"""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  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ["vqvae"]







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







       def 		a_						( self)      ->      int:
            return 50 if isinstance(self.scheduler,							lowerCAmelCase__) else 1000







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

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

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

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

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

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

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

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

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

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

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

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







       @torch.no_grad()
       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = 50)      ->      np.ndarray:
            assert isinstance(self.scheduler,							lowerCAmelCase__)
            self.scheduler.set_timesteps(lowerCAmelCase__)
            snake_case_					       =					np.array(
                [np.frombuffer(image.tobytes(),							dtype='uint8').reshape((1, image.height, image.width)) for image in images])
            snake_case_					       =					(sample / 255) * 2 - 1
            snake_case_					       =					torch.Tensor(lowerCAmelCase__).to(self.device)

            for t in self.progress_bar(torch.flip(self.scheduler.timesteps,							(0,))):
                 snake_case_					       =					t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
                 snake_case_					       =					self.scheduler.alphas_cumprod[t]
                 snake_case_					       =					(
                     self.scheduler.alphas_cumprod[prev_timestep]
                     if prev_timestep >= 0
                     else self.scheduler.final_alpha_cumprod
                 )
                 snake_case_					       =					1 - alpha_prod_t
                 snake_case_					       =					self.unet(lowerCAmelCase__,							lowerCAmelCase__)['sample']
                 snake_case_					       =					(1 - alpha_prod_t_prev) ** 0.5 * model_output
                 snake_case_					       =					(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
                 snake_case_					       =					sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

            return sample







       @staticmethod
       def 		a_						( lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)      ->      torch.Tensor:
            snake_case_					       =					acos(torch.dot(torch.flatten(lowerCAmelCase__),							torch.flatten(lowerCAmelCase__)) / torch.norm(lowerCAmelCase__) / torch.norm(lowerCAmelCase__))
            return sin((1 - alpha) * theta) * xa / sin(lowerCAmelCase__) + sin(alpha * theta) * xa / sin(lowerCAmelCase__)







import re

from filelock import FileLock


try:
   import nltk

   lowerCamelCase__ :   str					=							True
except (ImportError, ModuleNotFoundError):
   lowerCamelCase__ :   Union[str, Any]					=							False

if NLTK_AVAILABLE:
   with FileLock('.lock') as lock:
      nltk.download('punkt', quiet=True)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str							)					-> str:
      re.sub('<n>'			,			''			,			__UpperCAmelCase							)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(__UpperCAmelCase							)							)