



'''simple docstring'''

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    Union[str, Any]   				=					['vqvae']







							def __init__(							self					:       str	,_UpperCAmelCase					:       AutoencoderKL	,_UpperCAmelCase					:       UNetaDConditionModel	,_UpperCAmelCase					:       Mel	,_UpperCAmelCase					:       Union[DDIMScheduler, DDPMScheduler]	,):
									super().__init__()
									self.register_modules(unet=_UpperCAmelCase	,scheduler=_UpperCAmelCase	,mel=_UpperCAmelCase	,vqvae=_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Tuple		):
									return 50 if isinstance(self.scheduler	,_UpperCAmelCase		) else 1000







							@torch.no_grad()
							def __call__(							self					:       List[str]	,_UpperCAmelCase					:       int = 1	,_UpperCAmelCase					:       str = None	,_UpperCAmelCase					:       np.ndarray = None	,_UpperCAmelCase					:       int = 0	,_UpperCAmelCase					:       int = 0	,_UpperCAmelCase					:       int = None	,_UpperCAmelCase					:       torch.Generator = None	,_UpperCAmelCase					:       float = 0	,_UpperCAmelCase					:       float = 0	,_UpperCAmelCase					:       torch.Generator = None	,_UpperCAmelCase					:       float = 0	,_UpperCAmelCase					:       torch.Tensor = None	,_UpperCAmelCase					:       torch.Tensor = None	,_UpperCAmelCase					:       str=True	,):
									_a     :      List[str]        =							steps or self.get_default_steps()
									self.scheduler.set_timesteps(_UpperCAmelCase		)
									_a     :      Any        =							step_generator or generator
									# For backwards compatibility
									if type(self.unet.config.sample_size		) == int:
											_a     :      Optional[Any]        =							(self.unet.config.sample_size, self.unet.config.sample_size)
									if noise is None:
											_a     :      int        =							randn_tensor(
											    (
											        batch_size,
											        self.unet.config.in_channels,
											        self.unet.config.sample_size[0],
											        self.unet.config.sample_size[1],
											    )	,generator=_UpperCAmelCase	,device=self.device	,)
									_a     :      Optional[int]        =							noise
									_a     :      Optional[int]        =							None

									if audio_file is not None or raw_audio is not None:
											self.mel.load_audio(_UpperCAmelCase	,_UpperCAmelCase		)
											_a     :      int        =							self.mel.audio_slice_to_image(_UpperCAmelCase		)
											_a     :      str        =							np.frombuffer(input_image.tobytes()	,dtype='uint8'		).reshape(
											    (input_image.height, input_image.width)		)
											_a     :      Union[str, Any]        =							(input_image / 255) * 2 - 1
											_a     :      Optional[int]        =							torch.tensor(input_image[np.newaxis, :, :]	,dtype=torch.float		).to(self.device		)

											if self.vqvae is not None:
													_a     :      Dict        =							self.vqvae.encode(torch.unsqueeze(_UpperCAmelCase	,0		)		).latent_dist.sample(
													    generator=_UpperCAmelCase		)[0]
													_a     :      Dict        =							self.vqvae.config.scaling_factor * input_images

											if start_step > 0:
													_a     :      str        =							self.scheduler.add_noise(_UpperCAmelCase	,_UpperCAmelCase	,self.scheduler.timesteps[start_step - 1]		)

											_a     :      List[str]        =							(
											    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
											)
											_a     :      List[Any]        =							int(mask_start_secs * pixels_per_second		)
											_a     :      str        =							int(mask_end_secs * pixels_per_second		)
											_a     :      str        =							self.scheduler.add_noise(_UpperCAmelCase	,_UpperCAmelCase	,torch.tensor(self.scheduler.timesteps[start_step:]		)		)

									for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
											if isinstance(self.unet	,_UpperCAmelCase		):
													_a     :      int        =							self.unet(_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)['sample']
											else:
													_a     :      Union[str, Any]        =							self.unet(_UpperCAmelCase	,_UpperCAmelCase		)['sample']

											if isinstance(self.scheduler	,_UpperCAmelCase		):
													_a     :      int        =							self.scheduler.step(
													    model_output=_UpperCAmelCase	,timestep=_UpperCAmelCase	,sample=_UpperCAmelCase	,eta=_UpperCAmelCase	,generator=_UpperCAmelCase	,)['prev_sample']
											else:
													_a     :      int        =							self.scheduler.step(
													    model_output=_UpperCAmelCase	,timestep=_UpperCAmelCase	,sample=_UpperCAmelCase	,generator=_UpperCAmelCase	,)['prev_sample']

											if mask is not None:
													if mask_start > 0:
															_a     :      Tuple        =							mask[:, step, :, :mask_start]
													if mask_end > 0:
															_a     :      List[Any]        =							mask[:, step, :, -mask_end:]

									if self.vqvae is not None:
											# 0.18215 was scaling factor used in training to ensure unit variance
											_a     :      int        =							1 / self.vqvae.config.scaling_factor * images
											_a     :      Tuple        =							self.vqvae.decode(_UpperCAmelCase		)['sample']

									_a     :      Union[str, Any]        =							(images / 2 + 0.5).clamp(0	,1		)
									_a     :      Dict        =							images.cpu().permute(0	,2	,3	,1		).numpy()
									_a     :      Optional[Any]        =							(images * 255).round().astype('uint8'		)
									_a     :      List[Any]        =							list(
									    (Image.fromarray(_[:, :, 0]		) for _ in images)
									    if images.shape[3] == 1
									    else (Image.fromarray(_UpperCAmelCase	,mode='RGB'		).convert('L'		) for _ in images)		)

									_a     :      List[Any]        =							[self.mel.image_to_audio(_UpperCAmelCase		) for _ in images]
									if not return_dict:
											return images, (self.mel.get_sample_rate(), audios)

									return BaseOutput(**AudioPipelineOutput(np.array(_UpperCAmelCase		)[:, np.newaxis, :]		)	,**ImagePipelineOutput(_UpperCAmelCase		)		)







							@torch.no_grad()
							def 							__lowercase			(							self					:       str	,_UpperCAmelCase					:       List[Image.Image]	,_UpperCAmelCase					:       int = 50		):
									assert isinstance(self.scheduler	,_UpperCAmelCase		)
									self.scheduler.set_timesteps(_UpperCAmelCase		)
									_a     :      Union[str, Any]        =							np.array(
									    [np.frombuffer(image.tobytes()	,dtype='uint8'		).reshape((1, image.height, image.width)		) for image in images]		)
									_a     :      Optional[Any]        =							(sample / 255) * 2 - 1
									_a     :      Any        =							torch.Tensor(_UpperCAmelCase		).to(self.device		)

									for t in self.progress_bar(torch.flip(self.scheduler.timesteps	,(0,)		)		):
											_a     :      Dict        =							t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
											_a     :      Any        =							self.scheduler.alphas_cumprod[t]
											_a     :      Optional[int]        =							(
											    self.scheduler.alphas_cumprod[prev_timestep]
											    if prev_timestep >= 0
											    else self.scheduler.final_alpha_cumprod
											)
											_a     :      int        =							1 - alpha_prod_t
											_a     :      Union[str, Any]        =							self.unet(_UpperCAmelCase	,_UpperCAmelCase		)['sample']
											_a     :      Union[str, Any]        =							(1 - alpha_prod_t_prev) ** 0.5 * model_output
											_a     :      Any        =							(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
											_a     :      Any        =							sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

									return sample







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       torch.Tensor	,_UpperCAmelCase					:       torch.Tensor	,_UpperCAmelCase					:       float		):
									_a     :      int        =							acos(torch.dot(torch.flatten(_UpperCAmelCase		)	,torch.flatten(_UpperCAmelCase		)		) / torch.norm(_UpperCAmelCase		) / torch.norm(_UpperCAmelCase		)		)
									return sin((1 - alpha) * theta		) * xa / sin(_UpperCAmelCase		) + sin(alpha * theta		) * xa / sin(_UpperCAmelCase		)









'''simple docstring'''

import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      np.array:
		_a     :      Optional[int]        =							f"""{sampling_rate}"""
		_a     :      Any        =							'1'
		_a     :      Optional[int]        =							'f32le'
		_a     :      Any        =							[
		    'ffmpeg',
		    '-i',
		    'pipe:0',
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]

		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdin=subprocess.PIPE						,			stdout=subprocess.PIPE  ) as ffmpeg_process:
						_a     :      int        =							ffmpeg_process.communicate(lowerCAmelCase_  )
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to load audio files from filename'  ) from error
		_a     :      int        =							output_stream[0]
		_a     :      List[str]        =							np.frombuffer(lowerCAmelCase_						,			np.floataa  )
		if audio.shape[0] == 0:
				raise ValueError('Malformed soundfile'  )
		return audio



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = "f32le"						,			)			->      Union[str, Any]:
		_a     :      List[str]        =							f"""{sampling_rate}"""
		_a     :      List[str]        =							'1'
		if format_for_conversion == "s16le":
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Dict        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		_a     :      Any        =							platform.system()
		if system == "Linux":
				_a     :      Union[str, Any]        =							'alsa'
				_a     :      Union[str, Any]        =							'default'
		elif system == "Darwin":
				_a     :      Any        =							'avfoundation'
				_a     :      Optional[int]        =							':0'
		elif system == "Windows":
				_a     :      str        =							'dshow'
				_a     :      Tuple        =							'default'

		_a     :      str        =							[
		    'ffmpeg',
		    '-f',
		    format_,
		    '-i',
		    input_,
		    '-ac',
		    ac,
		    '-ar',
		    ar,
		    '-f',
		    format_for_conversion,
		    '-fflags',
		    'nobuffer',
		    '-hide_banner',
		    '-loglevel',
		    'quiet',
		    'pipe:1',
		]
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		_a     :      Union[str, Any]        =							_ffmpeg_stream(lowerCAmelCase_						,			lowerCAmelCase_  )
		for item in iterator:
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = "f32le"						,			)			->      str:
		if stream_chunk_s is not None:
				_a     :      str        =							stream_chunk_s
		else:
				_a     :      List[str]        =							chunk_length_s

		_a     :      int        =							ffmpeg_microphone(lowerCAmelCase_						,			lowerCAmelCase_						,			format_for_conversion=lowerCAmelCase_  )
		if format_for_conversion == "s16le":
				_a     :      Optional[Any]        =							np.intaa
				_a     :      List[Any]        =							2
		elif format_for_conversion == "f32le":
				_a     :      Tuple        =							np.floataa
				_a     :      Any        =							4
		else:
				raise ValueError(f"""Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`"""  )

		if stride_length_s is None:
				_a     :      str        =							chunk_length_s / 6
		_a     :      Any        =							int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
		if isinstance(lowerCAmelCase_						,			(int, float)  ):
				_a     :      List[str]        =							[stride_length_s, stride_length_s]

		_a     :      str        =							int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
		_a     :      List[str]        =							int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
		_a     :      Any        =							datetime.datetime.now()
		_a     :      Dict        =							datetime.timedelta(seconds=lowerCAmelCase_  )
		for item in chunk_bytes_iter(lowerCAmelCase_						,			lowerCAmelCase_						,			stride=(stride_left, stride_right)						,			stream=lowerCAmelCase_  ):
				# Put everything back in numpy scale
				_a     :      List[Any]        =							np.frombuffer(item['raw']						,			dtype=lowerCAmelCase_  )
				_a     :      List[str]        =							(
				    item['stride'][0] // size_of_sample,
				    item['stride'][1] // size_of_sample,
				)
				_a     :      Union[str, Any]        =							sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = False  )			->      List[Any]:
		_a     :      Tuple        =							B''
		_a       ,					_a     :      str        =							stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f"""Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}"""  )
		_a     :      Optional[int]        =							0
		for raw in iterator:
				acc += raw
				if stream and len(lowerCAmelCase_  ) < chunk_len:
						_a     :      str        =							(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(lowerCAmelCase_  ) >= chunk_len:
								# We are flushing the accumulator
								_a     :      Union[str, Any]        =							(_stride_left, stride_right)
								_a     :      Dict        =							{'raw': acc[:chunk_len], 'stride': stride}
								if stream:
										_a     :      List[str]        =							False
								yield item
								_a     :      int        =							stride_left
								_a     :      List[Any]        =							acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(lowerCAmelCase_  ) > stride_left:
				_a     :      str        =							{'raw': acc, 'stride': (_stride_left, 0)}
				if stream:
						_a     :      str        =							False
				yield item



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_  )			->      Tuple:
		_a     :      Optional[Any]        =							2**24  # 16Mo
		try:
				with subprocess.Popen(lowerCAmelCase_						,			stdout=subprocess.PIPE						,			bufsize=lowerCAmelCase_  ) as ffmpeg_process:
						while True:
								_a     :      Any        =							ffmpeg_process.stdout.read(lowerCAmelCase_  )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError('ffmpeg was not found but is required to stream audio files from filename'  ) from error




