






"""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  lowerCamelCase__ (	__lowercase			):
    SCREAMING_SNAKE_CASE		=      ["vqvae"]






    def __init__(			self     ,A     ,A     ,A     ,A     ,):
          super().__init__()
          self.register_modules(unet=_a     ,scheduler=_a     ,mel=_a     ,vqvae=_a )






    def     _UpperCamelCase							(			self ):
          return 50 if isinstance(self.scheduler     ,_a ) else 1_000






    @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     ,):
          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 / 255) * 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 * 255).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     _UpperCamelCase							(			self     ,A     ,A = 50 ):
          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 / 255) * 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     _UpperCamelCase							(			A     ,A     ,A ):
          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"""







import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase					      =  logging.get_logger(__name__)

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForSequenceClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""projector.weight"""]
      UpperCAmelCase          =	downstream_dict["""projector.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.post_net.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForAudioFrameClassification.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""model.linear.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.linear.bias"""]
      return model

def _a			(		_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	WavaVecaForXVector.from_pretrained(_snake_case				,	config=_snake_case							)
      UpperCAmelCase          =	downstream_dict["""connector.weight"""]
      UpperCAmelCase          =	downstream_dict["""connector.bias"""]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
            UpperCAmelCase          =	downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCAmelCase          =	downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
      UpperCAmelCase          =	downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
      UpperCAmelCase          =	downstream_dict["""objective.W"""]
      return model

@torch.no_grad()
def _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case							):







      """simple docstring"""






      UpperCAmelCase          =	torch.load(_snake_case				,	map_location="""cpu"""							)

      UpperCAmelCase          =	checkpoint["""Downstream"""]

      UpperCAmelCase          =	WavaVecaConfig.from_pretrained(_snake_case							)
      UpperCAmelCase          =	WavaVecaFeatureExtractor.from_pretrained(
          _snake_case				,	return_attention_mask=_snake_case				,	do_normalize=_snake_case							)

      UpperCAmelCase          =	hf_config.architectures[0]
      if arch.endswith("""ForSequenceClassification"""							):
            UpperCAmelCase          =	convert_classification(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForAudioFrameClassification"""							):
            UpperCAmelCase          =	convert_diarization(_snake_case				,	_snake_case				,	_snake_case							)
      elif arch.endswith("""ForXVector"""							):
            UpperCAmelCase          =	convert_xvector(_snake_case				,	_snake_case				,	_snake_case							)
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''							)

      if hf_config.use_weighted_layer_sum:
            UpperCAmelCase          =	checkpoint["""Featurizer"""]["""weights"""]

      hf_feature_extractor.save_pretrained(_snake_case							)
      hf_model.save_pretrained(_snake_case							)


if __name__ == "__main__":
  _UpperCamelCase					      =  argparse.ArgumentParser()
  parser.add_argument(
      """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
  )
  parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
  parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
  parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
  _UpperCamelCase					      =  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



