

"""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 		_A       (			__UpperCAmelCase					):
      __a								=	["""vqvae"""]





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





      def 					_lowerCamelCase  (  self		)   ->					str:
         return 50 if isinstance(self.scheduler    ,						UpperCAmelCase_		) else 1000





      @torch.no_grad()
      def __call__(  self    ,						SCREAMING_SNAKE_CASE__ = 1    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = 0    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__ = None    ,						SCREAMING_SNAKE_CASE__=True    ,						)   ->					List[str]:
         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__ = 50		)   ->					List[str]:
         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 					_lowerCamelCase  (  SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         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_		)







"""simple docstring"""




# flake8: noqa
# Lint as: python3
SCREAMING_SNAKE_CASE_     :			Dict	  =			[
    '''VerificationMode''',
    '''Version''',
    '''disable_progress_bar''',
    '''enable_progress_bar''',
    '''is_progress_bar_enabled''',
    '''experimental''',
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental




