





'''simple docstring'''







import argparse
from collections import defaultdict





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[int],			SCREAMING_SNAKE_CASE__      :			Dict,			SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			List[str],			SCREAMING_SNAKE_CASE__      :			int   ) ->	Union[str, Any]:
						UpperCAmelCase_			:      List[Any]     						=  F"""{file}_{class_name}_{test_name}"""
						done_test[_id] += 1

						with open(SCREAMING_SNAKE_CASE__,			'''r'''   ) as f:
												UpperCAmelCase_			:      List[str]     						=  f.readlines()

						UpperCAmelCase_			:      List[str]     						=  F"""class {class_name}("""
						UpperCAmelCase_			:      int     						=  F"""{4 * " "}def {test_name}("""
						UpperCAmelCase_			:      Any     						=  F"""{8 * " "}{correct_line.split()[0]}"""
						UpperCAmelCase_			:      str     						=  F"""{16 * " "}{correct_line.split()[0]}"""
						UpperCAmelCase_			:      Tuple     						=  False
						UpperCAmelCase_			:      Any     						=  False
						UpperCAmelCase_			:      List[str]     						=  False
						UpperCAmelCase_			:      Dict     						=  False
						UpperCAmelCase_			:      Optional[int]     						=  0
						UpperCAmelCase_			:      str     						=  0

						UpperCAmelCase_			:      Dict     						=  []
						for line in lines:
												if line.startswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      List[Any]     						=  True
												elif in_class and line.startswith(SCREAMING_SNAKE_CASE__   ):
																		UpperCAmelCase_			:      List[Any]     						=  True
												elif in_class and in_func and (line.startswith(SCREAMING_SNAKE_CASE__   ) or line.startswith(SCREAMING_SNAKE_CASE__   )):
																		UpperCAmelCase_			:      str     						=  len(line.split(correct_line.split()[0]   )[0]   )
																		count += 1

																		if count == done_test[_id]:
																								UpperCAmelCase_			:      Optional[Any]     						=  True

												if in_class and in_func and in_line:
																		if ")" not in line:
																								continue
																		else:
																								UpperCAmelCase_			:      str     						=  True

												if in_class and in_func and in_line and insert_line:
																		new_lines.append(F"""{spaces * " "}{correct_line}"""   )
																		UpperCAmelCase_			:      int     						=  False
												else:
																		new_lines.append(SCREAMING_SNAKE_CASE__   )

						with open(SCREAMING_SNAKE_CASE__,			'''w'''   ) as f:
												for line in new_lines:
																		f.write(SCREAMING_SNAKE_CASE__   )





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			str,			SCREAMING_SNAKE_CASE__      :			Optional[Any]=None   ) ->	str:
						if fail is not None:
												with open(SCREAMING_SNAKE_CASE__,			'''r'''   ) as f:
																		UpperCAmelCase_			:      List[Any]     						=  {l.strip() for l in f.readlines()}
						else:
												UpperCAmelCase_			:      Dict     						=  None

						with open(SCREAMING_SNAKE_CASE__,			'''r'''   ) as f:
												UpperCAmelCase_			:      Dict     						=  f.readlines()

						UpperCAmelCase_			:      int     						=  defaultdict(SCREAMING_SNAKE_CASE__   )
						for line in correct_lines:
												UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_			:      Tuple     						=  line.split(''';'''   )
												if test_failures is None or "::".join([file, class_name, test_name]   ) in test_failures:
																		overwrite_file(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )


if __name__ == "__main__":
					snake_case_     :       Optional[int]						=  argparse.ArgumentParser()
					parser.add_argument("--correct_filename", help="filename of tests with expected result")
					parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
					snake_case_     :       str						=  parser.parse_args()

					main(args.correct_filename, args.fail_filename)












'''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)





