


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,
)
__UpperCamelCase				:  Any      					=							logging.getLogger(__name__)
def      snake_case_						( __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   git.Repo(search_parent_directories=_lowerCamelCase       )
   UpperCAmelCase_							:      Dict						=   {
       "repo_id": str(_lowerCamelCase       ),
       "repo_sha": str(repo.head.object.hexsha       ),
       "repo_branch": str(repo.active_branch       ),
   }

   with open(os.path.join(_lowerCamelCase			,       '''git_log.json'''       )			,       '''w'''       ) as f:
      json.dump(_lowerCamelCase			,       _lowerCamelCase			,       indent=4       )
def      snake_case_						( __lowercase       ):
   if params.n_gpu <= 0:
      UpperCAmelCase_							:      List[str]						=   0
      UpperCAmelCase_							:      Tuple						=   -1
      UpperCAmelCase_							:      int						=   True
      UpperCAmelCase_							:      Tuple						=   False
      return

   assert torch.cuda.is_available()

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

      UpperCAmelCase_							:      Tuple						=   int(os.environ['''WORLD_SIZE''']       )
      UpperCAmelCase_							:      Dict						=   int(os.environ['''N_GPU_NODE''']       )
      UpperCAmelCase_							:      Optional[Any]						=   int(os.environ['''RANK''']       )

      # number of nodes / node ID
      UpperCAmelCase_							:      Optional[Any]						=   params.world_size // params.n_gpu_per_node
      UpperCAmelCase_							:      Optional[int]						=   params.global_rank // params.n_gpu_per_node
      UpperCAmelCase_							:      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

      UpperCAmelCase_							:      Dict						=   1
      UpperCAmelCase_							:      Union[str, Any]						=   0
      UpperCAmelCase_							:      Any						=   0
      UpperCAmelCase_							:      List[str]						=   0
      UpperCAmelCase_							:      Tuple						=   1
      UpperCAmelCase_							:      Any						=   1
      UpperCAmelCase_							:      str						=   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_							:      Any						=   params.node_id == 0 and params.local_rank == 0
   UpperCAmelCase_							:      Union[str, Any]						=   params.n_nodes > 1

   # summary
   UpperCAmelCase_							:      Optional[int]						=   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      snake_case_						( __lowercase       ):
   np.random.seed(args.seed       )
   torch.manual_seed(args.seed       )
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed       )



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase				:  int      					=							logging.get_logger(__name__)

__UpperCamelCase				:  Union[str, Any]      					=							{
    'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/config.json',
}





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= 'timesformer'





    def __init__(	self				:   int       ,   __snake_case				:   Any=224       ,   __snake_case				:   str=16       ,   __snake_case				:   Any=3       ,   __snake_case				:   List[Any]=8       ,   __snake_case				:   Dict=768       ,   __snake_case				:   Dict=12       ,   __snake_case				:   Tuple=12       ,   __snake_case				:   Dict=3_072       ,   __snake_case				:   str="gelu"       ,   __snake_case				:   Union[str, Any]=0.0       ,   __snake_case				:   List[Any]=0.0       ,   __snake_case				:   Tuple=0.02       ,   __snake_case				:   Optional[Any]=1E-6       ,   __snake_case				:   List[Any]=True       ,   __snake_case				:   List[str]="divided_space_time"       ,   __snake_case				:   Optional[int]=0       ,   **__snake_case				:   Dict       ,   ):





       '''simple docstring'''
       super().__init__(**__snake_case	)

       UpperCAmelCase_							:      Optional[int]						=   image_size
       UpperCAmelCase_							:      Optional[Any]						=   patch_size
       UpperCAmelCase_							:      Any						=   num_channels
       UpperCAmelCase_							:      int						=   num_frames

       UpperCAmelCase_							:      List[Any]						=   hidden_size
       UpperCAmelCase_							:      Any						=   num_hidden_layers
       UpperCAmelCase_							:      Dict						=   num_attention_heads
       UpperCAmelCase_							:      List[Any]						=   intermediate_size
       UpperCAmelCase_							:      str						=   hidden_act
       UpperCAmelCase_							:      List[str]						=   hidden_dropout_prob
       UpperCAmelCase_							:      List[str]						=   attention_probs_dropout_prob
       UpperCAmelCase_							:      Optional[int]						=   initializer_range
       UpperCAmelCase_							:      List[str]						=   layer_norm_eps
       UpperCAmelCase_							:      str						=   qkv_bias

       UpperCAmelCase_							:      Dict						=   attention_type
       UpperCAmelCase_							:      str						=   drop_path_rate