


"""simple docstring"""
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:    List[str]		=    logging.get_logger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Any:
							snake_case_           =		UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""projector.weight"""]
							snake_case_           =		downstream_dict["""projector.bias"""]
							snake_case_           =		downstream_dict["""model.post_net.linear.weight"""]
							snake_case_           =		downstream_dict["""model.post_net.linear.bias"""]
							return model







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""model.linear.weight"""]
							snake_case_           =		downstream_dict["""model.linear.bias"""]
							return model







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Tuple:
							snake_case_           =		UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE				, config=_SCREAMING_SNAKE_CASE				)
							snake_case_           =		downstream_dict["""connector.weight"""]
							snake_case_           =		downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
														snake_case_           =		downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														snake_case_           =		downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

							snake_case_           =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							snake_case_           =		downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							snake_case_           =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							snake_case_           =		downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							snake_case_           =		downstream_dict["""objective.W"""]
							return model







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Optional[Any]:
							snake_case_           =		torch.load(_SCREAMING_SNAKE_CASE				, map_location="""cpu"""				)

							snake_case_           =		checkpoint["""Downstream"""]

							snake_case_           =		UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		WavaVecaFeatureExtractor.from_pretrained(
							    _SCREAMING_SNAKE_CASE				, return_attention_mask=_SCREAMING_SNAKE_CASE				, do_normalize=_SCREAMING_SNAKE_CASE				)

							snake_case_           =		hf_config.architectures[0]
							if arch.endswith("""ForSequenceClassification"""				):
														snake_case_           =		convert_classification(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							elif arch.endswith("""ForAudioFrameClassification"""				):
														snake_case_           =		convert_diarization(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)
							elif arch.endswith("""ForXVector"""				):
														snake_case_           =		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:
														snake_case_           =		checkpoint["""Featurizer"""]["""weights"""]

							hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE				)
							hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:    Tuple		=    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.')
				__SCREAMING_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"""
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							snake_case_           =		gray_code_sequence_string(_SCREAMING_SNAKE_CASE				)
							#
							# convert them to integers
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														snake_case_           =		int(sequence[i]				, 2				)

							return sequence







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							snake_case_           =		1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							snake_case_           =		gray_code_sequence_string(bit_count - 1				)

							snake_case_           =		[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														snake_case_           =		"""0""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														snake_case_           =		"""1""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



