



"""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:     Union[str, Any]										=	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:     Optional[int]										=	df.iloc[:, 1:2]
      A:     Optional[int]										=	actual_data.values.reshape(len_data, 1)
      A:     Optional[Any]										=	MinMaxScaler().fit_transform(actual_data)
      A:     int										=	1_0
      A:     List[str]										=	5
      A:     int										=	2_0
      A:     Any										=	len_data - periods * look_back
      A:     Optional[Any]										=	actual_data[:division]
      A:     List[str]										=	actual_data[division - look_back :]
      A							,   A:     Tuple										=	[], []
      A							,   A:     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])
      A:     Union[str, Any]										=	np.array(train_x)
      A:     str										=	np.array(test_x)
      A:     List[str]										=	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:     Union[str, Any]										=	model.fit(
          x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
      )
      A:     str										=	model.predict(x_test)





"""simple docstring"""
from typing import TYPE_CHECKING

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


A:     Union[str, Any]										=	{
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     List[str]										=	["SpeechT5Tokenizer"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:     Dict										=	[
          "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
          "SpeechT5ForSpeechToText",
          "SpeechT5ForSpeechToSpeech",
          "SpeechT5ForTextToSpeech",
          "SpeechT5Model",
          "SpeechT5PreTrainedModel",
          "SpeechT5HifiGan",
      ]

if TYPE_CHECKING:
      from .configuration_speechta import (
          SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
          SpeechTaConfig,
          SpeechTaHifiGanConfig,
      )
      from .feature_extraction_speechta import SpeechTaFeatureExtractor
      from .processing_speechta import SpeechTaProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speechta import SpeechTaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speechta import (
                SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechTaForSpeechToSpeech,
                SpeechTaForSpeechToText,
                SpeechTaForTextToSpeech,
                SpeechTaHifiGan,
                SpeechTaModel,
                SpeechTaPreTrainedModel,
            )

else:
      import sys

      A:     Dict										=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
