


import argparse

import torch

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


logging.set_verbosity_info()
_lowerCAmelCase:						int   				=       logging.get_logger(__name__)






def   _lowercase(					__a				:						List[str]    ,     __a				:						List[Any]    ,     __a				:						Tuple     ):
							a__      =WavaVecaForSequenceClassification.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['projector.weight']
							a__      =downstream_dict['projector.bias']
							a__      =downstream_dict['model.post_net.linear.weight']
							a__      =downstream_dict['model.post_net.linear.bias']
							return model






def   _lowercase(					__a				:						str    ,     __a				:						List[Any]    ,     __a				:						Tuple     ):
							a__      =WavaVecaForAudioFrameClassification.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['model.linear.weight']
							a__      =downstream_dict['model.linear.bias']
							return model






def   _lowercase(					__a				:						List[Any]    ,     __a				:						Union[str, Any]    ,     __a				:						Union[str, Any]     ):
							a__      =WavaVecaForXVector.from_pretrained(__a    ,     config=__a     )
							a__      =downstream_dict['connector.weight']
							a__      =downstream_dict['connector.bias']
							for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
														a__      =downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														a__      =downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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






@torch.no_grad()
def   _lowercase(					__a				:						Tuple    ,     __a				:						Optional[int]    ,     __a				:						List[Any]    ,     __a				:						List[Any]     ):
							a__      =torch.load(__a    ,     map_location='cpu'     )

							a__      =checkpoint['Downstream']

							a__      =WavaVecaConfig.from_pretrained(__a     )
							a__      =WavaVecaFeatureExtractor.from_pretrained(
							    __a    ,     return_attention_mask=__a    ,     do_normalize=__a     )

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

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

							hf_feature_extractor.save_pretrained(__a     )
							hf_model.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[Any]   				=       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:						Dict   				=       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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_UpperCamelCase			:	Any								=logging.get_logger(__name__)


if is_vision_available():
	import PIL






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Union[str, Any]     =   ["pixel_values"]







		def __init__(					self       ,A__ = True       ,A__ = None       ,A__ = PILImageResampling.BICUBIC       ,A__ = True       ,A__ = None       ,A__ = True       ,A__ = 1 / 255       ,A__ = True       ,A__ = None       ,A__ = None       ,A__ = True       ,**A__       ,):
						super().__init__(**A__		)
						_A	:				List[str]   =      size if size is not None else {'''shortest_edge''': 224}
						_A	:				int   =      get_size_dict(A__       ,default_to_square=A__		)
						_A	:				Optional[int]   =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
						_A	:				Dict   =      get_size_dict(A__       ,default_to_square=A__       ,param_name='''crop_size'''		)

						_A	:				List[str]   =      do_resize
						_A	:				List[str]   =      size
						_A	:				Tuple   =      resample
						_A	:				List[Any]   =      do_center_crop
						_A	:				Dict   =      crop_size
						_A	:				Any   =      do_rescale
						_A	:				Optional[Any]   =      rescale_factor
						_A	:				Dict   =      do_normalize
						_A	:				Optional[int]   =      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						_A	:				Optional[int]   =      image_std if image_std is not None else OPENAI_CLIP_STD
						_A	:				Any   =      do_convert_rgb







		def  A__      (					self       ,A__       ,A__       ,A__ = PILImageResampling.BICUBIC       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      get_size_dict(A__       ,default_to_square=A__		)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""		)
						_A	:				Any   =      get_resize_output_image_size(A__       ,size=size['''shortest_edge''']       ,default_to_square=A__		)
						return resize(A__       ,size=A__       ,resample=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						_A	:				Union[str, Any]   =      get_size_dict(A__		)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""		)
						return center_crop(A__       ,size=(size['''height'''], size['''width'''])       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return rescale(A__       ,scale=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__       ,A__       ,A__ = None       ,**A__       ,):
						return normalize(A__       ,mean=A__       ,std=A__       ,data_format=A__       ,**A__		)







		def  A__      (					self       ,A__       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = ChannelDimension.FIRST       ,**A__       ,):
						_A	:				str   =      do_resize if do_resize is not None else self.do_resize
						_A	:				List[str]   =      size if size is not None else self.size
						_A	:				List[str]   =      get_size_dict(A__       ,param_name='''size'''       ,default_to_square=A__		)
						_A	:				Optional[Any]   =      resample if resample is not None else self.resample
						_A	:				int   =      do_center_crop if do_center_crop is not None else self.do_center_crop
						_A	:				Any   =      crop_size if crop_size is not None else self.crop_size
						_A	:				int   =      get_size_dict(A__       ,param_name='''crop_size'''       ,default_to_square=A__		)
						_A	:				int   =      do_rescale if do_rescale is not None else self.do_rescale
						_A	:				List[str]   =      rescale_factor if rescale_factor is not None else self.rescale_factor
						_A	:				Tuple   =      do_normalize if do_normalize is not None else self.do_normalize
						_A	:				Union[str, Any]   =      image_mean if image_mean is not None else self.image_mean
						_A	:				int   =      image_std if image_std is not None else self.image_std
						_A	:				Optional[Any]   =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						_A	:				Optional[Any]   =      make_list_of_images(A__		)

						if not valid_images(A__		):
										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.'''		)

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
										_A	:				int   =      [convert_to_rgb(A__		) for image in images]

						# All transformations expect numpy arrays.
						_A	:				int   =      [to_numpy_array(A__		) for image in images]

						if do_resize:
										_A	:				List[str]   =      [self.resize(image=A__       ,size=A__       ,resample=A__		) for image in images]

						if do_center_crop:
										_A	:				Union[str, Any]   =      [self.center_crop(image=A__       ,size=A__		) for image in images]

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

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

						_A	:				Dict   =      [to_channel_dimension_format(A__       ,A__		) for image in images]

						_A	:				Dict   =      {'''pixel_values''': images}
						return BatchFeature(data=A__       ,tensor_type=A__		)



