






import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf
   from tensorflow.python.eager import context
   from tensorflow.python.framework import ops

   from transformers import GradientAccumulator, create_optimizer



@require_tf
class 				_A			(     unittest.TestCase				):
    """simple docstring"""

    def _a							(				self      :     Union[str, Any] ,     __SCREAMING_SNAKE_CASE      :     Any ,     __SCREAMING_SNAKE_CASE      :     Optional[int] ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]      )      ->							List[Any]:
           self.assertEqual(len(__SCREAMING_SNAKE_CASE      ) ,     len(__SCREAMING_SNAKE_CASE      )      )
           for a, b in zip(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ):
                  self.assertAlmostEqual(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE ,     delta=__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Dict      )      ->							str:
           __UpperCAmelCase     				=GradientAccumulator()
           accumulator([tf.constant([1.0, 2.0]      )]      )
           accumulator([tf.constant([-2.0, 1.0]      )]      )
           accumulator([tf.constant([-1.0, 2.0]      )]      )
           with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
                  accumulator([tf.constant([1.0, 1.0]      ), tf.constant([2.0, 2.0]      )]      )
           self.assertEqual(accumulator.step ,     3      )
           self.assertEqual(len(accumulator.gradients      ) ,     1      )
           self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,     [-2.0, 5.0] ,     tol=1e-2      )
           accumulator.reset()
           self.assertEqual(accumulator.step ,     0      )
           self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() ,     [0.0, 0.0] ,     tol=1e-2      )



    def _a							(				self      :     List[str]      )      ->							Optional[Any]:
           __UpperCAmelCase     				=None
           ops.enable_eager_execution_internal()
           __UpperCAmelCase     				=tf.config.list_physical_devices("""CPU"""      )
           if len(__SCREAMING_SNAKE_CASE      ) == 1:
                  tf.config.set_logical_device_configuration(
                      physical_devices[0] ,     [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]      )
           __UpperCAmelCase     				=tf.config.list_logical_devices(device_type="""CPU"""      )
           __UpperCAmelCase     				=tf.distribute.MirroredStrategy(devices=devices[:2]      )

           with strategy.scope():
                  __UpperCAmelCase     				=GradientAccumulator()
                  __UpperCAmelCase     				=tf.Variable([4.0, 3.0]      )
                  __UpperCAmelCase     				=create_optimizer(5e-5 ,     10 ,     5      )
                  __UpperCAmelCase     				=tf.Variable([0.0, 0.0] ,     trainable=__SCREAMING_SNAKE_CASE      )

           def accumulate_on_replica(__SCREAMING_SNAKE_CASE      :     int      ):
                  accumulator([gradient]      )

           def apply_on_replica():
                  optimizer.apply_gradients(list(zip(accumulator.gradients ,     [variable]      )      )      )

           @tf.function
           def accumulate(__SCREAMING_SNAKE_CASE      :     Tuple ,     __SCREAMING_SNAKE_CASE      :     Tuple      ):
                  with strategy.scope():
                         __UpperCAmelCase     				=strategy.experimental_local_results(__SCREAMING_SNAKE_CASE      )
                         local_variables[0].assign(__SCREAMING_SNAKE_CASE      )
                         local_variables[1].assign(__SCREAMING_SNAKE_CASE      )
                         strategy.run(__SCREAMING_SNAKE_CASE ,     args=(gradient_placeholder,)      )

           @tf.function
           def apply_grad():
                  with strategy.scope():
                         strategy.run(__SCREAMING_SNAKE_CASE      )

           def _check_local_values(__SCREAMING_SNAKE_CASE      :     Dict ,     __SCREAMING_SNAKE_CASE      :     Dict      ):
                  __UpperCAmelCase     				=strategy.experimental_local_results(accumulator._gradients[0]      )
                  self.assertListAlmostEqual(values[0].value() ,     __SCREAMING_SNAKE_CASE ,     tol=1e-2      )
                  self.assertListAlmostEqual(values[1].value() ,     __SCREAMING_SNAKE_CASE ,     tol=1e-2      )

           accumulate([1.0, 2.0] ,     [-1.0, 1.0]      )
           accumulate([3.0, -1.0] ,     [-1.0, -1.0]      )
           accumulate([-2.0, 2.0] ,     [3.0, -2.0]      )
           self.assertEqual(accumulator.step ,     3      )
           _check_local_values([2.0, 3.0] ,     [1.0, -2.0]      )
           apply_grad()
           self.assertListAlmostEqual(variable.value() ,     [4.0, 3.0] ,     tol=1e-2      )
           accumulator.reset()
           self.assertEqual(accumulator.step ,     0      )
           _check_local_values([0.0, 0.0] ,     [0.0, 0.0]      )










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__		     =     pd.read_csv('''sample_data.csv''', header=None)
	a__		     =     df.shape[:1][0]
	# If you're using some other dataset input the target column
	a__		     =     df.iloc[:, 1:2]
	a__		     =     actual_data.values.reshape(len_data, 1)
	a__		     =     MinMaxScaler().fit_transform(actual_data)
	a__		     =     10
	a__		     =     5
	a__		     =     20
	a__		     =     len_data - periods * look_back
	a__		     =     actual_data[:division]
	a__		     =     actual_data[division - look_back :]
	a__     ,							a__		     =     [], []
	a__     ,							a__		     =     [], []

	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__		     =     np.array(train_x)
	a__		     =     np.array(test_x)
	a__		     =     np.array([list(i.ravel()) for i in train_y])
	a__		     =     np.array([list(i.ravel()) for i in test_y])

	a__		     =     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__		     =     model.fit(
	    x_train, y_train, epochs=150, verbose=1, shuffle=True, batch_size=4
	)
	a__		     =     model.predict(x_test)






