






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 __lowerCamelCase					( snake_case__	,			snake_case__):





	"""simple docstring"""






	UpperCamelCase__								= "mel_config.json"

	@register_to_config
	def __init__(				self			,       UpperCAmelCase = 256			,       UpperCAmelCase = 256			,       UpperCAmelCase = 2_2050			,       UpperCAmelCase = 2048			,       UpperCAmelCase = 512			,       UpperCAmelCase = 80			,       UpperCAmelCase = 32			,       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     hop_length
							_UpperCAmelCase							   =     sample_rate
							_UpperCAmelCase							   =     n_fft
							_UpperCAmelCase							   =     top_db
							_UpperCAmelCase							   =     n_iter
							self.set_resolution(UpperCAmelCase_			,       UpperCAmelCase_       )
							_UpperCAmelCase							   =     None

							if not _librosa_can_be_imported:
													raise ValueError(_import_error       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase = None			,       UpperCAmelCase = None       ):
							"""simple docstring"""







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

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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







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

	def 			UpperCamelCase       (				self			,       UpperCAmelCase = 0       ):
							"""simple docstring"""







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

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							return self.sr

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     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       )
							_UpperCAmelCase							   =     librosa.power_to_db(UpperCAmelCase_			,       ref=np.max			,       top_db=self.top_db       )
							_UpperCAmelCase							   =     (((log_S + self.top_db) * 255 / self.top_db).clip(0			,       255       ) + 0.5).astype(np.uinta       )
							_UpperCAmelCase							   =     Image.fromarray(UpperCAmelCase_       )
							return image

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     np.frombuffer(image.tobytes()			,       dtype='uint8'       ).reshape((image.height, image.width)       )
							_UpperCAmelCase							   =     bytedata.astype('float'       ) * self.top_db / 255 - self.top_db
							_UpperCAmelCase							   =     librosa.db_to_power(UpperCAmelCase_       )
							_UpperCAmelCase							   =     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







'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class      SCREAMING_SNAKE_CASE__      (   snake_case__							,snake_case__		):







					"""simple docstring"""






					def __init__(   self       :      Any		):







							"""simple docstring"""
							super().__init__()

							self.register_buffer("watermark_image"		,	torch.zeros((62, 62, 4)		)		)
							__UpperCAmelCase	:							Optional[int]     =     None






					def 		lowerCamelCase_   (   self       :      List[Any]		,	UpperCAmelCase_       :      List[PIL.Image.Image]		,	UpperCAmelCase_       :      int=None		):







							"""simple docstring"""
							# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

							__UpperCAmelCase	:							int     =     images[0].height
							__UpperCAmelCase	:							Any     =     images[0].width

							__UpperCAmelCase	:							Union[str, Any]     =     sample_size or h

							__UpperCAmelCase	:							Dict     =     min(h / sample_size		,	w / sample_size		)
							__UpperCAmelCase  ,			__UpperCAmelCase	:							Optional[int]     =     (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

							__UpperCAmelCase  ,			__UpperCAmelCase	:							Union[str, Any]     =     1_024**2, img_w * img_h
							__UpperCAmelCase	:							Optional[int]     =     (Sa / Sa) ** 0.5
							__UpperCAmelCase  ,			__UpperCAmelCase  ,			__UpperCAmelCase	:							str     =     int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

							if self.watermark_image_as_pil is None:
									__UpperCAmelCase	:							int     =     self.watermark_image.to(torch.uinta		).cpu().numpy()
									__UpperCAmelCase	:							Optional[int]     =     Image.fromarray(UpperCAmelCase_		,	mode="RGBA"		)
									__UpperCAmelCase	:							Optional[int]     =     watermark_image

							__UpperCAmelCase	:							List[str]     =     self.watermark_image_as_pil.resize(
							    (wm_size, wm_size)		,	PIL_INTERPOLATION["bicubic"]		,	reducing_gap=UpperCAmelCase_		)

							for pil_img in images:
									pil_img.paste(UpperCAmelCase_		,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)		,	mask=wm_img.split()[-1]		)

							return images




