
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowerCAmelCase      :					List[Any]   	=       logging.get_logger(__name__)

__lowerCAmelCase      :					Optional[Any]   	=       ['UtteranceLevel']

@torch.no_grad()
def a__   (	A_,					A_,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	torch.load(SCREAMING_SNAKE_CASE_,					map_location="""cpu""" )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}''' )

  __magic_name__    					=	checkpoint["Downstream"]

  __magic_name__    					=	HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_ )
  __magic_name__    					=	HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_,					config=SCREAMING_SNAKE_CASE_ )
  __magic_name__    					=	WavaVecaFeatureExtractor.from_pretrained(
      SCREAMING_SNAKE_CASE_,					return_attention_mask=SCREAMING_SNAKE_CASE_,					do_normalize=SCREAMING_SNAKE_CASE_ )

  if hf_congfig.use_weighted_layer_sum:
    __magic_name__    					=	checkpoint["Featurizer"]["weights"]

  __magic_name__    					=	downstream_dict["projector.weight"]
  __magic_name__    					=	downstream_dict["projector.bias"]
  __magic_name__    					=	downstream_dict["model.post_net.linear.weight"]
  __magic_name__    					=	downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_ )
  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
   __lowerCAmelCase      :					Optional[int]   	=       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      :					Union[str, Any]   	=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase      :					str   	=       logging.get_logger(__name__)




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""pixel_values"""]
  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Dict[str, int]] = None      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[int, float] = 1 / 255      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							**UpperCamelCase__      :			Optional[Any]      ,							)      ->     None:







    """simple docstring"""






    super().__init__(**UpperCamelCase__   )
    __magic_name__    					=	size if size is not None else {"""height""": 224, """width""": 224}
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    __magic_name__    					=	do_resize
    __magic_name__    					=	do_rescale
    __magic_name__    					=	do_normalize
    __magic_name__    					=	size
    __magic_name__    					=	resample
    __magic_name__    					=	rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __magic_name__    					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Dict[str, int]      ,							UpperCamelCase__      :			PILImageResampling = PILImageResampling.BILINEAR      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Optional[int]      ,							)      ->     np.ndarray:







    """simple docstring"""






    __magic_name__    					=	get_size_dict(UpperCamelCase__   )
    if "height" not in size or "width" not in size:
      raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''   )
    return resize(
        UpperCamelCase__      ,							size=(size["""height"""], size["""width"""])      ,							resample=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			float      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     np.ndarray:







    """simple docstring"""






    return rescale(UpperCamelCase__      ,							scale=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			List[str]      ,							UpperCamelCase__      :			np.ndarray      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Union[float, List[float]]      ,							UpperCamelCase__      :			Optional[Union[str, ChannelDimension]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     np.ndarray:







    """simple docstring"""






    return normalize(UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__      ,							data_format=UpperCamelCase__      ,							**UpperCamelCase__   )





  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			ImageInput      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Dict[str, int] = None      ,							UpperCamelCase__      :			PILImageResampling = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[float] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[float, List[float]]] = None      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							UpperCamelCase__      :			Union[str, ChannelDimension] = ChannelDimension.FIRST      ,							**UpperCamelCase__      :			List[str]      ,							)      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	do_resize if do_resize is not None else self.do_resize
    __magic_name__    					=	do_rescale if do_rescale is not None else self.do_rescale
    __magic_name__    					=	do_normalize if do_normalize is not None else self.do_normalize
    __magic_name__    					=	resample if resample is not None else self.resample
    __magic_name__    					=	rescale_factor if rescale_factor is not None else self.rescale_factor
    __magic_name__    					=	image_mean if image_mean is not None else self.image_mean
    __magic_name__    					=	image_std if image_std is not None else self.image_std

    __magic_name__    					=	size if size is not None else self.size
    __magic_name__    					=	get_size_dict(UpperCamelCase__   )

    __magic_name__    					=	make_list_of_images(UpperCamelCase__   )

    if not valid_images(UpperCamelCase__   ):
      raise ValueError(
          """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
          """torch.Tensor, tf.Tensor or jax.ndarray."""   )

    if do_resize and size is None:
      raise ValueError("""Size must be specified if do_resize is True."""   )

    if do_rescale and rescale_factor is None:
      raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

    # All transformations expect numpy arrays.
    __magic_name__    					=	[to_numpy_array(UpperCamelCase__   ) for image in images]

    if do_resize:
      __magic_name__    					=	[self.resize(image=UpperCamelCase__      ,							size=UpperCamelCase__      ,							resample=UpperCamelCase__   ) for image in images]

    if do_rescale:
      __magic_name__    					=	[self.rescale(image=UpperCamelCase__      ,							scale=UpperCamelCase__   ) for image in images]

    if do_normalize:
      __magic_name__    					=	[self.normalize(image=UpperCamelCase__      ,							mean=UpperCamelCase__      ,							std=UpperCamelCase__   ) for image in images]

    __magic_name__    					=	[to_channel_dimension_format(UpperCamelCase__      ,							UpperCamelCase__   ) for image in images]

    __magic_name__    					=	{"""pixel_values""": images}
    return BatchFeature(data=UpperCamelCase__      ,							tensor_type=UpperCamelCase__   )

