






'''simple docstring'''







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


UpperCamelCase    :      List[Any]  			=		logging.get_logger(__name__)




@dataclass
class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					[
		    'no_inference',
		    'no_cuda',
		    'no_tpu',
		    'no_speed',
		    'no_memory',
		    'no_env_print',
		    'no_multi_process',
		]






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

		_UpperCamelCase				  =					field(
		    default=a   ,metadata={'help': 'Name of TPU'}   ,)
		_UpperCamelCase				  =					field(
		    default=0   ,metadata={'help': 'CPU / GPU device index. Defaults to 0.'}   ,)
		_UpperCamelCase				  =					field(default=a   ,metadata={'help': 'Benchmark models in eager model.'}			)
		_UpperCamelCase				  =					field(
		    default=a   ,metadata={
		        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
		    }   ,)






		@cached_property
		def 			UpperCamelCase_					(      self       ):
								requires_backends(self   ,["""tf"""]       )
								lowerCamelCase__         =  None
								if self.tpu:
														try:
																				if self.tpu_name:
																										lowerCamelCase__         =  tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
																				else:
																										lowerCamelCase__         =  tf.distribute.cluster_resolver.TPUClusterResolver()
														except ValueError:
																				lowerCamelCase__         =  None
								return tpu






		@cached_property
		def 			UpperCamelCase_					(      self       ):
								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       )

														lowerCamelCase__         =  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"""       )
																				lowerCamelCase__         =  tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
														else:
																				tf.config.set_visible_devices([]   ,"""GPU"""       )  # disable GPU
																				lowerCamelCase__         =  tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

								return strategy






		@property
		def 			UpperCamelCase_					(      self       ):
								requires_backends(self   ,["""tf"""]       )
								return self._setup_tpu is not None






		@property
		def 			UpperCamelCase_					(      self       ):
								requires_backends(self   ,["""tf"""]       )
								return self._setup_strategy






		@property
		def 			UpperCamelCase_					(      self       ):
								requires_backends(self   ,["""tf"""]       )
								return tf.config.list_physical_devices("""GPU"""       )






		@property
		def 			UpperCamelCase_					(      self       ):
								requires_backends(self   ,["""tf"""]       )
								if self.cuda:
														return len(self.gpu_list       )
								return 0






		@property
		def 			UpperCamelCase_					(      self       ):
								return self.n_gpu > 0









'''simple docstring'''







import numpy

# List of input, output pairs
UpperCamelCase    :      List[Any]  			=		(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCamelCase    :      Optional[int]  			=		(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
UpperCamelCase    :      int  			=		[2, 4, 1, 5]
UpperCamelCase    :      int  			=		len(train_data)
UpperCamelCase    :      Dict  			=		0.009
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			str="train"						):
						return calculate_hypothesis_value(__lowerCAmelCase   ,    __lowerCAmelCase						) - output(
						    __lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  0
						for i in range(len(__lowerCAmelCase						) - 1						):
												hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
						hyp_val += parameter_vector[0]
						return hyp_val
def 						A__			(				__lowerCAmelCase		:			Optional[Any]   ,    __lowerCAmelCase		:			Tuple						):
						if data_set == "train":
												return train_data[example_no][1]
						elif data_set == "test":
												return test_data[example_no][1]
						return None
def 						A__			(				__lowerCAmelCase		:			int   ,    __lowerCAmelCase		:			Dict						):
						if data_set == "train":
												return _hypothesis_value(train_data[example_no][0]						)
						elif data_set == "test":
												return _hypothesis_value(test_data[example_no][0]						)
						return None
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Optional[Any]=m						):
						lowerCamelCase__         =  0
						for i in range(__lowerCAmelCase						):
												if index == -1:
																		summation_value += _error(__lowerCAmelCase						)
												else:
																		summation_value += _error(__lowerCAmelCase						) * train_data[i][0][index]
						return summation_value
def 						A__			(				__lowerCAmelCase		:			List[Any]						):
						lowerCamelCase__         =  summation_of_cost_derivative(__lowerCAmelCase   ,    __lowerCAmelCase						) / m
						return cost_derivative_value
def 						A__			(				):
						global parameter_vector
						# Tune these values to set a tolerance value for predicted output
						lowerCamelCase__         =  0.00_0002
						lowerCamelCase__         =  0
						lowerCamelCase__         =  0
						while True:
												j += 1
												lowerCamelCase__         =  [0, 0, 0, 0]
												for i in range(0   ,    len(__lowerCAmelCase						)						):
																		lowerCamelCase__         =  get_cost_derivative(i - 1						)
																		lowerCamelCase__         =  (
																		    parameter_vector[i] - LEARNING_RATE * cost_derivative
																		)
												if numpy.allclose(
												    __lowerCAmelCase   ,    __lowerCAmelCase   ,    atol=__lowerCAmelCase   ,    rtol=__lowerCAmelCase   ,    ):
																		break
												lowerCamelCase__         =  temp_parameter_vector
						print(("""Number of iterations:""", j)						)
def 						A__			(				):
						for i in range(len(__lowerCAmelCase						)						):
												print(("""Actual output value:""", output(__lowerCAmelCase   ,    """test"""						))						)
												print(("""Hypothesis output:""", calculate_hypothesis_value(__lowerCAmelCase   ,    """test"""						))						)


if __name__ == "__main__":
							run_gradient_descent()
							print('\nTesting gradient descent for a linear hypothesis function.\n')
							test_gradient_descent()

