


import os
from datetime import datetime as dt

from github import Github


lowerCAmelCase__  :List[Any]             =							[
    '''good first issue''',
    '''feature request''',
    '''wip''',
]

def 	lowerCAmelCase__			(      )  ->		Tuple:
							_UpperCAmelCase					=      Github(os.environ['GITHUB_TOKEN']	)
							_UpperCAmelCase					=      g.get_repo('huggingface/accelerate'	)
							_UpperCAmelCase					=      repo.get_issues(state='open'	)

							for issue in open_issues:
														_UpperCAmelCase					=      sorted([comment for comment in issue.get_comments()]      ,  key=lambda a__	: i.created_at      ,  reverse=a__	)
														_UpperCAmelCase					=      comments[0] if len(a__	) > 0 else None
														_UpperCAmelCase					=      dt.utcnow()
														_UpperCAmelCase					=      (current_time - issue.updated_at).days
														_UpperCAmelCase					=      (current_time - issue.created_at).days
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and days_since_updated > 7
														    and days_since_creation >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# Close issue since it has been 7 days of inactivity since bot mention.
																					issue.edit(state='closed'	)
														elif (
														    days_since_updated > 2_3
														    and days_since_creation >= 3_0
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()	)
														):
																					# Add stale comment
																					issue.create_comment(
																					    'This issue has been automatically marked as stale because it has not had '
																					    'recent activity. If you think this still needs to be addressed '
																					    'please comment on this thread.\n\nPlease note that issues that do not follow the '
																					    '[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) '
																					    'are likely to be ignored.'	)


if __name__ == "__main__":
		main()




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)
