





'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
snake_case_     :       str						=  logging.get_logger(__name__)





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Union[str, Any]     						=  WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''projector.weight''']
						UpperCAmelCase_			:      int     						=  downstream_dict['''projector.bias''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''model.post_net.linear.weight''']
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.post_net.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Dict     						=  WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  downstream_dict['''model.linear.weight''']
						UpperCAmelCase_			:      Optional[int]     						=  downstream_dict['''model.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			int   ) ->	Any:
						UpperCAmelCase_			:      int     						=  WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''connector.weight''']
						UpperCAmelCase_			:      Any     						=  downstream_dict['''connector.bias''']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
												UpperCAmelCase_			:      List[Any]     						=  downstream_dict[
												    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
												]
												UpperCAmelCase_			:      Optional[Any]     						=  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

						UpperCAmelCase_			:      Union[str, Any]     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
						UpperCAmelCase_			:      int     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
						UpperCAmelCase_			:      Optional[Any]     						=  downstream_dict['''objective.W''']
						return model





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			int   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Optional[int]     						=  torch.load(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )

						UpperCAmelCase_			:      List[str]     						=  checkpoint['''Downstream''']

						UpperCAmelCase_			:      List[str]     						=  WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  WavaVecaFeatureExtractor.from_pretrained(
						    SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			do_normalize=SCREAMING_SNAKE_CASE__   )

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

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

						hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       Union[str, 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.")
					snake_case_     :       Any						=  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 List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







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



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





