





'''simple docstring'''







import argparse

import torch

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


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





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			List[Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Union[str, Any]     						=  WavaVecaForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Dict     						=  downstream_dict['''projector.weight''']
						UpperCAmelCase_			:      int     						=  downstream_dict['''projector.bias''']
						UpperCAmelCase_			:      List[Any]     						=  downstream_dict['''model.post_net.linear.weight''']
						UpperCAmelCase_			:      Any     						=  downstream_dict['''model.post_net.linear.bias''']
						return model





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ) ->	List[Any]:
						UpperCAmelCase_			:      Dict     						=  WavaVecaForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE__,			config=SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      str     						=  downstream_dict['''model.linear.weight''']
						UpperCAmelCase_			:      Optional[int]     						=  downstream_dict['''model.linear.bias''']
						return model





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

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





@torch.no_grad()
def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Tuple,			SCREAMING_SNAKE_CASE__      :			Optional[Any],			SCREAMING_SNAKE_CASE__      :			Any,			SCREAMING_SNAKE_CASE__      :			int   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      Optional[int]     						=  torch.load(SCREAMING_SNAKE_CASE__,			map_location='''cpu'''   )

						UpperCAmelCase_			:      List[str]     						=  checkpoint['''Downstream''']

						UpperCAmelCase_			:      List[str]     						=  WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__   )
						UpperCAmelCase_			:      Any     						=  WavaVecaFeatureExtractor.from_pretrained(
						    SCREAMING_SNAKE_CASE__,			return_attention_mask=SCREAMING_SNAKE_CASE__,			do_normalize=SCREAMING_SNAKE_CASE__   )

						UpperCAmelCase_			:      Tuple     						=  hf_config.architectures[0]
						if arch.endswith('''ForSequenceClassification'''   ):
												UpperCAmelCase_			:      List[Any]     						=  convert_classification(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )
						elif arch.endswith('''ForAudioFrameClassification'''   ):
												UpperCAmelCase_			:      int     						=  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_			:      str     						=  checkpoint['''Featurizer''']['''weights''']

						hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__   )
						hf_model.save_pretrained(SCREAMING_SNAKE_CASE__   )


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












'''simple docstring'''







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__":
					snake_case_     :       List[Any]						=  pd.read_csv("sample_data.csv", header=None)
					snake_case_     :       Optional[Any]						=  df.shape[:1][0]
					# If you're using some other dataset input the target column
					snake_case_     :       Any						=  df.iloc[:, 1:2]
					snake_case_     :       str						=  actual_data.values.reshape(len_data, 1)
					snake_case_     :       Optional[Any]						=  MinMaxScaler().fit_transform(actual_data)
					snake_case_     :       List[str]						=  10
					snake_case_     :       Any						=  5
					snake_case_     :       Any						=  20
					snake_case_     :       Tuple						=  len_data - periods * look_back
					snake_case_     :       str						=  actual_data[:division]
					snake_case_     :       Optional[int]						=  actual_data[division - look_back :]
					snake_case_			,snake_case_     :       Any						=  [], []
					snake_case_			,snake_case_     :       Union[str, Any]						=  [], []

					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])
					snake_case_     :       Any						=  np.array(train_x)
					snake_case_     :       Optional[Any]						=  np.array(test_x)
					snake_case_     :       Optional[Any]						=  np.array([list(i.ravel()) for i in train_y])
					snake_case_     :       List[str]						=  np.array([list(i.ravel()) for i in test_y])

					snake_case_     :       List[Any]						=  Sequential()
					model.add(LSTM(1_28, input_shape=(look_back, 1), return_sequences=True))
					model.add(LSTM(64, input_shape=(1_28, 1)))
					model.add(Dense(forward_days))
					model.compile(loss="mean_squared_error", optimizer="adam")
					snake_case_     :       Dict						=  model.fit(
					    x_train, y_train, epochs=1_50, verbose=1, shuffle=True, batch_size=4
					)
					snake_case_     :       Optional[Any]						=  model.predict(x_test)





