




"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A   =       logging.get_logger(__name__)

A   =       {
    """google/vit-base-patch16-224""": """https://huggingface.co/vit-base-patch16-224/resolve/main/config.json""",
    # See all ViT models at https://huggingface.co/models?filter=vit
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"vit"


			def __init__(      self      :    Any      ,  UpperCamelCase_      :    int=768      ,  UpperCamelCase_      :    Any=12      ,  UpperCamelCase_      :    Any=12      ,  UpperCamelCase_      :    int=3072      ,  UpperCamelCase_      :    List[str]="gelu"      ,  UpperCamelCase_      :    List[Any]=0.0      ,  UpperCamelCase_      :    List[str]=0.0      ,  UpperCamelCase_      :    Optional[Any]=0.02      ,  UpperCamelCase_      :    Tuple=1e-12      ,  UpperCamelCase_      :    Dict=224      ,  UpperCamelCase_      :    str=16      ,  UpperCamelCase_      :    Dict=3      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    List[str]=16      ,  **UpperCamelCase_      :    int      ,  ):


						"""simple docstring"""




						super().__init__(**UpperCamelCase_)

						__UpperCAmelCase	: int		=					hidden_size
						__UpperCAmelCase	: Optional[int]		=					num_hidden_layers
						__UpperCAmelCase	: str		=					num_attention_heads
						__UpperCAmelCase	: List[Any]		=					intermediate_size
						__UpperCAmelCase	: List[Any]		=					hidden_act
						__UpperCAmelCase	: Any		=					hidden_dropout_prob
						__UpperCAmelCase	: int		=					attention_probs_dropout_prob
						__UpperCAmelCase	: Dict		=					initializer_range
						__UpperCAmelCase	: Any		=					layer_norm_eps
						__UpperCAmelCase	: str		=					image_size
						__UpperCAmelCase	: Dict		=					patch_size
						__UpperCAmelCase	: Optional[Any]		=					num_channels
						__UpperCAmelCase	: Any		=					qkv_bias
						__UpperCAmelCase	: List[str]		=					encoder_stride






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    List[Any]):


						"""simple docstring"""




						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						    ])


			@property
			def       a_     (      self      :    Any):


						"""simple docstring"""




						return 1e-4








"""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,
)
A   =       logging.getLogger(__name__)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					git.Repo(search_parent_directories=UpperCamelCase							)
			__UpperCAmelCase	: Any		=					{
			    "repo_id": str(UpperCamelCase							),
			    "repo_sha": str(repo.head.object.hexsha							),
			    "repo_branch": str(repo.active_branch							),
			}

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

def 					_UpperCamelCase    (			UpperCamelCase							)			->			str:


			"""simple docstring"""
			if params.n_gpu <= 0:
						__UpperCAmelCase	: str		=					0
						__UpperCAmelCase	: Dict		=					-1
						__UpperCAmelCase	: Tuple		=					True
						__UpperCAmelCase	: List[str]		=					False
						return

			assert torch.cuda.is_available()

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

						__UpperCAmelCase	: Optional[int]		=					int(os.environ["WORLD_SIZE"]							)
						__UpperCAmelCase	: Union[str, Any]		=					int(os.environ["N_GPU_NODE"]							)
						__UpperCAmelCase	: Optional[int]		=					int(os.environ["RANK"]							)

						# number of nodes / node ID
						__UpperCAmelCase	: int		=					params.world_size // params.n_gpu_per_node
						__UpperCAmelCase	: Optional[int]		=					params.global_rank // params.n_gpu_per_node
						__UpperCAmelCase	: Tuple		=					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	: Union[str, Any]		=					1
						__UpperCAmelCase	: Dict		=					0
						__UpperCAmelCase	: List[Any]		=					0
						__UpperCAmelCase	: Tuple		=					0
						__UpperCAmelCase	: Any		=					1
						__UpperCAmelCase	: str		=					1
						__UpperCAmelCase	: Dict		=					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	: Union[str, Any]		=					params.node_id == 0 and params.local_rank == 0
			__UpperCAmelCase	: Dict		=					params.n_nodes > 1

			# summary
			__UpperCAmelCase	: 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 					_UpperCamelCase    (			UpperCamelCase							)			->			Tuple:


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


