

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__								=		pd.read_csv('''sample_data.csv''', header=None)
							lowerCAmelCase__								=		df.shape[:1][0]
							# If you're using some other dataset input the target column
							lowerCAmelCase__								=		df.iloc[:, 1:2]
							lowerCAmelCase__								=		actual_data.values.reshape(len_data, 1)
							lowerCAmelCase__								=		MinMaxScaler().fit_transform(actual_data)
							lowerCAmelCase__								=		1_0
							lowerCAmelCase__								=		5
							lowerCAmelCase__								=		2_0
							lowerCAmelCase__								=		len_data - periods * look_back
							lowerCAmelCase__								=		actual_data[:division]
							lowerCAmelCase__								=		actual_data[division - look_back :]
							lowerCAmelCase__								=		[], []
							lowerCAmelCase__								=		[], []

							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__								=		np.array(train_x)
							lowerCAmelCase__								=		np.array(test_x)
							lowerCAmelCase__								=		np.array([list(i.ravel()) for i in train_y])
							lowerCAmelCase__								=		np.array([list(i.ravel()) for i in test_y])

							lowerCAmelCase__								=		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__								=		model.fit(
							    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
							)
							lowerCAmelCase__								=		model.predict(x_test)







# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCAmelCase__								=		abspath(join(dirname(__file__), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				Union[str, Any]:




	'''simple docstring'''




	config.addinivalue_line(
	    'markers'							,       'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'	)
	config.addinivalue_line(
	    'markers'							,       'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'	)
	config.addinivalue_line('markers'							,       'is_pipeline_test: mark test to run only when pipelines are tested'	)
	config.addinivalue_line('markers'							,       'is_staging_test: mark test to run only in the staging environment'	)
	config.addinivalue_line('markers'							,       'accelerate_tests: mark test that require accelerate'	)
	config.addinivalue_line('markers'							,       'tool_tests: mark the tool tests that are run on their specific schedule'	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]	)						->				List[Any]:




	'''simple docstring'''




	from transformers.testing_utils import pytest_addoption_shared

	pytest_addoption_shared(UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				List[Any]:




	'''simple docstring'''




	from transformers.testing_utils import pytest_terminal_summary_main

	_UpperCamelCase   :		str			=						terminalreporter.config.getoption('--make-reports'	)
	if make_reports:
		pytest_terminal_summary_main(UpperCAmelCase_							,       id=UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict							,       UpperCAmelCase_							:     Dict	)						->				Tuple:




	'''simple docstring'''




	if exitstatus == 5:
		_UpperCamelCase   :		List[Any]			=						0


# Doctest custom flag to ignore output.
lowerCAmelCase__								=		doctest.register_optionflag("""IGNORE_RESULT""")

lowerCAmelCase__								=		doctest.OutputChecker







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			if IGNORE_RESULT & optionflags:
				return True
			return OutputChecker.check_output(self	,       __snake_case	,       __snake_case	,       __snake_case)

lowerCAmelCase__								=		CustomOutputChecker
lowerCAmelCase__								=		HfDoctestModule







lowerCAmelCase__								=		HfDocTestParser




