

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,
)
_lowerCAmelCase					:			int  				=			logging.getLogger(__name__)






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =git.Repo(search_parent_directories=_snake_case	)
			__a        ={
			    '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        UpperCamelCase_(							_snake_case   :  Dict	):




			"""simple docstring"""






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

			assert torch.cuda.is_available()

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

						__a        =int(os.environ['WORLD_SIZE']	)
						__a        =int(os.environ['N_GPU_NODE']	)
						__a        =int(os.environ['RANK']	)

						# number of nodes / node ID
						__a        =params.world_size // params.n_gpu_per_node
						__a        =params.global_rank // params.n_gpu_per_node
						__a        =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        =1
						__a        =0
						__a        =0
						__a        =0
						__a        =1
						__a        =1
						__a        =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        =params.node_id == 0 and params.local_rank == 0
			__a        =params.n_nodes > 1

			# summary
			__a        =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_(							_snake_case   :  Dict	):




			"""simple docstring"""






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









from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






