"""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__":
       __lowerCAmelCase    :       List[Any]					     =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    :       Optional[Any]					     =df.iloc[:, 1:2]
       __lowerCAmelCase    :       List[str]					     =actual_data.values.reshape(len_data, 1)
       __lowerCAmelCase    :       List[Any]					     =MinMaxScaler().fit_transform(actual_data)
       __lowerCAmelCase    :       List[Any]					     =1_0
       __lowerCAmelCase    :       List[Any]					     =5
       __lowerCAmelCase    :       Optional[int]					     =2_0
       __lowerCAmelCase    :       str					     =len_data - periods * look_back
       __lowerCAmelCase    :       str					     =actual_data[:division]
       __lowerCAmelCase    :       Optional[Any]					     =actual_data[division - look_back :]
       __lowerCAmelCase     ,     __lowerCAmelCase    :       Dict					     =[], []
       __lowerCAmelCase     ,     __lowerCAmelCase    :       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])
       __lowerCAmelCase    :       List[Any]					     =np.array(train_x)
       __lowerCAmelCase    :       List[str]					     =np.array(test_x)
       __lowerCAmelCase    :       Optional[Any]					     =np.array([list(i.ravel()) for i in train_y])
       __lowerCAmelCase    :       Optional[int]					     =np.array([list(i.ravel()) for i in test_y])

       __lowerCAmelCase    :       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    :       Tuple					     =model.fit(
           x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
       )
       __lowerCAmelCase    :       Optional[Any]					     =model.predict(x_test)







'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__    					=      namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Optional[int]:
    return (abs(source - target						) / target) < 0.0_1







@pytest.mark.integration
def 					a__       (					lowerCAmelCase__						)    ->	Optional[int]:
    UpperCAmelCase__			:		str													=				_TestCommandArgs(dataset=lowerCAmelCase__  ,					all_configs=lowerCAmelCase__  ,					save_infos=lowerCAmelCase__						)
    UpperCAmelCase__			:		Tuple													=				TestCommand(*lowerCAmelCase__						)
    test_command.run()
    UpperCAmelCase__			:		List[Any]													=				os.path.join(lowerCAmelCase__  ,					'''README.md'''						)
    assert os.path.exists(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				DatasetInfosDict.from_directory(lowerCAmelCase__						)
    UpperCAmelCase__			:		Any													=				DatasetInfosDict(
        {
            '''default''': DatasetInfo(
                features=Features(
                    {
                        '''tokens''': Sequence(Value('''string'''						)						),
                        '''ner_tags''': Sequence(
                            ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']						)						),
                        '''langs''': Sequence(Value('''string'''						)						),
                        '''spans''': Sequence(Value('''string'''						)						),
                    }						)  ,					splits=[
                    {
                        '''name''': '''train''',
                        '''num_bytes''': 2_35_15_63,
                        '''num_examples''': 1_00_00,
                    },
                    {
                        '''name''': '''validation''',
                        '''num_bytes''': 23_84_18,
                        '''num_examples''': 10_00,
                    },
                ]  ,					download_size=3_94_06_80  ,					dataset_size=2_58_99_81  ,					)
        }						)
    assert dataset_infos.keys() == expected_dataset_infos.keys()
    for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
        UpperCAmelCase__						,						UpperCAmelCase__			:		str													=				getattr(dataset_infos['''default''']  ,					lowerCAmelCase__						), getattr(expected_dataset_infos['''default''']  ,					lowerCAmelCase__						)
        if key == "num_bytes":
            assert is_apercent_close(lowerCAmelCase__  ,					lowerCAmelCase__						)
        elif key == "splits":
            assert list(lowerCAmelCase__						) == list(lowerCAmelCase__						)
            for split in result:
                assert result[split].name == expected[split].name
                assert result[split].num_examples == expected[split].num_examples
                assert is_apercent_close(result[split].num_bytes  ,					expected[split].num_bytes						)
        else:
            result == expected
