

'''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 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[str]     	= ["vqvae"]
  def __init__(  self ,			a_ ,			a_ ,			a_ ,			a_ ,			)				->					int:


    """simple docstring"""
    super().__init__()
    self.register_modules(unet=a_ ,			scheduler=a_ ,			mel=a_ ,			vqvae=a_  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    return 5_0 if isinstance(self.scheduler ,			a_  ) else 1_0_0_0
  @torch.no_grad()
  def __call__(  self ,			a_ = 1 ,			a_ = None ,			a_ = None ,			a_ = 0 ,			a_ = 0 ,			a_ = None ,			a_ = None ,			a_ = 0 ,			a_ = 0 ,			a_ = None ,			a_ = 0 ,			a_ = None ,			a_ = None ,			a_=True ,			)				->					Union[
      Union[AudioPipelineOutput, ImagePipelineOutput],
      Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
  ]:


    """simple docstring"""
    UpperCAmelCase    				=						steps or self.get_default_steps()
    self.scheduler.set_timesteps(a_  )
    UpperCAmelCase    				=						step_generator or generator
    # For backwards compatibility
    if type(self.unet.config.sample_size  ) == int:
      UpperCAmelCase    				=						(self.unet.config.sample_size, self.unet.config.sample_size)
    if noise is None:
      UpperCAmelCase    				=						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 ,			)
    UpperCAmelCase    				=						noise
    UpperCAmelCase    				=						None

    if audio_file is not None or raw_audio is not None:
      self.mel.load_audio(a_ ,			a_  )
      UpperCAmelCase    				=						self.mel.audio_slice_to_image(a_  )
      UpperCAmelCase    				=						np.frombuffer(input_image.tobytes() ,			dtype='uint8'  ).reshape(
          (input_image.height, input_image.width)  )
      UpperCAmelCase    				=						(input_image / 2_5_5) * 2 - 1
      UpperCAmelCase    				=						torch.tensor(input_image[np.newaxis, :, :] ,			dtype=torch.float  ).to(self.device  )

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

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

      UpperCAmelCase    				=						(
          self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
      )
      UpperCAmelCase    				=						int(mask_start_secs * pixels_per_second  )
      UpperCAmelCase    				=						int(mask_end_secs * pixels_per_second  )
      UpperCAmelCase    				=						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_  ):
        UpperCAmelCase    				=						self.unet(a_ ,			a_ ,			a_  )['sample']
      else:
        UpperCAmelCase    				=						self.unet(a_ ,			a_  )['sample']

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

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

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

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

    UpperCAmelCase    				=						[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     snake_case_      (  self ,			a_ ,			a_ = 5_0  )				->					np.ndarray:


    """simple docstring"""
    assert isinstance(self.scheduler ,			a_  )
    self.scheduler.set_timesteps(a_  )
    UpperCAmelCase    				=						np.array(
        [np.frombuffer(image.tobytes() ,			dtype='uint8'  ).reshape((1, image.height, image.width)  ) for image in images]  )
    UpperCAmelCase    				=						(sample / 2_5_5) * 2 - 1
    UpperCAmelCase    				=						torch.Tensor(a_  ).to(self.device  )

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

    return sample





  @staticmethod
  def     snake_case_      (  a_ ,			a_ ,			a_  )				->					torch.Tensor:


    """simple docstring"""
    UpperCAmelCase    				=						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_  )





'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a					:			str							=    logging.get_logger(__name__)

_a					:			Dict							=    {
    'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			List[Any]     	= "cvt"
  def __init__(  self ,			a_=3 ,			a_=[7, 3, 3] ,			a_=[4, 2, 2] ,			a_=[2, 1, 1] ,			a_=[6_4, 1_9_2, 3_8_4] ,			a_=[1, 3, 6] ,			a_=[1, 2, 1_0] ,			a_=[4.0, 4.0, 4.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.0] ,			a_=[0.0, 0.0, 0.1] ,			a_=[True, True, True] ,			a_=[False, False, True] ,			a_=["dw_bn", "dw_bn", "dw_bn"] ,			a_=[3, 3, 3] ,			a_=[1, 1, 1] ,			a_=[2, 2, 2] ,			a_=[1, 1, 1] ,			a_=[1, 1, 1] ,			a_=0.02 ,			a_=1E-12 ,			**a_ ,			)				->					Union[str, Any]:


    """simple docstring"""
    super().__init__(**a_  )
    UpperCAmelCase    				=						num_channels
    UpperCAmelCase    				=						patch_sizes
    UpperCAmelCase    				=						patch_stride
    UpperCAmelCase    				=						patch_padding
    UpperCAmelCase    				=						embed_dim
    UpperCAmelCase    				=						num_heads
    UpperCAmelCase    				=						depth
    UpperCAmelCase    				=						mlp_ratio
    UpperCAmelCase    				=						attention_drop_rate
    UpperCAmelCase    				=						drop_rate
    UpperCAmelCase    				=						drop_path_rate
    UpperCAmelCase    				=						qkv_bias
    UpperCAmelCase    				=						cls_token
    UpperCAmelCase    				=						qkv_projection_method
    UpperCAmelCase    				=						kernel_qkv
    UpperCAmelCase    				=						padding_kv
    UpperCAmelCase    				=						stride_kv
    UpperCAmelCase    				=						padding_q
    UpperCAmelCase    				=						stride_q
    UpperCAmelCase    				=						initializer_range
    UpperCAmelCase    				=						layer_norm_eps


