

"""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,
)
_SCREAMING_SNAKE_CASE					:  str             =      logging.getLogger(__name__)
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   git.Repo(search_parent_directories=_A    )
     SCREAMING_SNAKE_CASE__   =   {
         '''repo_id''': str(_A    ),
         '''repo_sha''': str(repo.head.object.hexsha    ),
         '''repo_branch''': str(repo.active_branch    ),
     }

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





     '''simple docstring'''




     if params.n_gpu <= 0:
          SCREAMING_SNAKE_CASE__   =   0
          SCREAMING_SNAKE_CASE__   =   -1
          SCREAMING_SNAKE_CASE__   =   True
          SCREAMING_SNAKE_CASE__   =   False
          return

     assert torch.cuda.is_available()

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

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

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

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

     # summary
     SCREAMING_SNAKE_CASE__   =   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_       (						_A    ):





     '''simple docstring'''




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





import doctest
from collections import deque

import numpy as np






class 	UpperCAmelCase__		:






  """simple docstring"""


  def __init__(							self						:		List[Any] )		->							None:
       SCREAMING_SNAKE_CASE__   =   [2, 1, 2, -1]
       SCREAMING_SNAKE_CASE__   =   [1, 2, 3, 4]







  def 				lowercase_		(							self						:		Optional[int] )		->							list[float]:
       SCREAMING_SNAKE_CASE__   =   len(self.first_signal )
       SCREAMING_SNAKE_CASE__   =   len(self.second_signal )

       SCREAMING_SNAKE_CASE__   =   max(__lowerCamelCase     ,       __lowerCamelCase )

       # create a zero matrix of max_length x max_length
       SCREAMING_SNAKE_CASE__   =   [[0] * max_length for i in range(__lowerCamelCase )]

       # fills the smaller signal with zeros to make both signals of same length
       if length_first_signal < length_second_signal:
            self.first_signal += [0] * (max_length - length_first_signal)
       elif length_first_signal > length_second_signal:
            self.second_signal += [0] * (max_length - length_second_signal)
       for i in range(__lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   deque(self.second_signal )
            rotated_signal.rotate(__lowerCamelCase )
            for j, item in enumerate(__lowerCamelCase ):
                 matrix[i][j] += item

        # multiply the matrix with the first signal
       SCREAMING_SNAKE_CASE__   =   np.matmul(np.transpose(__lowerCamelCase )     ,       np.transpose(self.first_signal ) )

       # rounding-off to two decimal places
       return [round(__lowerCamelCase     ,       2 ) for i in final_signal]


if __name__ == "__main__":
       doctest.testmod()


