





'''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     :Union[str, Any]       =				logging.getLogger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       Tuple:


     '''simple docstring'''



     _UpperCAmelCase     							=   git.Repo(search_parent_directories=__lowercase       )
     _UpperCAmelCase     							=   {
         "repo_id": str(__lowercase       ),
         "repo_sha": str(repo.head.object.hexsha       ),
         "repo_branch": str(repo.active_branch       ),
     }

     with open(os.path.join(__lowercase			,     "git_log.json"       )			,     "w"       ) as f:
          json.dump(__lowercase			,     __lowercase			,     indent=4       )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple       )		->       Tuple:


     '''simple docstring'''



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

     assert torch.cuda.is_available()

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

          _UpperCAmelCase     							=   int(os.environ["WORLD_SIZE"]       )
          _UpperCAmelCase     							=   int(os.environ["N_GPU_NODE"]       )
          _UpperCAmelCase     							=   int(os.environ["RANK"]       )

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

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

     # summary
     _UpperCAmelCase     							=   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_							(					__lowercase							:      Union[str, Any]       )		->       str:


     '''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 numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class    lowerCAmelCase__     :






		def __init__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							Dict:
						if dst_width < 0 or dst_height < 0:
										raise ValueError('''Destination width/height should be > 0'''  )

						__lowerCamelCase    					=	img
						__lowerCamelCase    					=	img.shape[1]
						__lowerCamelCase    					=	img.shape[0]
						__lowerCamelCase    					=	dst_width
						__lowerCamelCase    					=	dst_height

						__lowerCamelCase    					=	self.src_w / self.dst_w
						__lowerCamelCase    					=	self.src_h / self.dst_h

						__lowerCamelCase    					=	__lowerCamelCase    					=	(
						    np.ones((self.dst_h, self.dst_w, 3)							,			np.uinta  ) * 2_55
						)






		def  __A       (		self		:							List[Any]  )     ->							str:
						for i in range(self.dst_h  ):
										for j in range(self.dst_w  ):
														__lowerCamelCase    					=	self.img[self.get_y(SCREAMING_SNAKE_CASE__  )][self.get_x(SCREAMING_SNAKE_CASE__  )]






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_x * x  )







		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							int:
						return int(self.ratio_y * y  )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__   :			List[str]												=    800, 600
					SCREAMING_SNAKE_CASE__   :			int												=    imread("image_data/lena.jpg", 1)
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    NearestNeighbour(im, dst_w, dst_h)
					n.process()

					imshow(
					    F'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
					)
					waitKey(0)
					destroyAllWindows()



