


"""simple docstring"""


import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=()    , _UpperCamelCase=None    , _UpperCamelCase="no"    , _UpperCamelCase="29500"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			False
			__lowerCAmelCase				    =			False
			if any(key.startswith("KAGGLE"      ) for key in os.environ.keys()      ):
						__lowerCAmelCase				    =			True
			elif "IPython" in sys.modules:
						__lowerCAmelCase				    =			"google.colab" in str(sys.modules["IPython"].get_ipython()      )

			try:
						__lowerCAmelCase				    =			PrecisionType(mixed_precision.lower()      )
			except ValueError:
						raise ValueError(
						    f"Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}."      )

			if (in_colab or in_kaggle) and (os.environ.get("TPU_NAME"    , _UpperCamelCase      ) is not None):
						# TPU launch
						import torch_xla.distributed.xla_multiprocessing as xmp

						if len(AcceleratorState._shared_state      ) > 0:
									raise ValueError(
									    "To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside "
									    "your training function. Restart your notebook and make sure no cells initializes an "
									    "`Accelerator`."      )
						if num_processes is None:
									__lowerCAmelCase				    =			8

						__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , distributed_type="TPU"      )
						print(f"Launching a training on {num_processes} TPU cores."      )
						xmp.spawn(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )
			elif in_colab:
						# No need for a distributed launch otherwise as it's either CPU or one GPU.
						if torch.cuda.is_available():
									print("Launching training on one GPU."      )
						else:
									print("Launching training on one CPU."      )
						function(*_UpperCamelCase      )
			else:
						if num_processes is None:
									raise ValueError(
									    "You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call."      )

						if num_processes > 1:
									# Multi-GPU launch
									from torch.multiprocessing import start_processes
									from torch.multiprocessing.spawn import ProcessRaisedException

									if len(AcceleratorState._shared_state      ) > 0:
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized "
												    "inside your training function. Restart your notebook and make sure no cells initializes an "
												    "`Accelerator`."      )

									if torch.cuda.is_initialized():
												raise ValueError(
												    "To launch a multi-GPU training from your notebook, you need to avoid running any instruction "
												    "using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA "
												    "function."      )

									# torch.distributed will expect a few environment variable to be here. We set the ones common to each
									# process here (the other ones will be set be the launcher).
									with patch_environment(
									    world_size=_UpperCamelCase    , master_addr="127.0.01"    , master_port=_UpperCamelCase    , mixed_precision=_UpperCamelCase      ):
												__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , distributed_type="MULTI_GPU"      )
												print(f"Launching training on {num_processes} GPUs."      )
												try:
															start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )
												except ProcessRaisedException as e:
															if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																		raise RuntimeError(
																		    "CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. "
																		    "This likely stems from an outside import causing issues once the `notebook_launcher()` is called. "
																		    "Please review your imports and test them when running the `notebook_launcher()` to identify "
																		    "which one is problematic."      ) from e

						else:
									# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
									if is_mps_available():
												__lowerCAmelCase				    =			"1"
												print("Launching training on MPS."      )
									elif torch.cuda.is_available():
												print("Launching training on one GPU."      )
									else:
												print("Launching training on CPU."      )
									function(*_UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=()    , _UpperCamelCase=2      ):

			'''simple docstring'''




			from torch.multiprocessing import start_processes

			with tempfile.NamedTemporaryFile() as tmp_file:
						# torch.distributed will expect a few environment variable to be here. We set the ones common to each
						# process here (the other ones will be set be the launcher).
						with patch_environment(
						    world_size=_UpperCamelCase    , master_addr="127.0.01"    , master_port="29500"    , accelerate_mixed_precision="no"    , accelerate_debug_rdv_file=tmp_file.name    , accelerate_use_cpu="yes"    , ):
									__lowerCAmelCase				    =			PrepareForLaunch(_UpperCamelCase    , debug=_UpperCamelCase      )
									start_processes(_UpperCamelCase    , args=_UpperCamelCase    , nprocs=_UpperCamelCase    , start_method="fork"      )








"""simple docstring"""


import numpy

# List of input, output pairs
A     :   Any			    =			(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
A     :   str			    =			(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A     :   Union[str, Any]			    =			[2, 4, 1, 5]
A     :   int			    =			len(train_data)
A     :   Dict			    =			0.009


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase="train"      ):

			'''simple docstring'''




			return calculate_hypothesis_value(_UpperCamelCase    , _UpperCamelCase      ) - output(
			    _UpperCamelCase    , _UpperCamelCase      )


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(len(_UpperCamelCase      ) - 1      ):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




			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 _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=m      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			0
			for i in range(_UpperCamelCase      ):
						if index == -1:
									summation_value += _error(_UpperCamelCase      )
						else:
									summation_value += _error(_UpperCamelCase      ) * train_data[i][0][index]
			return summation_value


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			summation_of_cost_derivative(_UpperCamelCase    , _UpperCamelCase      ) / m
			return cost_derivative_value


def _lowerCamelCase	(		):

			'''simple docstring'''




			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__lowerCAmelCase				    =			0.00_00_02
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			while True:
						j += 1
						__lowerCAmelCase				    =			[0, 0, 0, 0]
						for i in range(0    , len(_UpperCamelCase      )      ):
									__lowerCAmelCase				    =			get_cost_derivative(i - 1      )
									__lowerCAmelCase				    =			(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    _UpperCamelCase    , _UpperCamelCase    , atol=_UpperCamelCase    , rtol=_UpperCamelCase    , ):
									break
						__lowerCAmelCase				    =			temp_parameter_vector
			print(("Number of iterations:", j)      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			for i in range(len(_UpperCamelCase      )      ):
						print(("Actual output value:", output(_UpperCamelCase    , "test"      ))      )
						print(("Hypothesis output:", calculate_hypothesis_value(_UpperCamelCase    , "test"      ))      )


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




