






import argparse

import torch

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


logging.set_verbosity_info()
__snake_case      :      Optional[Any]             =       logging.get_logger(__name__)







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase_      ,    config=UpperCamelCase_						)
						lowerCAmelCase__        =  downstream_dict['projector.weight']
						lowerCAmelCase__        =  downstream_dict['projector.bias']
						lowerCAmelCase__        =  downstream_dict['model.post_net.linear.weight']
						lowerCAmelCase__        =  downstream_dict['model.post_net.linear.bias']
						return model







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   int						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase_      ,    config=UpperCamelCase_						)
						lowerCAmelCase__        =  downstream_dict['model.linear.weight']
						lowerCAmelCase__        =  downstream_dict['model.linear.bias']
						return model







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]						)					->				Any:

						"""simple docstring"""



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

						lowerCAmelCase__        =  downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
						lowerCAmelCase__        =  downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
						lowerCAmelCase__        =  downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
						lowerCAmelCase__        =  downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
						lowerCAmelCase__        =  downstream_dict['objective.W']
						return model







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[int]						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  torch.load(UpperCamelCase_      ,    map_location='cpu'						)

						lowerCAmelCase__        =  checkpoint['Downstream']

						lowerCAmelCase__        =  WavaVecaConfig.from_pretrained(UpperCamelCase_						)
						lowerCAmelCase__        =  WavaVecaFeatureExtractor.from_pretrained(
						    UpperCamelCase_      ,    return_attention_mask=UpperCamelCase_      ,    do_normalize=UpperCamelCase_						)

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

						if hf_config.use_weighted_layer_sum:
												lowerCAmelCase__        =  checkpoint['Featurizer']['weights']

						hf_feature_extractor.save_pretrained(UpperCamelCase_						)
						hf_model.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case      :      List[str]             =       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.""")
					__snake_case      :      List[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 (
    center_crop,
    get_resize_output_image_size,
    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


__snake_case      :      Any             =       logging.get_logger(__name__)


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''pixel_values''']






						def __init__(		self       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PILImageResampling.BILINEAR       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = True       ,  _UpperCamelCase = 1 / 2_55       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__(**_UpperCamelCase				)
												lowerCAmelCase__        =  size if size is not None else {'shortest_edge': 2_56}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_resize
												lowerCAmelCase__        =  size
												lowerCAmelCase__        =  resample
												lowerCAmelCase__        =  do_center_crop
												lowerCAmelCase__        =  crop_size
												lowerCAmelCase__        =  do_rescale
												lowerCAmelCase__        =  rescale_factor
												lowerCAmelCase__        =  do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCAmelCase__        =  image_std if image_std is not None else IMAGENET_STANDARD_STD






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = PILImageResampling.BICUBIC       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												if "shortest_edge" not in size:
																		raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"				)
												lowerCAmelCase__        =  get_resize_output_image_size(_UpperCamelCase       ,  size=size['shortest_edge']       ,  default_to_square=_UpperCamelCase				)
												return resize(_UpperCamelCase       ,  size=_UpperCamelCase       ,  resample=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												return center_crop(_UpperCamelCase       ,  size=(size['height'], size['width'])       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase				):






												"""simple docstring"""
												return rescale(_UpperCamelCase       ,  scale=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												return normalize(_UpperCamelCase       ,  mean=_UpperCamelCase       ,  std=_UpperCamelCase       ,  data_format=_UpperCamelCase       ,  **_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = ChannelDimension.FIRST       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  do_resize if do_resize is not None else self.do_resize
												lowerCAmelCase__        =  size if size is not None else self.size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase       ,  default_to_square=_UpperCamelCase				)
												lowerCAmelCase__        =  resample if resample is not None else self.resample
												lowerCAmelCase__        =  do_center_crop if do_center_crop is not None else self.do_center_crop
												lowerCAmelCase__        =  crop_size if crop_size is not None else self.crop_size
												lowerCAmelCase__        =  get_size_dict(_UpperCamelCase				)
												lowerCAmelCase__        =  do_rescale if do_rescale is not None else self.do_rescale
												lowerCAmelCase__        =  rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCAmelCase__        =  do_normalize if do_normalize is not None else self.do_normalize
												lowerCAmelCase__        =  image_mean if image_mean is not None else self.image_mean
												lowerCAmelCase__        =  image_std if image_std is not None else self.image_std

												lowerCAmelCase__        =  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_center_crop and crop_size is None:
																		raise ValueError('Crop size must be specified if do_center_crop 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 std must be specified if do_normalize is True.'				)

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

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

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

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

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

												lowerCAmelCase__        =  [to_channel_dimension_format(_UpperCamelCase       ,  _UpperCamelCase				) for image in images]

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





