import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase    :  Dict										= logging.get_logger(__name__)

def   a_					(_lowerCAmelCase  :   List[Any]			,	_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   Dict       )->				str:
    snake_case:						Union[str, Any]         =   WavLMForSequenceClassification.from_pretrained(_lowerCAmelCase			,	config=_lowerCAmelCase       )
    snake_case:						List[str]         =   downstream_dict["""projector.weight"""]
    snake_case:						List[Any]         =   downstream_dict["""projector.bias"""]
    snake_case:						Tuple         =   downstream_dict["""model.post_net.linear.weight"""]
    snake_case:						Dict         =   downstream_dict["""model.post_net.linear.bias"""]
    return model

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Union[str, Any]			,	_lowerCAmelCase  :   Dict       )->				Tuple:
    snake_case:						Dict         =   WavLMForAudioFrameClassification.from_pretrained(_lowerCAmelCase			,	config=_lowerCAmelCase       )
    snake_case:						List[str]         =   downstream_dict["""model.linear.weight"""]
    snake_case:						str         =   downstream_dict["""model.linear.bias"""]
    return model

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   Dict			,	_lowerCAmelCase  :   Tuple       )->				Optional[Any]:
    snake_case:						Optional[int]         =   WavLMForXVector.from_pretrained(_lowerCAmelCase			,	config=_lowerCAmelCase       )
    snake_case:						int         =   downstream_dict["""connector.weight"""]
    snake_case:						Optional[Any]         =   downstream_dict["""connector.bias"""]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
        snake_case:						Dict         =   downstream_dict[
            F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        snake_case:						str         =   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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

@torch.no_grad()
def   a_					(_lowerCAmelCase  :   Dict			,	_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   Optional[int]			,	_lowerCAmelCase  :   Any       )->				Union[str, Any]:
    snake_case:						List[Any]         =   torch.load(_lowerCAmelCase			,	map_location="""cpu"""       )

    snake_case:						str         =   checkpoint["""Downstream"""]

    snake_case:						Any         =   WavLMConfig.from_pretrained(_lowerCAmelCase       )
    snake_case:						Dict         =   WavaVecaFeatureExtractor.from_pretrained(
        _lowerCAmelCase			,	return_attention_mask=_lowerCAmelCase			,	do_normalize=_lowerCAmelCase       )

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

    if hf_config.use_weighted_layer_sum:
        snake_case:						Optional[Any]         =   checkpoint["""Featurizer"""]["""weights"""]

    hf_feature_extractor.save_pretrained(_lowerCAmelCase       )
    hf_model.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
    __lowerCAmelCase    :  int										= 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    :  Union[str, Any]										= parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



from __future__ import annotations

from statistics import mean

def   a_					(_lowerCAmelCase  :   list[int]       )->				list[int]:
    snake_case:						Tuple         =   2
    snake_case:						str         =   list(_lowerCAmelCase       )
    snake_case:						Union[str, Any]         =   [0] * len(_lowerCAmelCase       )
    snake_case:						List[Any]         =   0
    while True:
        snake_case:						Optional[int]         =   True
        for i, burst_time in enumerate(_lowerCAmelCase       ):
            if rem_burst_times[i] > 0:
                snake_case:						Dict         =   False
                if rem_burst_times[i] > quantum:
                    t += quantum
                    rem_burst_times[i] -= quantum
                else:
                    t += rem_burst_times[i]
                    snake_case:						Optional[Any]         =   t - burst_time
                    snake_case:						Tuple         =   0
        if done is True:
            return waiting_times

def   a_					(_lowerCAmelCase  :   list[int]			,	_lowerCAmelCase  :   list[int]       )->				list[int]:
    return [burst + waiting for burst, waiting in zip(_lowerCAmelCase			,	_lowerCAmelCase       )]


if __name__ == "__main__":
    __lowerCAmelCase    :  List[Any]										= [3, 5, 7]
    __lowerCAmelCase    :  List[Any]										= calculate_waiting_times(burst_times)
    __lowerCAmelCase    :  str										= calculate_turn_around_times(burst_times, waiting_times)
    print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
    for i, burst_time in enumerate(burst_times):
        print(
            F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
            F"""{turn_around_times[i]}"""
        )
    print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
    print(F"""Average turn around time = {mean(turn_around_times):.5f}""")


