
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase							=				{
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				['''LongformerTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LongformerForMaskedLM''',
					    '''LongformerForMultipleChoice''',
					    '''LongformerForQuestionAnswering''',
					    '''LongformerForSequenceClassification''',
					    '''LongformerForTokenClassification''',
					    '''LongformerModel''',
					    '''LongformerPreTrainedModel''',
					    '''LongformerSelfAttention''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							=				[
					    '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLongformerForMaskedLM''',
					    '''TFLongformerForMultipleChoice''',
					    '''TFLongformerForQuestionAnswering''',
					    '''TFLongformerForSequenceClassification''',
					    '''TFLongformerForTokenClassification''',
					    '''TFLongformerModel''',
					    '''TFLongformerPreTrainedModel''',
					    '''TFLongformerSelfAttention''',
					]


if TYPE_CHECKING:
					from .configuration_longformer import (
					    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LongformerConfig,
					    LongformerOnnxConfig,
					)
					from .tokenization_longformer import LongformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_longformer_fast import LongformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longformer import (
										    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongformerForMaskedLM,
										    LongformerForMultipleChoice,
										    LongformerForQuestionAnswering,
										    LongformerForSequenceClassification,
										    LongformerForTokenClassification,
										    LongformerModel,
										    LongformerPreTrainedModel,
										    LongformerSelfAttention,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_longformer import (
										    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLongformerForMaskedLM,
										    TFLongformerForMultipleChoice,
										    TFLongformerForQuestionAnswering,
										    TFLongformerForSequenceClassification,
										    TFLongformerForTokenClassification,
										    TFLongformerModel,
										    TFLongformerPreTrainedModel,
										    TFLongformerSelfAttention,
										)

else:
					import sys

					_lowercase							=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""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__":
					_lowercase							=				pd.read_csv('''sample_data.csv''', header=None)
					_lowercase							=				df.shape[:1][0]
					# If you're using some other dataset input the target column
					_lowercase							=				df.iloc[:, 1:2]
					_lowercase							=				actual_data.values.reshape(len_data, 1)
					_lowercase							=				MinMaxScaler().fit_transform(actual_data)
					_lowercase							=				10
					_lowercase							=				5
					_lowercase							=				20
					_lowercase							=				len_data - periods * look_back
					_lowercase							=				actual_data[:division]
					_lowercase							=				actual_data[division - look_back :]
					_lowercase			,       _lowercase							=				[], []
					_lowercase			,       _lowercase							=				[], []

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

					_lowercase							=				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''')
					_lowercase							=				model.fit(
					    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
					)
					_lowercase							=				model.predict(x_test)





