

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 					__UpperCAmelCase     (       unittest.TestCase						):



   def        __magic_name__    (							self							:     Tuple						):
     UpperCAmelCase       :							Dict  = inspect.getfile(accelerate.test_utils						)
     UpperCAmelCase       :							Optional[int]  = os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_script.py''']						)
     UpperCAmelCase       :							List[str]  = os.path.sep.join(
         mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_distributed_data_loop.py''']						)
     UpperCAmelCase       :							Optional[Any]  = os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_ops.py''']						)



   @require_multi_gpu
   def        __magic_name__    (							self							:     List[Any]						):
     print(F'''Found {torch.cuda.device_count()} devices.'''						)
     UpperCAmelCase       :							Tuple  = ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
     with patch_environment(omp_num_threads=1						):
       execute_subprocess_async(__a,		env=os.environ.copy()						)



   @require_multi_gpu
   def        __magic_name__    (							self							:     Tuple						):
     print(F'''Found {torch.cuda.device_count()} devices.'''						)
     UpperCAmelCase       :							Dict  = ['''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(__a,		env=os.environ.copy()						)



   @require_multi_gpu
   def        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							Tuple  = ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
     with patch_environment(omp_num_threads=1						):
       execute_subprocess_async(__a,		env=os.environ.copy()						)





   @require_multi_gpu
   def        __magic_name__    (							self							:     Dict						):
     print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''						)
     UpperCAmelCase       :							Tuple  = ['''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(__a,		env=os.environ.copy()						)


if __name__ == "__main__":
     _lowerCamelCase				:   str					   =							Accelerator()
     _lowerCamelCase				:   Optional[Any]					   =							(accelerator.state.process_index + 2, 1_0)
     _lowerCamelCase				:   str					   =							torch.randint(0, 1_0, shape).to(accelerator.device)

     _lowerCamelCase				:   Union[str, Any]					   =							""

     _lowerCamelCase				:   List[str]					   =							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)."

     _lowerCamelCase				:   str					   =							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."
     _lowerCamelCase				:   str					   =							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"""





# Logistic Regression from scratch

# In[62]:

# In[63]:

# importing all the required libraries

import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets



def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


					"""simple docstring"""





					return 1 / (1 + np.exp(-z	))



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""simple docstring"""





					return (-y * np.log(__snake_case	) - (1 - y) * np.log(1 - h	)).mean()



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case	)							->		int:


					"""simple docstring"""





					_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
					return np.sum(y * scores - np.log(1 + np.exp(__snake_case	)	)	)



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=7_00_00	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  np.zeros(x.shape[1]	)

					for iterations in range(__snake_case	):
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  np.dot(x.T,     h - y	) / y.size
										_UpperCamelCase							 =  theta - alpha * gradient  # updating the weights
										_UpperCamelCase							 =  np.dot(__snake_case,     __snake_case	)
										_UpperCamelCase							 =  sigmoid_function(__snake_case	)
										_UpperCamelCase							 =  cost_function(__snake_case,     __snake_case	)
										if iterations % 1_00 == 0:
															print(F'''loss: {j} \t'''	)  # printing the loss after every 100 iterations
					return theta


# In[68]:

if __name__ == "__main__":
				_a														=		datasets.load_iris()
				_a														=		iris.data[:, :2]
				_a														=		(iris.target != 0) * 1

				_a														=		0.1
				_a														=		logistic_reg(alpha, x, y, max_iterations=7_0000)
				print("""theta: """, theta)  # printing the theta i.e our weights vector



				def       lowerCamelCase__		(			__snake_case	)							->		Tuple:


									"""simple docstring"""





									return sigmoid_function(
									    np.dot(__snake_case,     __snake_case	)	)  # predicting the value of probability from the logistic regression algorithm

				plt.figure(figsize=(10, 6))
				plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="""b""", label="""0""")
				plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="""r""", label="""1""")
				((_a)							,							(_a))														=		(x[:, 0].min(), x[:, 0].max())
				((_a)							,							(_a))														=		(x[:, 1].min(), x[:, 1].max())
				((_a)							,							(_a))														=		np.meshgrid(np.linspace(xa_min, xa_max), np.linspace(xa_min, xa_max))
				_a														=		np.c_[xxa.ravel(), xxa.ravel()]
				_a														=		predict_prob(grid).reshape(xxa.shape)
				plt.contour(xxa, xxa, probs, [0.5], linewidths=1, colors="""black""")

				plt.legend()
				plt.show()






