






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 :      Dict										=				df.shape[:1][0]
 # If you're using some other dataset input the target column
 __lowerCamelCase :      List[Any]										=				df.iloc[:, 1:2]
 __lowerCamelCase :      Optional[Any]										=				actual_data.values.reshape(len_data, 1)
 __lowerCamelCase :      str										=				MinMaxScaler().fit_transform(actual_data)
 __lowerCamelCase :      int										=				10
 __lowerCamelCase :      Optional[int]										=				5
 __lowerCamelCase :      Optional[int]										=				20
 __lowerCamelCase :      str										=				len_data - periods * look_back
 __lowerCamelCase :      Union[str, Any]										=				actual_data[:division]
 __lowerCamelCase :      Optional[int]										=				actual_data[division - look_back :]
 __lowerCamelCase :      Optional[int]										=				[], []
 __lowerCamelCase :      Optional[int]										=				[], []

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

 __lowerCamelCase :      Optional[Any]										=				Sequential()
 model.add(LSTM(128, input_shape=(look_back, 1), return_sequences=True))
 model.add(LSTM(64, input_shape=(128, 1)))
 model.add(Dense(forward_days))
 model.compile(loss="""mean_squared_error""", optimizer="""adam""")
 __lowerCamelCase :      Any										=				model.fit(
     x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
 )
 __lowerCamelCase :      Tuple										=				model.predict(x_test)











import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Tuple					,  snake_case_ :							str					,  snake_case_ :							Union[str, Any]       ):
	# Initialise PyTorch model
	snake_case__			:				List[str]       =  MobileBertConfig.from_json_file(snake_case_       )
	print(F'''Building PyTorch model from configuration: {config}'''       )
	snake_case__			:				Dict       =  MobileBertForPreTraining(snake_case_       )
	# Load weights from tf checkpoint
	snake_case__			:				Any       =  load_tf_weights_in_mobilebert(snake_case_					,  snake_case_					,  snake_case_       )
	# Save pytorch-model
	print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
	torch.save(model.state_dict()					,  snake_case_       )


if __name__ == "__main__":
	__lowerCamelCase :      List[Any]										=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
	)
	parser.add_argument(
	    """--mobilebert_config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        """The config json file corresponding to the pre-trained MobileBERT model. \n"""
	        """This specifies the model architecture."""
	    ),
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	__lowerCamelCase :      int										=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



