import argparse

import torch

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


logging.set_verbosity_info()
__lowercase											=      logging.get_logger(__name__)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	downstream_dict['''projector.weight''']
    __UpperCamelCase  :str    						=	downstream_dict['''projector.bias''']
    __UpperCamelCase  :int    						=	downstream_dict['''model.post_net.linear.weight''']
    __UpperCamelCase  :Dict    						=	downstream_dict['''model.post_net.linear.bias''']
    return model



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Any    						=	downstream_dict['''model.linear.weight''']
    __UpperCamelCase  :Optional[Any]    						=	downstream_dict['''model.linear.bias''']
    return model



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Any    						=	WavaVecaForXVector.from_pretrained(SCREAMING_SNAKE_CASE						,    config=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Any    						=	downstream_dict['''connector.weight''']
    __UpperCamelCase  :List[Any]    						=	downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
        __UpperCamelCase  :Tuple    						=	downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __UpperCamelCase  :Tuple    						=	downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __UpperCamelCase  :Any    						=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __UpperCamelCase  :Optional[int]    						=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __UpperCamelCase  :str    						=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __UpperCamelCase  :List[str]    						=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __UpperCamelCase  :Tuple    						=	downstream_dict['''objective.W''']
    return model



@torch.no_grad()
def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[str]    						=	torch.load(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)

    __UpperCamelCase  :Optional[int]    						=	checkpoint['''Downstream''']

    __UpperCamelCase  :int    						=	WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Tuple    						=	WavaVecaFeatureExtractor.from_pretrained(
        SCREAMING_SNAKE_CASE						,    return_attention_mask=SCREAMING_SNAKE_CASE						,    do_normalize=SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :str    						=	hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''			):
        __UpperCamelCase  :Optional[Any]    						=	convert_classification(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    elif arch.endswith('''ForAudioFrameClassification'''			):
        __UpperCamelCase  :Optional[Any]    						=	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  :int    						=	checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE			)
    hf_model.save_pretrained(SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
    __lowercase											=      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.''')
    __lowercase											=      parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

__lowercase											=      datasets.load_iris()

__lowercase											=      np.array(data['''data'''])
__lowercase											=      np.array(data['''target'''])
__lowercase											=      data['''target_names''']

__lowercase				, __lowercase				, __lowercase				, __lowercase											=      train_test_split(X, y)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return np.linalg.norm(np.array(SCREAMING_SNAKE_CASE			) - np.array(SCREAMING_SNAKE_CASE			)			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=5			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[int]    						=	zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    # List of distances of all points from the point to be classified
    __UpperCamelCase  :List[str]    						=	[]
    for data_point in data:
        __UpperCamelCase  :Optional[int]    						=	euclidean_distance(data_point[0]						,    SCREAMING_SNAKE_CASE			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    __UpperCamelCase  :Any    						=	[i[1] for i in sorted(SCREAMING_SNAKE_CASE			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    __UpperCamelCase  :Union[str, Any]    						=	Counter(SCREAMING_SNAKE_CASE			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
    print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))





