





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,
)
lowercase_					     =	logging.getLogger(__name__)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  lowercase__			    =						git.Repo(search_parent_directories=_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						{
      'repo_id': str(_SCREAMING_SNAKE_CASE	),
      'repo_sha': str(repo.head.object.hexsha	),
      'repo_branch': str(repo.active_branch	),
  }

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




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Union[str, Any]:
  if params.n_gpu <= 0:
    lowercase__			    =						0
    lowercase__			    =						-1
    lowercase__			    =						True
    lowercase__			    =						False
    return

  assert torch.cuda.is_available()

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

    lowercase__			    =						int(os.environ['WORLD_SIZE']	)
    lowercase__			    =						int(os.environ['N_GPU_NODE']	)
    lowercase__			    =						int(os.environ['RANK']	)

    # number of nodes / node ID
    lowercase__			    =						params.world_size // params.n_gpu_per_node
    lowercase__			    =						params.global_rank // params.n_gpu_per_node
    lowercase__			    =						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

    lowercase__			    =						1
    lowercase__			    =						0
    lowercase__			    =						0
    lowercase__			    =						0
    lowercase__			    =						1
    lowercase__			    =						1
    lowercase__			    =						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
  lowercase__			    =						params.node_id == 0 and params.local_rank == 0
  lowercase__			    =						params.n_nodes > 1

  # summary
  lowercase__			    =						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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  np.random.seed(args.seed	)
  torch.manual_seed(args.seed	)
  if args.n_gpu > 0:
    torch.cuda.manual_seed_all(args.seed	)










from __future__ import annotations

import math
from collections.abc import Callable




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 100	,		)       ->     float:
  lowercase__			    =						x_start
  lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    lowercase__			    =						(x_end - x_start) / steps + xa
    lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa	,		fxa - fxa	)

    # Increment step
    lowercase__			    =						xa
    lowercase__			    =						fxa

  return length


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return math.sin(10 * x	)

      print("""f(x) = sin(10 * x)""")
      print("""The length of the curve from x = -10 to x = 10 is:""")
      lowercase_					     =	10
      while i <= 100_000:
            print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
            i *= 10


