



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

      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:   Optional[int]								=		np.array(train_x)
      A:   Optional[Any]								=		np.array(test_x)
      A:   int								=		np.array([list(i.ravel()) for i in train_y])
      A:   str								=		np.array([list(i.ravel()) for i in test_y])

      A:   Dict								=		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")
      A:   int								=		model.fit(
          x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
      )
      A:   List[str]								=		model.predict(x_test)




from typing import TYPE_CHECKING

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


_A		       =						{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

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

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ConvBertForMaskedLM',
       'ConvBertForMultipleChoice',
       'ConvBertForQuestionAnswering',
       'ConvBertForSequenceClassification',
       'ConvBertForTokenClassification',
       'ConvBertLayer',
       'ConvBertModel',
       'ConvBertPreTrainedModel',
       'load_tf_weights_in_convbert',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFConvBertForMaskedLM',
       'TFConvBertForMultipleChoice',
       'TFConvBertForQuestionAnswering',
       'TFConvBertForSequenceClassification',
       'TFConvBertForTokenClassification',
       'TFConvBertLayer',
       'TFConvBertModel',
       'TFConvBertPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

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





