


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 			_lowerCAmelCase      (			__a ):
				_lowercase    	=['''vqvae''']




				def __init__(      self       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase       ,       )       ->   str:
							super().__init__()
							self.register_modules(unet=_UpperCamelCase       ,       scheduler=_UpperCamelCase       ,       mel=_UpperCamelCase       ,       vqvae=_UpperCamelCase  )




				def   __a		(      self  )       ->   int:
							return 50 if isinstance(self.scheduler       ,       _UpperCamelCase  ) else 1_000




				@torch.no_grad()
				def __call__(      self       ,       _UpperCamelCase = 1       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = None       ,       _UpperCamelCase = 0       ,       _UpperCamelCase = None       ,       _UpperCamelCase = None       ,       _UpperCamelCase=True       ,       )       ->   Union[
				    Union[AudioPipelineOutput, ImagePipelineOutput],
				    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
				]:
							lowerCAmelCase_      			=			steps or self.get_default_steps()
							self.scheduler.set_timesteps(_UpperCamelCase  )
							lowerCAmelCase_      			=			step_generator or generator
							# For backwards compatibility
							if type(self.unet.config.sample_size  ) == int:
										lowerCAmelCase_      			=			(self.unet.config.sample_size, self.unet.config.sample_size)
							if noise is None:
										lowerCAmelCase_      			=			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       ,       )
							lowerCAmelCase_      			=			noise
							lowerCAmelCase_      			=			None

							if audio_file is not None or raw_audio is not None:
										self.mel.load_audio(_UpperCamelCase       ,       _UpperCamelCase  )
										lowerCAmelCase_      			=			self.mel.audio_slice_to_image(_UpperCamelCase  )
										lowerCAmelCase_      			=			np.frombuffer(input_image.tobytes()       ,       dtype="uint8"  ).reshape(
										    (input_image.height, input_image.width)  )
										lowerCAmelCase_      			=			(input_image / 255) * 2 - 1
										lowerCAmelCase_      			=			torch.tensor(input_image[np.newaxis, :, :]       ,       dtype=torch.float  ).to(self.device  )

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

										if start_step > 0:
													lowerCAmelCase_      			=			self.scheduler.add_noise(_UpperCamelCase       ,       _UpperCamelCase       ,       self.scheduler.timesteps[start_step - 1]  )

										lowerCAmelCase_      			=			(
										    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
										)
										lowerCAmelCase_      			=			int(mask_start_secs * pixels_per_second  )
										lowerCAmelCase_      			=			int(mask_end_secs * pixels_per_second  )
										lowerCAmelCase_      			=			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  ):
													lowerCAmelCase_      			=			self.unet(_UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )["sample"]
										else:
													lowerCAmelCase_      			=			self.unet(_UpperCamelCase       ,       _UpperCamelCase  )["sample"]

										if isinstance(self.scheduler       ,       _UpperCamelCase  ):
													lowerCAmelCase_      			=			self.scheduler.step(
													    model_output=_UpperCamelCase       ,       timestep=_UpperCamelCase       ,       sample=_UpperCamelCase       ,       eta=_UpperCamelCase       ,       generator=_UpperCamelCase       ,       )["prev_sample"]
										else:
													lowerCAmelCase_      			=			self.scheduler.step(
													    model_output=_UpperCamelCase       ,       timestep=_UpperCamelCase       ,       sample=_UpperCamelCase       ,       generator=_UpperCamelCase       ,       )["prev_sample"]

										if mask is not None:
													if mask_start > 0:
																lowerCAmelCase_      			=			mask[:, step, :, :mask_start]
													if mask_end > 0:
																lowerCAmelCase_      			=			mask[:, step, :, -mask_end:]

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

							lowerCAmelCase_      			=			(images / 2 + 0.5).clamp(0       ,       1  )
							lowerCAmelCase_      			=			images.cpu().permute(0       ,       2       ,       3       ,       1  ).numpy()
							lowerCAmelCase_      			=			(images * 255).round().astype("uint8"  )
							lowerCAmelCase_      			=			list(
							    (Image.fromarray(_[:, :, 0]  ) for _ in images)
							    if images.shape[3] == 1
							    else (Image.fromarray(_UpperCamelCase       ,       mode="RGB"  ).convert("L"  ) for _ in images)  )

							lowerCAmelCase_      			=			[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   __a		(      self       ,       _UpperCamelCase       ,       _UpperCamelCase = 50  )       ->   np.ndarray:
							assert isinstance(self.scheduler       ,       _UpperCamelCase  )
							self.scheduler.set_timesteps(_UpperCamelCase  )
							lowerCAmelCase_      			=			np.array(
							    [np.frombuffer(image.tobytes()       ,       dtype="uint8"  ).reshape((1, image.height, image.width)  ) for image in images]  )
							lowerCAmelCase_      			=			(sample / 255) * 2 - 1
							lowerCAmelCase_      			=			torch.Tensor(_UpperCamelCase  ).to(self.device  )

							for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,       (0,)  )  ):
										lowerCAmelCase_      			=			t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
										lowerCAmelCase_      			=			self.scheduler.alphas_cumprod[t]
										lowerCAmelCase_      			=			(
										    self.scheduler.alphas_cumprod[prev_timestep]
										    if prev_timestep >= 0
										    else self.scheduler.final_alpha_cumprod
										)
										lowerCAmelCase_      			=			1 - alpha_prod_t
										lowerCAmelCase_      			=			self.unet(_UpperCamelCase       ,       _UpperCamelCase  )["sample"]
										lowerCAmelCase_      			=			(1 - alpha_prod_t_prev) ** 0.5 * model_output
										lowerCAmelCase_      			=			(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
										lowerCAmelCase_      			=			sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

							return sample




				@staticmethod
				def   __a		(      _UpperCamelCase       ,       _UpperCamelCase       ,       _UpperCamelCase  )       ->   torch.Tensor:
							lowerCAmelCase_      			=			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  )





from string import ascii_lowercase, ascii_uppercase






def      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


			if not sentence:
						return ""
			lowerCAmelCase_      			=			dict(zip(__lowerCAmelCase					,							__lowerCAmelCase       )       )
			return lower_to_upper.get(sentence[0]					,							sentence[0]       ) + sentence[1:]


if __name__ == "__main__":
			from doctest import testmod

			testmod()



