






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_					:   int 							=						logging.getLogger(__name__)






def 							_lowerCAmelCase (  _a   :					str			)	->			Union[str, Any]:
     lowerCAmelCase_					:       Tuple    =		git.Repo(search_parent_directories=_a			)
     lowerCAmelCase_					:       Tuple    =		{
         """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 							_lowerCAmelCase (  _a   :					Tuple			)	->			Optional[int]:
     if params.n_gpu <= 0:
          lowerCAmelCase_					:       List[str]    =		0
          lowerCAmelCase_					:       Tuple    =		-1
          lowerCAmelCase_					:       int    =		True
          lowerCAmelCase_					:       Any    =		False
          return

     assert torch.cuda.is_available()

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

          lowerCAmelCase_					:       List[Any]    =		int(os.environ["""WORLD_SIZE"""]			)
          lowerCAmelCase_					:       Tuple    =		int(os.environ["""N_GPU_NODE"""]			)
          lowerCAmelCase_					:       Dict    =		int(os.environ["""RANK"""]			)

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

          lowerCAmelCase_					:       str    =		1
          lowerCAmelCase_					:       Any    =		0
          lowerCAmelCase_					:       Dict    =		0
          lowerCAmelCase_					:       List[str]    =		0
          lowerCAmelCase_					:       Dict    =		1
          lowerCAmelCase_					:       List[str]    =		1
          lowerCAmelCase_					:       List[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
     lowerCAmelCase_					:       Optional[Any]    =		params.node_id == 0 and params.local_rank == 0
     lowerCAmelCase_					:       str    =		params.n_nodes > 1

     # summary
     lowerCAmelCase_					:       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 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			Union[str, Any]:
     np.random.seed(args.seed			)
     torch.manual_seed(args.seed			)
     if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed			)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
