





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__       (	__snake_case      ):
      _UpperCAmelCase     :Dict						   =       ['vqvae']





      def __init__(			self	,							A_	,							A_	,							A_	,							A_	,							):
       '''simple docstring'''




       super().__init__()
       self.register_modules(unet=A_	,							scheduler=A_	,							mel=A_	,							vqvae=A_       )





      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       return 50 if isinstance(self.scheduler	,							A_       ) else 1000





      @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	,							):
       '''simple docstring'''




       UpperCamelCase     : Union[str, Any]		=       steps or self.get_default_steps()
       self.scheduler.set_timesteps(A_       )
       UpperCamelCase     : Optional[int]		=       step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size       ) == int:
        UpperCamelCase     : Union[str, Any]		=       (self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
        UpperCamelCase     : List[str]		=       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     : int		=       noise
       UpperCamelCase     : Tuple		=       None

       if audio_file is not None or raw_audio is not None:
        self.mel.load_audio(A_	,							A_       )
        UpperCamelCase     : Optional[Any]		=       self.mel.audio_slice_to_image(A_       )
        UpperCamelCase     : Optional[int]		=       np.frombuffer(input_image.tobytes()	,							dtype="uint8"       ).reshape(
            (input_image.height, input_image.width)       )
        UpperCamelCase     : int		=       (input_image / 255) * 2 - 1
        UpperCamelCase     : Union[str, Any]		=       torch.tensor(input_image[np.newaxis, :, :]	,							dtype=torch.float       ).to(self.device       )

        if self.vqvae is not None:
         UpperCamelCase     : Optional[int]		=       self.vqvae.encode(torch.unsqueeze(A_	,							0       )       ).latent_dist.sample(
             generator=A_       )[0]
         UpperCamelCase     : List[str]		=       self.vqvae.config.scaling_factor * input_images

        if start_step > 0:
         UpperCamelCase     : Optional[int]		=       self.scheduler.add_noise(A_	,							A_	,							self.scheduler.timesteps[start_step - 1]       )

        UpperCamelCase     : Any		=       (
            self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
        )
        UpperCamelCase     : Any		=       int(mask_start_secs * pixels_per_second       )
        UpperCamelCase     : Dict		=       int(mask_end_secs * pixels_per_second       )
        UpperCamelCase     : str		=       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     : Optional[int]		=       self.unet(A_	,							A_	,							A_       )["sample"]
        else:
         UpperCamelCase     : List[Any]		=       self.unet(A_	,							A_       )["sample"]

        if isinstance(self.scheduler	,							A_       ):
         UpperCamelCase     : List[Any]		=       self.scheduler.step(
             model_output=A_	,							timestep=A_	,							sample=A_	,							eta=A_	,							generator=A_	,							)["prev_sample"]
        else:
         UpperCamelCase     : Tuple		=       self.scheduler.step(
             model_output=A_	,							timestep=A_	,							sample=A_	,							generator=A_	,							)["prev_sample"]

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

       if self.vqvae is not None:
        # 0.18215 was scaling factor used in training to ensure unit variance
        UpperCamelCase     : Optional[int]		=       1 / self.vqvae.config.scaling_factor * images
        UpperCamelCase     : Union[str, Any]		=       self.vqvae.decode(A_       )["sample"]

       UpperCamelCase     : Tuple		=       (images / 2 + 0.5).clamp(0	,							1       )
       UpperCamelCase     : List[Any]		=       images.cpu().permute(0	,							2	,							3	,							1       ).numpy()
       UpperCamelCase     : Dict		=       (images * 255).round().astype("uint8"       )
       UpperCamelCase     : List[str]		=       list(
           (Image.fromarray(_[:, :, 0]       ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(A_	,							mode="RGB"       ).convert("L"       ) for _ in images)       )

       UpperCamelCase     : Any		=       [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 	__UpperCamelCase(			self	,							A_	,							A_ = 50       ):
       '''simple docstring'''




       assert isinstance(self.scheduler	,							A_       )
       self.scheduler.set_timesteps(A_       )
       UpperCamelCase     : Dict		=       np.array(
           [np.frombuffer(image.tobytes()	,							dtype="uint8"       ).reshape((1, image.height, image.width)       ) for image in images]       )
       UpperCamelCase     : Dict		=       (sample / 255) * 2 - 1
       UpperCamelCase     : List[Any]		=       torch.Tensor(A_       ).to(self.device       )

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps	,							(0,)       )       ):
        UpperCamelCase     : Optional[int]		=       t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
        UpperCamelCase     : str		=       self.scheduler.alphas_cumprod[t]
        UpperCamelCase     : Tuple		=       (
            self.scheduler.alphas_cumprod[prev_timestep]
            if prev_timestep >= 0
            else self.scheduler.final_alpha_cumprod
        )
        UpperCamelCase     : Optional[Any]		=       1 - alpha_prod_t
        UpperCamelCase     : Union[str, Any]		=       self.unet(A_	,							A_       )["sample"]
        UpperCamelCase     : Optional[int]		=       (1 - alpha_prod_t_prev) ** 0.5 * model_output
        UpperCamelCase     : List[Any]		=       (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
        UpperCamelCase     : Tuple		=       sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample





      @staticmethod
      def 	__UpperCamelCase(			A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict		=       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_       )







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCamelCase	:					Dict			  =					logging.get_logger("""transformers.models.speecht5""")



def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase					)						->			Tuple:
 hf_model.apply_weight_norm()

 UpperCamelCase     : int	     =       checkpoint["input_conv.weight_g"]
 UpperCamelCase     : Dict	     =       checkpoint["input_conv.weight_v"]
 UpperCamelCase     : List[Any]	     =       checkpoint["input_conv.bias"]

 for i in range(len(config.upsample_rates					)					):
  UpperCamelCase     : Any	     =       checkpoint[F"""upsamples.{i}.1.weight_g"""]
  UpperCamelCase     : List[Any]	     =       checkpoint[F"""upsamples.{i}.1.weight_v"""]
  UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""upsamples.{i}.1.bias"""]

 for i in range(len(config.upsample_rates					) * len(config.resblock_kernel_sizes					)					):
  for j in range(len(config.resblock_dilation_sizes					)					):
   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCamelCase     : str	     =       checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCamelCase     : Union[str, Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCamelCase     : int	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCamelCase     : Optional[Any]	     =       checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_g"]
 UpperCamelCase     : Tuple	     =       checkpoint["output_conv.1.weight_v"]
 UpperCamelCase     : int	     =       checkpoint["output_conv.1.bias"]

 hf_model.remove_weight_norm()



@torch.no_grad()
def 		A_    (					_lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase							,       _lowerCAmelCase=None							,       _lowerCAmelCase=None							,       )						->			Tuple:
 if config_path is not None:
  UpperCamelCase     : List[Any]	     =       SpeechTaHifiGanConfig.from_pretrained(_lowerCAmelCase					)
 else:
  UpperCamelCase     : Optional[int]	     =       SpeechTaHifiGanConfig()

 UpperCamelCase     : List[str]	     =       SpeechTaHifiGan(_lowerCAmelCase					)

 UpperCamelCase     : str	     =       torch.load(_lowerCAmelCase					)
 load_weights(orig_checkpoint["model"]["generator"]							,       _lowerCAmelCase							,       _lowerCAmelCase					)

 UpperCamelCase     : List[Any]	     =       np.load(_lowerCAmelCase					)
 UpperCamelCase     : List[str]	     =       stats[0].reshape(-1					)
 UpperCamelCase     : Tuple	     =       stats[1].reshape(-1					)
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()
 UpperCamelCase     : Any	     =       torch.from_numpy(_lowerCAmelCase					).float()

 model.save_pretrained(_lowerCAmelCase					)

 if repo_id:
  print("Pushing to the hub..."					)
  model.push_to_hub(_lowerCAmelCase					)


if __name__ == "__main__":
    __lowerCamelCase	:					Union[str, Any]			  =					argparse.ArgumentParser()
    parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
    parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
    parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
    parser.add_argument(
        """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
    )

    __lowerCamelCase	:					Tuple			  =					parser.parse_args()
    convert_hifigan_checkpoint(
        args.checkpoint_path,
        args.stats_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.push_to_hub,
    )
