

'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_a					:			Optional[int]							=    logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    super().__init__()
    self.register_modules(unet=a_ ,			scheduler=a_  )





  @torch.no_grad()
  def __call__(  self ,			a_ = 1 ,			a_ = 1_0_0 ,			a_ = None ,			a_ = None ,			a_ = True ,			)				->					Union[AudioPipelineOutput, Tuple]:


    """simple docstring"""

    if audio_length_in_s is None:
      UpperCAmelCase    				=						self.unet.config.sample_size / self.unet.config.sample_rate

    UpperCAmelCase    				=						audio_length_in_s * self.unet.config.sample_rate

    UpperCAmelCase    				=						2 ** len(self.unet.up_blocks  )
    if sample_size < 3 * down_scale_factor:
      raise ValueError(
          F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
          F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''  )

    UpperCAmelCase    				=						int(a_  )
    if sample_size % down_scale_factor != 0:
      UpperCAmelCase    				=						(
          (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
      ) * down_scale_factor
      logger.info(
          F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
          F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
          ' process.'  )
    UpperCAmelCase    				=						int(a_  )

    UpperCAmelCase    				=						next(iter(self.unet.parameters()  )  ).dtype
    UpperCAmelCase    				=						(batch_size, self.unet.config.in_channels, sample_size)
    if isinstance(a_ ,			a_  ) and len(a_  ) != batch_size:
      raise ValueError(
          F'''You have passed a list of generators of length {len(a_  )}, but requested an effective batch'''
          F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''  )

    UpperCAmelCase    				=						randn_tensor(a_ ,			generator=a_ ,			device=self.device ,			dtype=a_  )

    # set step values
    self.scheduler.set_timesteps(a_ ,			device=audio.device  )
    UpperCAmelCase    				=						self.scheduler.timesteps.to(a_  )

    for t in self.progress_bar(self.scheduler.timesteps  ):
      # 1. predict noise model_output
      UpperCAmelCase    				=						self.unet(a_ ,			a_  ).sample

      # 2. compute previous image: x_t -> t_t-1
      UpperCAmelCase    				=						self.scheduler.step(a_ ,			a_ ,			a_  ).prev_sample

    UpperCAmelCase    				=						audio.clamp(-1 ,			1  ).float().cpu().numpy()

    UpperCAmelCase    				=						audio[:, :, :original_sample_size]

    if not return_dict:
      return (audio,)

    return AudioPipelineOutput(audios=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


