


from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
		import tensorflow as tf


lowerCAmelCase__  :str             =							logging.get_logger(__name__)

@dataclass
class 		__a				(    UpperCAmelCase      ):
		_a     :  Optional[Any]           =     [
		    'no_inference',
		    'no_cuda',
		    'no_tpu',
		    'no_speed',
		    'no_memory',
		    'no_env_print',
		    'no_multi_process',
		]
		def __init__(      self      ,   **_SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									for deprecated_arg in self.deprecated_args:
																if deprecated_arg in kwargs:
																							_UpperCAmelCase					=      deprecated_arg[3:]
																							_UpperCAmelCase					=      not kwargs.pop(_SCREAMING_SNAKE_CASE						)
																							logger.warning(
																							    f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
																							    f''' {positive_arg}={kwargs[positive_arg]}'''						)
									_UpperCAmelCase					=      kwargs.pop('tpu_name'      ,   self.tpu_name						)
									_UpperCAmelCase					=      kwargs.pop('device_idx'      ,   self.device_idx						)
									_UpperCAmelCase					=      kwargs.pop('eager_mode'      ,   self.eager_mode						)
									_UpperCAmelCase					=      kwargs.pop('use_xla'      ,   self.use_xla						)
									super().__init__(**_SCREAMING_SNAKE_CASE						)

		_a     :  str           =     field(
		    default=UpperCAmelCase   ,				metadata={'help': 'Name of TPU'}   ,				)
		_a     :  int           =     field(
		    default=0   ,				metadata={'help': 'CPU / GPU device index. Defaults to 0.'}   ,				)
		_a     :  bool           =     field(default=UpperCAmelCase   ,				metadata={'help': 'Benchmark models in eager model.'}      )
		_a     :  bool           =     field(
		    default=UpperCAmelCase   ,				metadata={
		        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
		    }   ,				)
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									_UpperCAmelCase					=      None
									if self.tpu:
																try:
																							if self.tpu_name:
																														_UpperCAmelCase					=      tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
																							else:
																														_UpperCAmelCase					=      tf.distribute.cluster_resolver.TPUClusterResolver()
																except ValueError:
																							_UpperCAmelCase					=      None
									return tpu
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									if self.is_tpu:
																tf.config.experimental_connect_to_cluster(self._setup_tpu						)
																tf.tpu.experimental.initialize_tpu_system(self._setup_tpu						)

																_UpperCAmelCase					=      tf.distribute.TPUStrategy(self._setup_tpu						)
									else:
																# currently no multi gpu is allowed
																if self.is_gpu:
																							# TODO: Currently only single GPU is supported
																							tf.config.set_visible_devices(self.gpu_list[self.device_idx]      ,   'GPU'						)
																							_UpperCAmelCase					=      tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''						)
																else:
																							tf.config.set_visible_devices([]      ,   'GPU'						)  # disable GPU
																							_UpperCAmelCase					=      tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''						)

									return strategy
		@property
		def      UpperCAmelCase__    (      self						)			->	bool:




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									return self._setup_tpu is not None
		@property
		def      UpperCAmelCase__    (      self						)			->	"tf.distribute.Strategy":




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									return self._setup_strategy
		@property
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									return tf.config.list_physical_devices('GPU'						)
		@property
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									requires_backends(self      ,   ['tf']						)
									if self.cuda:
																return len(self.gpu_list						)
									return 0






		@property
		def      UpperCAmelCase__    (      self						)			->	bool:




									"""simple docstring"""





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

		lowerCAmelCase__  :Optional[int]             =							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''')
		lowerCAmelCase__  :List[Any]             =							model.fit(
		    x_train, y_train, epochs=1_5_0, verbose=1, shuffle=True, batch_size=4
		)
		lowerCAmelCase__  :Optional[Any]             =							model.predict(x_test)
