


import argparse

import torch

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


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







def 						lowerCamelCase(   a__	,a__	,a__):
    _SCREAMING_SNAKE_CASE			  =UniSpeechSatForSequenceClassification.from_pretrained(a__	,config=a__)
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''projector.weight''']
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''projector.bias''']
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''model.post_net.linear.weight''']
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''model.post_net.linear.bias''']
    return model







def 						lowerCamelCase(   a__	,a__	,a__):
    _SCREAMING_SNAKE_CASE			  =UniSpeechSatForAudioFrameClassification.from_pretrained(a__	,config=a__)
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''model.linear.weight''']
    _SCREAMING_SNAKE_CASE			  =downstream_dict['''model.linear.bias''']
    return model







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

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







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__	,a__):
    _SCREAMING_SNAKE_CASE			  =torch.load(a__	,map_location='''cpu''')

    _SCREAMING_SNAKE_CASE			  =checkpoint['''Downstream''']

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

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

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

    hf_feature_extractor.save_pretrained(a__)
    hf_model.save_pretrained(a__)


if __name__ == "__main__":
       snake_case_						:			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.''')
       snake_case_						:			Optional[Any]													=							parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=False):
				if smoothing:
								if n == 0:
												raise ValueError('''log10(0) is undefined.''')
								return round(1 + logaa(n / (1 + df))	,3)

				if df == 0:
								raise ZeroDivisionError('''df must be > 0''')
				elif n == 0:
								raise ValueError('''log10(0) is undefined.''')
				return round(logaa(n / df)	,3)







def 						lowerCamelCase(   a__	,a__):
				return round(tf * idf	,3)