



"""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			:			Dict										=						pd.read_csv("sample_data.csv", header=None)
		_lowercase			:			Union[str, Any]										=						df.shape[:1][0]
		# If you're using some other dataset input the target column
		_lowercase			:			Any										=						df.iloc[:, 1:2]
		_lowercase			:			Tuple										=						actual_data.values.reshape(len_data, 1)
		_lowercase			:			Optional[Any]										=						MinMaxScaler().fit_transform(actual_data)
		_lowercase			:			Tuple										=						1_0
		_lowercase			:			List[str]										=						5
		_lowercase			:			Union[str, Any]										=						2_0
		_lowercase			:			List[Any]										=						len_data - periods * look_back
		_lowercase			:			Dict										=						actual_data[:division]
		_lowercase			:			Optional[int]										=						actual_data[division - look_back :]
		_lowercase			:			Any										=						[], []
		_lowercase			:			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])
		_lowercase			:			Dict										=						np.array(train_x)
		_lowercase			:			Optional[int]										=						np.array(test_x)
		_lowercase			:			Optional[int]										=						np.array([list(i.ravel()) for i in train_y])
		_lowercase			:			List[Any]										=						np.array([list(i.ravel()) for i in test_y])

		_lowercase			:			Union[str, Any]										=						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")
		_lowercase			:			str										=						model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		_lowercase			:			Any										=						model.predict(x_test)









"""simple docstring"""






from typing import TYPE_CHECKING

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


_lowercase			:			Any										=						{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[Any]										=						[
		    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TrOCRForCausalLM",
		    "TrOCRPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
		from .processing_trocr import TrOCRProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
		import sys

		_lowercase			:			Tuple										=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




