =  n_iter
							self.set_resolution(__UpperCAmelCase						,      __UpperCAmelCase    )
							lowerCAmelCase__	 =  None

							if not _librosa_can_be_imported:
													raise ValueError(_import_error    )

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







							'''simple docstring'''





							lowerCAmelCase__	 =  x_res
							lowerCAmelCase__	 =  y_res
							lowerCAmelCase__	 =  self.y_res
							lowerCAmelCase__	 =  self.x_res * self.hop_length - 1

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase = None						,      __UpperCAmelCase = None    )->   Dict:







							'''simple docstring'''





							if audio_file is not None:
													lowerCAmelCase__ ,							lowerCAmelCase__	 =  librosa.load(__UpperCAmelCase						,      mono=__UpperCAmelCase						,      sr=self.sr    )
							else:
													lowerCAmelCase__	 =  raw_audio

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

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							return len(self.audio    ) // self.slice_size

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase = 0    )->   np.ndarray:







							'''simple docstring'''





							return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							return self.sr

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Image.Image:







							'''simple docstring'''





							lowerCAmelCase__	 =  librosa.feature.melspectrogram(
							    y=self.get_audio_slice(__UpperCAmelCase    )						,      sr=self.sr						,      n_fft=self.n_fft						,      hop_length=self.hop_length						,      n_mels=self.n_mels    )
							lowerCAmelCase__	 =  librosa.power_to_db(__UpperCAmelCase						,      ref=np.max						,      top_db=self.top_db    )
							lowerCAmelCase__	 =  (((log_S + self.top_db) * 255 / self.top_db).clip(0						,      255    ) + 0.5).astype(np.uinta    )
							lowerCAmelCase__	 =  Image.fromarray(__UpperCAmelCase    )
							return image


	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   np.ndarray:







							'''simple docstring'''





							lowerCAmelCase__	 =  np.frombuffer(image.tobytes()						,      dtype="uint8"    ).reshape((image.height, image.width)    )
							lowerCAmelCase__	 =  bytedata.astype("float"    ) * self.top_db / 255 - self.top_db
							lowerCAmelCase__	 =  librosa.db_to_power(__UpperCAmelCase    )
							lowerCAmelCase__	 =  librosa.feature.inverse.mel_to_audio(
							    __UpperCAmelCase						,      sr=self.sr						,      n_fft=self.n_fft						,      hop_length=self.hop_length						,      n_iter=self.n_iter    )
							return audio














import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class        lowercase__       (     unittest.TestCase		):

	@property
	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  1
							lowerCAmelCase__	 =  3
							lowerCAmelCase__	 =  8
							lowerCAmelCase__	 =  8

							lowerCAmelCase__	 =  torch.rand((batch_size, num_channels, height, width)    )

							return sample

	def 						UpperCAmelCase					(							self						,      __UpperCAmelCase    )->   Dict:







							'''simple docstring'''





							if isinstance(image[0]						,      PIL.Image.Image    ):
													return np.stack([np.array(__UpperCAmelCase    ) for i in image]						,      axis=0    )
							elif isinstance(__UpperCAmelCase						,      torch.Tensor    ):
													return image.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							return image

	def 						UpperCAmelCase					(							self    )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(
													    image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase						,      )
													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   int:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )
							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )

													lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    )
													lowerCAmelCase__	 =  (input_np * 255).round() if output_type == "pil" else input_np
													assert (
													    np.abs(in_np - out_np    ).max() < 1E-6
													), F"decoded output does not match input for output_type {output_type}"

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample.cpu().numpy().transpose(0						,      2						,      3						,      1    )
							lowerCAmelCase__	 =  image_processor.numpy_to_pil(__UpperCAmelCase    )

							for output_type in ["pt", "np", "pil"]:
													lowerCAmelCase__	 =  image_processor.postprocess(image_processor.preprocess(__UpperCAmelCase    )						,      output_type=__UpperCAmelCase    )
													for i, o in zip(__UpperCAmelCase						,      __UpperCAmelCase    ):
																			lowerCAmelCase__	 =  np.array(__UpperCAmelCase    )
																			lowerCAmelCase__	 =  self.to_np(__UpperCAmelCase    ) if output_type == "pil" else (self.to_np(__UpperCAmelCase    ) * 255).round()
																			assert (
																			    np.abs(in_np - out_np    ).max() < 1E-6
																			), F"decoded output does not match input for output_type {output_type}"

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







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  input_pt_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							lowerCAmelCase__	 =  input_np_ad.squeeze(0    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )
							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_ad    ).max() < 1E-6


	def 						UpperCAmelCase					(							self    )->   List[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  VaeImageProcessor(do_resize=__UpperCAmelCase						,      do_normalize=__UpperCAmelCase    )

							lowerCAmelCase__	 =  self.dummy_sample
							lowerCAmelCase__	 =  list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  self.to_np(self.dummy_sample    )
							list(__UpperCAmelCase    )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							lowerCAmelCase__	 =  image_processor.postprocess(
							    image_processor.preprocess(__UpperCAmelCase    )						,      output_type="np"						,      )

							assert np.abs(out_pt_ad - out_pt_list    ).max() < 1E-6
							assert np.abs(out_np_ad - out_np_list    ).max() < 1E-6






