




'''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__":
   UpperCamelCase_						:      List[Any]       =						pd.read_csv('''sample_data.csv''', header=None)
   UpperCamelCase_						:      Optional[Any]       =						df.shape[:1][0]
   # If you're using some other dataset input the target column
   UpperCamelCase_						:      Tuple       =						df.iloc[:, 1:2]
   UpperCamelCase_						:      Tuple       =						actual_data.values.reshape(len_data, 1)
   UpperCamelCase_						:      List[Any]       =						MinMaxScaler().fit_transform(actual_data)
   UpperCamelCase_						:      List[str]       =						10
   UpperCamelCase_						:      Optional[int]       =						5
   UpperCamelCase_						:      Optional[Any]       =						20
   UpperCamelCase_						:      Tuple       =						len_data - periods * look_back
   UpperCamelCase_						:      Dict       =						actual_data[:division]
   UpperCamelCase_						:      Union[str, Any]       =						actual_data[division - look_back :]
   UpperCamelCase_						:      Optional[Any]       =						[], []
   UpperCamelCase_						:      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])
   UpperCamelCase_						:      List[Any]       =						np.array(train_x)
   UpperCamelCase_						:      int       =						np.array(test_x)
   UpperCamelCase_						:      Optional[int]       =						np.array([list(i.ravel()) for i in train_y])
   UpperCamelCase_						:      int       =						np.array([list(i.ravel()) for i in test_y])

   UpperCamelCase_						:      Tuple       =						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''')
   UpperCamelCase_						:      int       =						model.fit(
       x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
   )
   UpperCamelCase_						:      Optional[Any]       =						model.predict(x_test)






'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


