






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A     :      Union[str, Any]   					={
    "configuration_altclip": [
        "ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AltCLIPConfig",
        "AltCLIPTextConfig",
        "AltCLIPVisionConfig",
    ],
    "processing_altclip": ["AltCLIPProcessor"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :      Any   					=[
       "ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
       "AltCLIPPreTrainedModel",
       "AltCLIPModel",
       "AltCLIPTextModel",
       "AltCLIPVisionModel",
   ]


if TYPE_CHECKING:
   from .configuration_altclip import (
       ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
       AltCLIPConfig,
       AltCLIPTextConfig,
       AltCLIPVisionConfig,
   )
   from .processing_altclip import AltCLIPProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_altclip import (
          ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
          AltCLIPModel,
          AltCLIPPreTrainedModel,
          AltCLIPTextModel,
          AltCLIPVisionModel,
      )


else:
   import sys

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

			_A     :      Optional[Any]   					=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''')
			_A     :      Dict   					=model.fit(
			    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
			)
			_A     :      List[str]   					=model.predict(x_test)

