

'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

UpperCAmelCase				   =  ['''UtteranceLevel''']

@torch.no_grad()
def 				__UpperCamelCase							(	lowercase__						:  Optional[Any],					lowercase__						:  Tuple,					lowercase__						:  List[str],					lowercase__						:  int ):






     '''simple docstring'''





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

     __lowercase					=checkpoint['Downstream']

     __lowercase					=HubertConfig.from_pretrained(lowercase__ )
     __lowercase					=HubertForSequenceClassification.from_pretrained(lowercase__,					config=lowercase__ )
     __lowercase					=WavaVecaFeatureExtractor.from_pretrained(
         lowercase__,					return_attention_mask=lowercase__,					do_normalize=lowercase__ )

     if hf_congfig.use_weighted_layer_sum:
          __lowercase					=checkpoint['Featurizer']['weights']

     __lowercase					=downstream_dict['projector.weight']
     __lowercase					=downstream_dict['projector.bias']
     __lowercase					=downstream_dict['model.post_net.linear.weight']
     __lowercase					=downstream_dict['model.post_net.linear.bias']

     hf_feature_extractor.save_pretrained(lowercase__ )
     hf_model.save_pretrained(lowercase__ )


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)





'''simple docstring'''

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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


UpperCAmelCase				   =  logging.get_logger(__name__)


class   lowerCAmelCase     (      A   ):
       lowerCAmelCase_		= ["pixel_values"]







       def __init__(							self			: Dict						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: bool = True						,      __lowercase			: Dict[str, int] = None						,      __lowercase			: PILImageResampling = PILImageResampling.BICUBIC						,      __lowercase			: bool = True						,      __lowercase			: Union[int, float] = 1 / 255						,      __lowercase			: bool = True						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      **__lowercase			: Any						,      ):







            """simple docstring"""







            super().__init__(**__lowercase      )
            __lowercase					=crop_size if crop_size is not None else {'height': 256, 'width': 256}
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=size if size is not None else {'height': 224, 'width': 224}
            __lowercase					=get_size_dict(__lowercase      )

            __lowercase					=do_center_crop
            __lowercase					=crop_size
            __lowercase					=do_resize
            __lowercase					=size
            __lowercase					=resample
            __lowercase					=do_rescale
            __lowercase					=rescale_factor
            __lowercase					=do_normalize
            __lowercase					=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
            __lowercase					=image_std if image_std is not None else IMAGENET_DEFAULT_STD







       def 			snake_case   (							self			: int						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: Optional[int] = None						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[Any]						,      ):







            """simple docstring"""







            __lowercase					=self.size if size is None else size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )

            __lowercase		,     __lowercase					=get_image_size(__lowercase      )
            __lowercase					=min(__lowercase						,      __lowercase      )
            __lowercase					=(size['height'] / crop_size['height']) * min_dim
            __lowercase					=(size['width'] / crop_size['width']) * min_dim
            return center_crop(__lowercase						,      size=(cropped_height, cropped_width)						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: str						,      __lowercase			: np.ndarray						,      __lowercase			: Dict[str, int]						,      __lowercase			: PILImageResampling = PIL.Image.BICUBIC						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=get_size_dict(__lowercase      )
            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(
                __lowercase						,      size=(size['height'], size['width'])						,      resample=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Dict						,      __lowercase			: np.ndarray						,      __lowercase			: Union[int, float]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: Dict						,      ):







            """simple docstring"""







            return rescale(__lowercase						,      scale=__lowercase						,      data_format=__lowercase						,      **__lowercase      )







       def 			snake_case   (							self			: Union[str, Any]						,      __lowercase			: np.ndarray						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Union[float, List[float]]						,      __lowercase			: Optional[Union[str, ChannelDimension]] = None						,      **__lowercase			: int						,      ):







            """simple docstring"""







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







       def 			snake_case   (							self			: Dict						,      __lowercase			: ImageInput						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Dict[str, int]] = None						,      __lowercase			: PILImageResampling = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[float] = None						,      __lowercase			: Optional[bool] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[float, List[float]]] = None						,      __lowercase			: Optional[Union[str, TensorType]] = None						,      __lowercase			: ChannelDimension = ChannelDimension.FIRST						,      **__lowercase			: Optional[int]						,      ):







            """simple docstring"""







            __lowercase					=do_center_crop if do_center_crop is not None else self.do_center_crop
            __lowercase					=crop_size if crop_size is not None else self.crop_size
            __lowercase					=get_size_dict(__lowercase						,      param_name='crop_size'      )
            __lowercase					=do_resize if do_resize is not None else self.do_resize
            __lowercase					=size if size is not None else self.size
            __lowercase					=get_size_dict(__lowercase      )
            __lowercase					=resample if resample is not None else self.resample
            __lowercase					=do_rescale if do_rescale is not None else self.do_rescale
            __lowercase					=rescale_factor if rescale_factor is not None else self.rescale_factor
            __lowercase					=do_normalize if do_normalize is not None else self.do_normalize
            __lowercase					=image_mean if image_mean is not None else self.image_mean
            __lowercase					=image_std if image_std is not None else self.image_std

            __lowercase					=make_list_of_images(__lowercase      )

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

            if do_center_crop and crop_size is None:
                 raise ValueError('If `do_center_crop` is set to `True`, `crop_size` must be provided.'      )

            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.'      )

            if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError('Image mean and image standard deviation must be specified if do_normalize is True.'      )

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

            if do_center_crop:
                 __lowercase					=[self.center_crop(__lowercase						,      __lowercase						,      size=__lowercase      ) for image in images]

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

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

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

            __lowercase					=[to_channel_dimension_format(__lowercase						,      __lowercase      ) for image in images]

            __lowercase					={'pixel_values': images}
            return BatchFeature(data=__lowercase						,      tensor_type=__lowercase      )


