


'''simple docstring'''







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __a						(					self     :							Any						):





       '''simple docstring'''

       __a       =   inspect.getfile(accelerate.test_utils						)
       __a       =   os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_script.py"""]						)
       __a       =   os.path.sep.join(
           mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_distributed_data_loop.py"""]						)
       __a       =   os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_ops.py"""]						)

  @require_multi_gpu
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.'''						)
       __a       =   ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
       with patch_environment(omp_num_threads=1						):
            execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=os.environ.copy()						)

  @require_multi_gpu
  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices.'''						)
       __a       =   ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
       print(f'''Command: {cmd}'''						)
       with patch_environment(omp_num_threads=1						):
            execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=os.environ.copy()						)

  @require_multi_gpu
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
       with patch_environment(omp_num_threads=1						):
            execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=os.environ.copy()						)


  @require_multi_gpu
  def __a						(					self     :							str						):





       '''simple docstring'''

       print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''						)
       __a       =   ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
       with patch_environment(omp_num_threads=1				,							cuda_visible_devices="""0,1"""						):
            execute_subprocess_async(SCREAMING_SNAKE_CASE__				,							env=os.environ.copy()						)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_      =			Accelerator()
 SCREAMING_SNAKE_CASE_      =			(accelerator.state.process_index + 2, 10)
 SCREAMING_SNAKE_CASE_      =			torch.randint(0, 10, shape).to(accelerator.device)

 SCREAMING_SNAKE_CASE_      =			''

 SCREAMING_SNAKE_CASE_      =			accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 SCREAMING_SNAKE_CASE_      =			accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 SCREAMING_SNAKE_CASE_      =			accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)










'''simple docstring'''







import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_      =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_      =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_      =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_      =			len(train_data)
SCREAMING_SNAKE_CASE_      =			0.0_0_9




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE="train"		)				->					Optional[int]:
     """simple docstring"""




     return calculate_hypothesis_value(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) - output(
         __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					int:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Optional[int]:
     """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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		)				->					Tuple:
     """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        __lowercase						(      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE=m		)				->					Optional[Any]:
     """simple docstring"""




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




def        __lowercase						(      __SCREAMING_SNAKE_CASE		)				->					List[str]:
     """simple docstring"""




     __a       =   summation_of_cost_derivative(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE		) / m
     return cost_derivative_value




def        __lowercase						(      )				->					str:
     """simple docstring"""




     global parameter_vector
     # Tune these values to set a tolerance value for predicted output
     __a       =   0.000_002
     __a       =   0
     __a       =   0
     while True:
          j += 1
          __a       =   [0, 0, 0, 0]
          for i in range(0				,  len(__SCREAMING_SNAKE_CASE		)		):
               __a       =   get_cost_derivative(i - 1		)
               __a       =   (
                   parameter_vector[i] - LEARNING_RATE * cost_derivative
               )
          if numpy.allclose(
              __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  atol=__SCREAMING_SNAKE_CASE				,  rtol=__SCREAMING_SNAKE_CASE				,  ):
               break
          __a       =   temp_parameter_vector
     print(("""Number of iterations:""", j)		)




def        __lowercase						(      )				->					List[Any]:
     """simple docstring"""




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


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






