


import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase__      		= logging.get_logger(__name__)
def 							_UpperCAmelCase				(UpperCamelCase__     :  Dict				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Dict						):
       _A      :       List[Any]				    = UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       Optional[Any]				    = downstream_dict["projector.weight"]
       _A      :       List[Any]				    = downstream_dict["projector.bias"]
       _A      :       int				    = downstream_dict["model.post_net.linear.weight"]
       _A      :       Optional[int]				    = downstream_dict["model.post_net.linear.bias"]
       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  Tuple				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  List[str]						):
       _A      :       Tuple				    = UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       Optional[Any]				    = downstream_dict["model.linear.weight"]
       _A      :       Optional[int]				    = downstream_dict["model.linear.bias"]
       return model
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]				,							UpperCamelCase__     :  List[Any]				,							UpperCamelCase__     :  Optional[Any]						):
       _A      :       List[Any]				    = UniSpeechSatForXVector.from_pretrained(UpperCamelCase__				,							config=UpperCamelCase__						)
       _A      :       str				    = downstream_dict["connector.weight"]
       _A      :       Optional[int]				    = downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
              _A      :       Union[str, Any]				    = downstream_dict[
                  f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              _A      :       Tuple				    = downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       _A      :       Union[str, Any]				    = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       _A      :       Dict				    = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       _A      :       str				    = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       _A      :       Union[str, Any]				    = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       _A      :       int				    = downstream_dict["objective.W"]
       return model
@torch.no_grad()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Dict						):
       _A      :       List[Any]				    = torch.load(UpperCamelCase__				,							map_location="cpu"						)

       _A      :       List[str]				    = checkpoint["Downstream"]

       _A      :       Union[str, Any]				    = UniSpeechSatConfig.from_pretrained(UpperCamelCase__						)
       _A      :       int				    = WavaVecaFeatureExtractor.from_pretrained(
           UpperCamelCase__				,							return_attention_mask=UpperCamelCase__				,							do_normalize=UpperCamelCase__						)

       _A      :       List[Any]				    = hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"						):
              _A      :       Union[str, Any]				    = convert_classification(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
       elif arch.endswith("ForAudioFrameClassification"						):
              _A      :       List[Any]				    = convert_diarization(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
       elif arch.endswith("ForXVector"						):
              _A      :       int				    = convert_xvector(UpperCamelCase__				,							UpperCamelCase__				,							UpperCamelCase__						)
       else:
              raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"						)

       if hf_config.use_weighted_layer_sum:
              _A      :       Optional[int]				    = checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(UpperCamelCase__						)
       hf_model.save_pretrained(UpperCamelCase__						)


if __name__ == "__main__":
    lowerCAmelCase__      		= 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.')
    lowerCAmelCase__      		= parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def 							_UpperCAmelCase				(UpperCamelCase__     :  Union[str, Any]						):
       _A    ,			_A      :       Any				    = image.size
       _A    ,			_A      :       str				    = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
       _A      :       List[str]				    = image.resize((w, h)				,							resample=PIL_INTERPOLATION["lanczos"]						)
       _A      :       Any				    = np.array(UpperCamelCase__						).astype(np.floataa						) / 2_55.0
       _A      :       Optional[Any]				    = image[None].transpose(0				,							3				,							1				,							2						)
       _A      :       Union[str, Any]				    = torch.from_numpy(UpperCamelCase__						)
       return 2.0 * image - 1.0







class      lowerCAmelCase__       (				a):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						)			->				Optional[int]:
            super().__init__()
            self.register_modules(vqvae=__lowerCamelCase ,						unet=__lowerCamelCase ,						scheduler=__lowerCamelCase)


     @torch.no_grad()
     def __call__(						self ,						__lowerCamelCase = None ,						__lowerCamelCase = 1 ,						__lowerCamelCase = 1_0_0 ,						__lowerCamelCase = 0.0 ,						__lowerCamelCase = None ,						__lowerCamelCase = "pil" ,						__lowerCamelCase = True ,						)			->				Union[Tuple, ImagePipelineOutput]:
            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Tuple				    = 1
            elif isinstance(__lowerCamelCase ,						torch.Tensor):
                   _A      :       Union[str, Any]				    = image.shape[0]
            else:
                   raise ValueError(F"`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowerCamelCase)}")

            if isinstance(__lowerCamelCase ,						PIL.Image.Image):
                   _A      :       Union[str, Any]				    = preprocess(__lowerCamelCase)

            _A    ,			_A      :       Union[str, Any]				    = image.shape[-2:]

            # in_channels should be 6: 3 for latents, 3 for low resolution image
            _A      :       Optional[Any]				    = (batch_size, self.unet.config.in_channels // 2, height, width)
            _A      :       str				    = next(self.unet.parameters()).dtype

            _A      :       Union[str, Any]				    = randn_tensor(__lowerCamelCase ,						generator=__lowerCamelCase ,						device=self.device ,						dtype=__lowerCamelCase)

            _A      :       List[Any]				    = image.to(device=self.device ,						dtype=__lowerCamelCase)

            # set timesteps and move to the correct device
            self.scheduler.set_timesteps(__lowerCamelCase ,						device=self.device)
            _A      :       Any				    = self.scheduler.timesteps

            # scale the initial noise by the standard deviation required by the scheduler
            _A      :       List[str]				    = latents * self.scheduler.init_noise_sigma

            # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
            # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
            # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
            # and should be between [0, 1]
            _A      :       str				    = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
            _A      :       Optional[int]				    = {}
            if accepts_eta:
                   _A      :       List[Any]				    = eta

            for t in self.progress_bar(__lowerCamelCase):
                   # concat latents and low resolution image in the channel dimension.
                   _A      :       List[Any]				    = torch.cat([latents, image] ,						dim=1)
                   _A      :       str				    = self.scheduler.scale_model_input(__lowerCamelCase ,						__lowerCamelCase)
                   # predict the noise residual
                   _A      :       Any				    = self.unet(__lowerCamelCase ,						__lowerCamelCase).sample
                   # compute the previous noisy sample x_t -> x_t-1
                   _A      :       Optional[int]				    = self.scheduler.step(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase).prev_sample

            # decode the image latents with the VQVAE
            _A      :       Union[str, Any]				    = self.vqvae.decode(__lowerCamelCase).sample
            _A      :       Dict				    = torch.clamp(__lowerCamelCase ,						-1.0 ,						1.0)
            _A      :       Tuple				    = image / 2 + 0.5
            _A      :       int				    = image.cpu().permute(0 ,						2 ,						3 ,						1).numpy()

            if output_type == "pil":
                   _A      :       Optional[int]				    = self.numpy_to_pil(__lowerCamelCase)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__lowerCamelCase)
