
'''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,
)
lowercase     :							Union[str, Any]       						=     logging.getLogger(__name__)







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



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

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







def 						lowerCAmelCase_							(  snake_case__		):




    '''simple docstring'''



    if params.n_gpu <= 0:
        A			:  Optional[int]   =		0
        A			:  List[str]   =		-1
        A			:  Union[str, Any]   =		True
        A			:  int   =		False
        return

    assert torch.cuda.is_available()

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

        A			:  Tuple   =		int(os.environ['''WORLD_SIZE''']		)
        A			:  str   =		int(os.environ['''N_GPU_NODE''']		)
        A			:  Any   =		int(os.environ['''RANK''']		)

        # number of nodes / node ID
        A			:  Union[str, Any]   =		params.world_size // params.n_gpu_per_node
        A			:  Any   =		params.global_rank // params.n_gpu_per_node
        A			:  List[Any]   =		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

        A			:  List[Any]   =		1
        A			:  int   =		0
        A			:  Any   =		0
        A			:  Optional[Any]   =		0
        A			:  Tuple   =		1
        A			:  Any   =		1
        A			:  List[Any]   =		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
    A			:  int   =		params.node_id == 0 and params.local_rank == 0
    A			:  List[str]   =		params.n_nodes > 1

    # summary
    A			:  List[Any]   =		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 						lowerCAmelCase_							(  snake_case__		):




    '''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'''




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase     :							Optional[Any]       						=     logging.get_logger(__name__)

lowercase     :							int       						=     {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}





class 					A							(				__snake_case		):
     __magic_name__          =						'''deta'''
     __magic_name__          =						{
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''encoder_attention_heads''',
     }





     def __init__(				self		,     SCREAMING_SNAKE_CASE=None		,     SCREAMING_SNAKE_CASE=900		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=2048		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=6		,     SCREAMING_SNAKE_CASE=1024		,     SCREAMING_SNAKE_CASE=8		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE="relu"		,     SCREAMING_SNAKE_CASE=256		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.0		,     SCREAMING_SNAKE_CASE=0.02		,     SCREAMING_SNAKE_CASE=1.0		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=False		,     SCREAMING_SNAKE_CASE="sine"		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=4		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=300		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=True		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=1		,     SCREAMING_SNAKE_CASE=5		,     SCREAMING_SNAKE_CASE=2		,     SCREAMING_SNAKE_CASE=0.1		,     SCREAMING_SNAKE_CASE=0.25		,     **SCREAMING_SNAKE_CASE		,     )					->							Any:






         """simple docstring"""






         if backbone_config is None:
             logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''      )
             A			:  Optional[Any]   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']      )
         else:
             if isinstance(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      ):
                 A			:  List[Any]   =		backbone_config.pop('''model_type'''      )
                 A			:  List[Any]   =		CONFIG_MAPPING[backbone_model_type]
                 A			:  Optional[int]   =		config_class.from_dict(SCREAMING_SNAKE_CASE      )

         A			:  str   =		backbone_config
         A			:  Optional[int]   =		num_queries
         A			:  Dict   =		max_position_embeddings
         A			:  Optional[Any]   =		d_model
         A			:  Optional[Any]   =		encoder_ffn_dim
         A			:  List[str]   =		encoder_layers
         A			:  Tuple   =		encoder_attention_heads
         A			:  Optional[Any]   =		decoder_ffn_dim
         A			:  Optional[int]   =		decoder_layers
         A			:  List[str]   =		decoder_attention_heads
         A			:  Union[str, Any]   =		dropout
         A			:  str   =		attention_dropout
         A			:  Any   =		activation_dropout
         A			:  Optional[int]   =		activation_function
         A			:  Tuple   =		init_std
         A			:  Any   =		init_xavier_std
         A			:  Optional[Any]   =		encoder_layerdrop
         A			:  int   =		auxiliary_loss
         A			:  Dict   =		position_embedding_type
         # deformable attributes
         A			:  str   =		num_feature_levels
         A			:  Optional[int]   =		encoder_n_points
         A			:  Any   =		decoder_n_points
         A			:  Tuple   =		two_stage
         A			:  Dict   =		two_stage_num_proposals
         A			:  List[str]   =		with_box_refine
         A			:  List[str]   =		assign_first_stage
         if two_stage is True and with_box_refine is False:
             raise ValueError('''If two_stage is True, with_box_refine must be True.'''      )
         # Hungarian matcher
         A			:  Dict   =		class_cost
         A			:  Optional[int]   =		bbox_cost
         A			:  Optional[Any]   =		giou_cost
         # Loss coefficients
         A			:  int   =		mask_loss_coefficient
         A			:  int   =		dice_loss_coefficient
         A			:  Tuple   =		bbox_loss_coefficient
         A			:  int   =		giou_loss_coefficient
         A			:  Dict   =		eos_coefficient
         A			:  Optional[Any]   =		focal_alpha
         super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE		,     **SCREAMING_SNAKE_CASE      )





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.encoder_attention_heads





     @property
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         return self.d_model





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A			:  Any   =		copy.deepcopy(self.__dict__      )
         A			:  Dict   =		self.backbone_config.to_dict()
         A			:  List[Any]   =		self.__class__.model_type
         return output




