


import argparse

import torch

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


logging.set_verbosity_info()
lowerCAmelCase__  :Tuple             =							logging.get_logger(__name__)

def 	lowerCAmelCase__			(      a__:		str      ,  a__:		int      ,  a__:		int	)  ->		Optional[Any]:
							_UpperCAmelCase					=      WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase      ,  config=_lowerCamelCase	)
							_UpperCAmelCase					=      downstream_dict["projector.weight"]
							_UpperCAmelCase					=      downstream_dict["projector.bias"]
							_UpperCAmelCase					=      downstream_dict["model.post_net.linear.weight"]
							_UpperCAmelCase					=      downstream_dict["model.post_net.linear.bias"]
							return model

def 	lowerCAmelCase__			(      a__:		Dict      ,  a__:		Tuple      ,  a__:		str	)  ->		Any:
							_UpperCAmelCase					=      WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase      ,  config=_lowerCamelCase	)
							_UpperCAmelCase					=      downstream_dict["model.linear.weight"]
							_UpperCAmelCase					=      downstream_dict["model.linear.bias"]
							return model

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

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

@torch.no_grad()
def 	lowerCAmelCase__			(      a__:		Optional[int]      ,  a__:		Union[str, Any]      ,  a__:		Optional[int]      ,  a__:		str	)  ->		Dict:
							_UpperCAmelCase					=      torch.load(_lowerCamelCase      ,  map_location='cpu'	)

							_UpperCAmelCase					=      checkpoint["Downstream"]

							_UpperCAmelCase					=      WavaVecaConfig.from_pretrained(_lowerCamelCase	)
							_UpperCAmelCase					=      WavaVecaFeatureExtractor.from_pretrained(
							    _lowerCamelCase      ,  return_attention_mask=_lowerCamelCase      ,  do_normalize=_lowerCamelCase	)

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

							if hf_config.use_weighted_layer_sum:
														_UpperCAmelCase					=      checkpoint["Featurizer"]["weights"]

							hf_feature_extractor.save_pretrained(_lowerCamelCase	)
							hf_model.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
		lowerCAmelCase__  :List[str]             =							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)




import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential

if __name__ == "__main__":
		lowerCAmelCase__  :Dict             =							pd.read_csv('''sample_data.csv''', header=None)
		lowerCAmelCase__  :int             =							df.shape[:1][0]
		# If you're using some other dataset input the target column
		lowerCAmelCase__  :Union[str, Any]             =							df.iloc[:, 1:2]
		lowerCAmelCase__  :Optional[int]             =							actual_data.values.reshape(len_data, 1)
		lowerCAmelCase__  :Tuple             =							MinMaxScaler().fit_transform(actual_data)
		lowerCAmelCase__  :str             =							1_0
		lowerCAmelCase__  :Optional[Any]             =							5
		lowerCAmelCase__  :List[str]             =							2_0
		lowerCAmelCase__  :Any             =							len_data - periods * look_back
		lowerCAmelCase__  :Union[str, Any]             =							actual_data[:division]
		lowerCAmelCase__  :Tuple             =							actual_data[division - look_back :]
		lowerCAmelCase__      ,  lowerCAmelCase__  :Optional[int]             =							[], []
		lowerCAmelCase__      ,  lowerCAmelCase__  :str             =							[], []

		for i in range(0, len(train_data) - forward_days - look_back + 1):
				train_x.append(train_data[i : i + look_back])
				train_y.append(train_data[i + look_back : i + look_back + forward_days])
		for i in range(0, len(test_data) - forward_days - look_back + 1):
				test_x.append(test_data[i : i + look_back])
				test_y.append(test_data[i + look_back : i + look_back + forward_days])
		lowerCAmelCase__  :Optional[Any]             =							np.array(train_x)
		lowerCAmelCase__  :Any             =							np.array(test_x)
		lowerCAmelCase__  :Dict             =							np.array([list(i.ravel()) for i in train_y])
		lowerCAmelCase__  :Tuple             =							np.array([list(i.ravel()) for i in test_y])

		lowerCAmelCase__  :Optional[int]             =							Sequential()
		model.add(LSTM(1_2_8, input_shape=(look_back, 1), return_sequences=True))
		model.add(LSTM(6_4, input_shape=(1_2_8, 1)))
		model.add(Dense(forward_days))
		model.compile(loss='''mean_squared_error''', optimizer='''adam''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
