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 						a__							(       A__		):
			A__    :       Union[str, Any]        =      ['vqvae']



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						)			->   Optional[int]:
								super().__init__()
								self.register_modules(unet=__snake_case				,						scheduler=__snake_case				,						mel=__snake_case				,						vqvae=__snake_case   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								return 5_0 if isinstance(self.scheduler				,						__snake_case   ) else 1_0_0_0



			@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]]],
			]:
								__a		 =       steps or self.get_default_steps()
								self.scheduler.set_timesteps(__snake_case   )
								__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=__snake_case				,						device=self.device				,						)
								__a		 =       noise
								__a		 =       None

								if audio_file is not None or raw_audio is not None:
													self.mel.load_audio(__snake_case				,						__snake_case   )
													__a		 =       self.mel.audio_slice_to_image(__snake_case   )
													__a		 =       np.frombuffer(input_image.tobytes()				,						dtype='uint8'   ).reshape(
													    (input_image.height, input_image.width)   )
													__a		 =       (input_image / 2_5_5) * 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(__snake_case				,						0   )   ).latent_dist.sample(
																		    generator=__snake_case   )[0]
																		__a		 =       self.vqvae.config.scaling_factor * input_images

													if start_step > 0:
																		__a		 =       self.scheduler.add_noise(__snake_case				,						__snake_case				,						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(__snake_case				,						__snake_case				,						torch.tensor(self.scheduler.timesteps[start_step:]   )   )

								for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]   )   ):
													if isinstance(self.unet				,						__snake_case   ):
																		__a		 =       self.unet(__snake_case				,						__snake_case				,						__snake_case   )['sample']
													else:
																		__a		 =       self.unet(__snake_case				,						__snake_case   )['sample']

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

								__a		 =       (images / 2 + 0.5).clamp(0				,						1   )
								__a		 =       images.cpu().permute(0				,						2				,						3				,						1   ).numpy()
								__a		 =       (images * 2_5_5).round().astype('uint8'   )
								__a		 =       list(
								    (Image.fromarray(_[:, :, 0]   ) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(__snake_case				,						mode='RGB'   ).convert('L'   ) for _ in images)   )

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

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



			@torch.no_grad()
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = 5_0   )			->   np.ndarray:
								assert isinstance(self.scheduler				,						__snake_case   )
								self.scheduler.set_timesteps(__snake_case   )
								__a		 =       np.array(
								    [np.frombuffer(image.tobytes()				,						dtype='uint8'   ).reshape((1, image.height, image.width)   ) for image in images]   )
								__a		 =       (sample / 2_5_5) * 2 - 1
								__a		 =       torch.Tensor(__snake_case   ).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(__snake_case				,						__snake_case   )['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 __SCREAMING_SNAKE_CASE							(						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   torch.Tensor:
								__a		 =       acos(torch.dot(torch.flatten(__snake_case   )				,						torch.flatten(__snake_case   )   ) / torch.norm(__snake_case   ) / torch.norm(__snake_case   )   )
								return sin((1 - alpha) * theta   ) * xa / sin(__snake_case   ) + sin(alpha * theta   ) * xa / sin(__snake_case   )





from ..utils import DummyObject, requires_backends


class 						a__							(       metaclass=__snake_case		):
			A__    :       List[Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Union[str, Any]        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[Any]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Dict        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Optional[int]:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   str:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )






class 						a__							(       metaclass=__snake_case		):
			A__    :       Tuple        =      ['torch', 'transformers', 'onnx']



			def __init__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   int:
								requires_backends(self				,						['torch', 'transformers', 'onnx']   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Any:
								requires_backends(cls				,						['torch', 'transformers', 'onnx']   )




