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 						lowercase    (     a     ):
							lowercase__						:       int								=							["""vqvae"""]




							def __init__(						self   :					List[str]	,							_UpperCamelCase   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					List[Any]	,							_UpperCamelCase   :					int	,							)					->				Dict:



												'''simple docstring'''

												super().__init__()
												self.register_modules(unet=_UpperCamelCase	,							scheduler=_UpperCamelCase	,							mel=_UpperCamelCase	,							vqvae=_UpperCamelCase      )




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



												'''simple docstring'''

												return 50 if isinstance(self.scheduler	,							_UpperCamelCase      ) else 1_000




							@torch.no_grad()
							def __call__(						self   :					Any	,							_UpperCamelCase   :					Optional[int] = 1	,							_UpperCamelCase   :					Dict = None	,							_UpperCamelCase   :					Any = None	,							_UpperCamelCase   :					List[Any] = 0	,							_UpperCamelCase   :					Dict = 0	,							_UpperCamelCase   :					Union[str, Any] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Any = 0	,							_UpperCamelCase   :					Optional[int] = 0	,							_UpperCamelCase   :					Union[str, Any] = None	,							_UpperCamelCase   :					Any = 0	,							_UpperCamelCase   :					Tuple = None	,							_UpperCamelCase   :					List[Any] = None	,							_UpperCamelCase   :					Optional[int]=True	,							)					->				str:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       steps or self.get_default_steps()
												self.scheduler.set_timesteps(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       step_generator or generator
												# For backwards compatibility
												if type(self.unet.config.sample_size      ) == int:
																	SCREAMING_SNAKE_CASE    			=       (self.unet.config.sample_size, self.unet.config.sample_size)
												if noise is None:
																	SCREAMING_SNAKE_CASE    			=       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	,							)
												SCREAMING_SNAKE_CASE    			=       noise
												SCREAMING_SNAKE_CASE    			=       None

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

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

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

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

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

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

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

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

												SCREAMING_SNAKE_CASE    			=       [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 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					List[str]	,							_UpperCamelCase   :					Optional[Any] = 50      )					->				Union[str, Any]:



												'''simple docstring'''

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

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

												return sample





							@staticmethod
							def 	__snake_case(						_UpperCamelCase   :					Optional[Any]	,							_UpperCamelCase   :					Optional[int]	,							_UpperCamelCase   :					int      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       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 __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

_lowerCamelCase					:   Optional[Any]							=		TypeVar('''T''')


class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Any	,							_UpperCamelCase   :					T      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       data
												SCREAMING_SNAKE_CASE    			=       None





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



												'''simple docstring'''

												return F"{self.data}"



class 						lowercase    (     Generic[T]     ):




							def __init__(						self   :					Optional[int]      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None




							def __iter__(						self   :					str      )					->				Iterator[T]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.top
												while node:
																	yield node.data
																	SCREAMING_SNAKE_CASE    			=       node.next




							def __str__(						self   :					int      )					->				str:



												'''simple docstring'''

												return "->".join([str(_UpperCamelCase      ) for item in self]      )




							def __len__(						self   :					Tuple      )					->				int:



												'''simple docstring'''

												return len(tuple(iter(self      )      )      )




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



												'''simple docstring'''

												return self.top is None




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					T      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       Node(_UpperCamelCase      )
												if not self.is_empty():
																	SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       node




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



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("pop from empty stack"      )
												assert isinstance(self.top	,							_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self.top
												SCREAMING_SNAKE_CASE    			=       self.top.next
												return pop_node.data




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



												'''simple docstring'''

												if self.is_empty():
																	raise IndexError("peek from empty stack"      )

												assert self.top is not None
												return self.top.data





							def 	__snake_case(						self   :					Dict      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       None


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

							testmod()




