
'''simple docstring'''


import json
import logging
import os
import socket

import git
import numpy as np
import torch


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
lowerCAmelCase__						:       str										=						logging.getLogger(__name__)




def 	_a       (				__lowerCAmelCase		:      Tuple     ):

		"""simple docstring"""

		snake_case__						:       Dict       						=  git.Repo(search_parent_directories=UpperCamelCase__     )
		snake_case__						:       Any       						=  {
		    '''repo_id''': str(UpperCamelCase__     ),
		    '''repo_sha''': str(repo.head.object.hexsha     ),
		    '''repo_branch''': str(repo.active_branch     ),
		}

		with open(os.path.join(UpperCamelCase__   ,							'''git_log.json'''     )   ,							'''w'''     ) as f:
				json.dump(UpperCamelCase__   ,							UpperCamelCase__   ,							indent=4     )




def 	_a       (				__lowerCAmelCase		:      Dict     ):

		"""simple docstring"""

		if params.n_gpu <= 0:
				snake_case__						:       Any       						=  0
				snake_case__						:       Tuple       						=  -1
				snake_case__						:       Tuple       						=  True
				snake_case__						:       List[str]       						=  False
				return

		assert torch.cuda.is_available()

		logger.info('''Initializing GPUs'''     )
		if params.n_gpu > 1:
				assert params.local_rank != -1

				snake_case__						:       List[str]       						=  int(os.environ['''WORLD_SIZE''']     )
				snake_case__						:       Optional[int]       						=  int(os.environ['''N_GPU_NODE''']     )
				snake_case__						:       Any       						=  int(os.environ['''RANK''']     )

				# number of nodes / node ID
				snake_case__						:       Any       						=  params.world_size // params.n_gpu_per_node
				snake_case__						:       int       						=  params.global_rank // params.n_gpu_per_node
				snake_case__						:       List[str]       						=  True

				assert params.n_nodes == int(os.environ['''N_NODES''']     )
				assert params.node_id == int(os.environ['''NODE_RANK''']     )

		# local job (single GPU)
		else:
				assert params.local_rank == -1

				snake_case__						:       Optional[int]       						=  1
				snake_case__						:       Tuple       						=  0
				snake_case__						:       Optional[int]       						=  0
				snake_case__						:       Optional[Any]       						=  0
				snake_case__						:       List[Any]       						=  1
				snake_case__						:       Optional[Any]       						=  1
				snake_case__						:       Optional[int]       						=  False

		# sanity checks
		assert params.n_nodes >= 1
		assert 0 <= params.node_id < params.n_nodes
		assert 0 <= params.local_rank <= params.global_rank < params.world_size
		assert params.world_size == params.n_nodes * params.n_gpu_per_node

		# define whether this is the master process / if we are in multi-node distributed mode
		snake_case__						:       Tuple       						=  params.node_id == 0 and params.local_rank == 0
		snake_case__						:       List[Any]       						=  params.n_nodes > 1

		# summary
		snake_case__						:       List[str]       						=  F"""--- Global rank: {params.global_rank} - """
		logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes     )
		logger.info(PREFIX + '''Node ID        : %i''' % params.node_id     )
		logger.info(PREFIX + '''Local rank     : %i''' % params.local_rank     )
		logger.info(PREFIX + '''World size     : %i''' % params.world_size     )
		logger.info(PREFIX + '''GPUs per node  : %i''' % params.n_gpu_per_node     )
		logger.info(PREFIX + '''Master         : %s''' % str(params.is_master     )     )
		logger.info(PREFIX + '''Multi-node     : %s''' % str(params.multi_node     )     )
		logger.info(PREFIX + '''Multi-GPU      : %s''' % str(params.multi_gpu     )     )
		logger.info(PREFIX + '''Hostname       : %s''' % socket.gethostname()     )

		# set GPU device
		torch.cuda.set_device(params.local_rank     )

		# initialize multi-GPU
		if params.multi_gpu:
				logger.info('''Initializing PyTorch distributed'''     )
				torch.distributed.init_process_group(
				    init_method='''env://'''   ,							backend='''nccl'''   ,							)





def 	_a       (				__lowerCAmelCase		:      Tuple     ):

		"""simple docstring"""

		np.random.seed(args.seed     )
		torch.manual_seed(args.seed     )
		if args.n_gpu > 0:
				torch.cuda.manual_seed_all(args.seed     )






'''simple docstring'''


from __future__ import annotations

from typing import Any




class     a			:






						"""simple docstring"""




						def __init__(							self					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      int			,			snake_case_					:      float = 0  ):



								'''simple docstring'''



								snake_case__						,       snake_case__						:       Optional[Any]       						=  row, column
								snake_case__						:       Dict       						=  [[default_value for c in range(snake_case_  )] for r in range(snake_case_  )]







						def __str__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

								# Make string identifier
								snake_case__						:       List[str]       						=  0
								for row_vector in self.array:
										for obj in row_vector:
												snake_case__						:       str       						=  max(snake_case_			,			len(str(snake_case_  )  )  )
								snake_case__						:       Dict       						=  F"""%{max_element_length}s"""

								# Make string and return
								def single_line(snake_case_					:      list[float]  ) -> str:
										nonlocal string_format_identifier
										snake_case__						:       str       						=  '''['''
										line += ", ".join(string_format_identifier % (obj,) for obj in row_vector  )
										line += "]"
										return line

								s += "\n".join(single_line(snake_case_  ) for row_vector in self.array  )
								return s







						def __repr__(							self					:      Optional[int]  ):



								'''simple docstring'''



								return str(self  )







						def       __magic_name__							(							self					:      Optional[int]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								if not (isinstance(snake_case_			,			(list, tuple)  ) and len(snake_case_  ) == 2):
										return False
								elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
										return False
								else:
										return True







						def __getitem__(							self					:      List[str]			,			snake_case_					:      tuple[int, int]  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								return self.array[loc[0]][loc[1]]







						def __setitem__(							self					:      int			,			snake_case_					:      tuple[int, int]			,			snake_case_					:      float  ):



								'''simple docstring'''



								assert self.validate_indicies(snake_case_  )
								snake_case__						:       Optional[Any]       						=  value







						def __add__(							self					:      Dict			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  )
								assert self.row == another.row and self.column == another.column

								# Add
								snake_case__						:       str       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       Optional[Any]       						=  self[r, c] + another[r, c]
								return result







						def __neg__(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       int       						=  Matrix(self.row			,			self.column  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       str       						=  -self[r, c]
								return result







						def __sub__(							self					:      Optional[int]			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								return self + (-another)







						def __mul__(							self					:      int			,			snake_case_					:      int | float | Matrix  ):



								'''simple docstring'''



								if isinstance(snake_case_			,			(int, float)  ):  # Scalar multiplication
										snake_case__						:       Dict       						=  Matrix(self.row			,			self.column  )
										for r in range(self.row  ):
												for c in range(self.column  ):
														snake_case__						:       Tuple       						=  self[r, c] * another
										return result
								elif isinstance(snake_case_			,			snake_case_  ):  # Matrix multiplication
										assert self.column == another.row
										snake_case__						:       List[Any]       						=  Matrix(self.row			,			another.column  )
										for r in range(self.row  ):
												for c in range(another.column  ):
														for i in range(self.column  ):
																result[r, c] += self[r, i] * another[i, c]
										return result
								else:
										snake_case__						:       Union[str, Any]       						=  F"""Unsupported type given for another ({type(snake_case_  )})"""
										raise TypeError(snake_case_  )







						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  Matrix(self.column			,			self.row  )
								for r in range(self.row  ):
										for c in range(self.column  ):
												snake_case__						:       List[Any]       						=  self[r, c]
								return result







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      Matrix			,			snake_case_					:      Matrix  ):



								'''simple docstring'''



								assert isinstance(snake_case_			,			snake_case_  ) and isinstance(snake_case_			,			snake_case_  )
								assert self.row == self.column == u.row == v.row  # u, v should be column vector
								assert u.column == v.column == 1  # u, v should be column vector

								# Calculate
								snake_case__						:       Tuple       						=  v.transpose()
								snake_case__						:       Optional[Any]       						=  (v_t * self * u)[0, 0] + 1
								if numerator_factor == 0:
										return None  # It's not invertable
								return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":




					def 	_a       (				):

							"""simple docstring"""

							snake_case__						:       Tuple       						=  Matrix(3   ,							3   ,							0     )
							for i in range(3     ):
									snake_case__						:       Any       						=  1
							print(F"""a^(-1) is {ainv}"""     )
							# u, v
							snake_case__						:       int       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       List[Any]       						=  1, 2, -3
							snake_case__						:       Any       						=  Matrix(3   ,							1   ,							0     )
							snake_case__						,       snake_case__						,       snake_case__						:       Optional[int]       						=  4, -2, 5
							print(F"""u is {u}"""     )
							print(F"""v is {v}"""     )
							print(F"""uv^T is {u * v.transpose()}"""     )
							# Sherman Morrison
							print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(__lowerCAmelCase   ,							__lowerCAmelCase     )}"""     )




					def 	_a       (				):

							"""simple docstring"""

							import doctest

							doctest.testmod()





					testa()



