
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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     ["vqvae"]






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






	def 				UpperCamelCase_  (       self					: Tuple ):
					return 50 if isinstance(self.scheduler							,A ) else 10_00






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

					if audio_file is not None or raw_audio is not None:
									self.mel.load_audio(A							,A )
									__A   			= self.mel.audio_slice_to_image(A )
									__A   			= np.frombuffer(input_image.tobytes()							,dtype="uint8" ).reshape(
									    (input_image.height, input_image.width) )
									__A   			= (input_image / 2_55) * 2 - 1
									__A   			= torch.tensor(input_image[np.newaxis, :, :]							,dtype=torch.float ).to(self.device )

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

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

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

					for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
									if isinstance(self.unet							,A ):
													__A   			= self.unet(A							,A							,A )["sample"]
									else:
													__A   			= self.unet(A							,A )["sample"]

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

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

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

					__A   			= (images / 2 + 0.5).clamp(0							,1 )
					__A   			= images.cpu().permute(0							,2							,3							,1 ).numpy()
					__A   			= (images * 2_55).round().astype("uint8" )
					__A   			= list(
					    (Image.fromarray(_[:, :, 0] ) for _ in images)
					    if images.shape[3] == 1
					    else (Image.fromarray(A							,mode="RGB" ).convert("L" ) for _ in images) )

					__A   			= [self.mel.image_to_audio(A ) for _ in images]
					if not return_dict:
									return images, (self.mel.get_sample_rate(), audios)

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






	@torch.no_grad()
	def 				UpperCamelCase_  (       self					: List[Any]							,A					: List[Image.Image]							,A					: int = 50 ):
					assert isinstance(self.scheduler							,A )
					self.scheduler.set_timesteps(A )
					__A   			= np.array(
					    [np.frombuffer(image.tobytes()							,dtype="uint8" ).reshape((1, image.height, image.width) ) for image in images] )
					__A   			= (sample / 2_55) * 2 - 1
					__A   			= torch.Tensor(A ).to(self.device )

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

					return sample







	@staticmethod
	def 				UpperCamelCase_  (       A					: torch.Tensor							,A					: torch.Tensor							,A					: float ):
					__A   			= acos(torch.dot(torch.flatten(A )							,torch.flatten(A ) ) / torch.norm(A ) / torch.norm(A ) )
					return sin((1 - alpha) * theta ) * xa / sin(A ) + sin(alpha * theta ) * xa / sin(A )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE							:List[Any]							 =				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]							 =				['BartphoTokenizer']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
