



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,
)
__snake_case    :		str  =logging.getLogger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[int]												=					git.Repo(search_parent_directories=lowerCamelCase_)
				lowerCAmelCase__					:    int												=					{
				    '''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      lowerCAmelCase__		(   lowerCamelCase_		:		Optional[Any]):





				'''simple docstring'''

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

				assert torch.cuda.is_available()

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

								lowerCAmelCase__					:    List[str]												=					int(os.environ['''WORLD_SIZE'''])
								lowerCAmelCase__					:    Optional[int]												=					int(os.environ['''N_GPU_NODE'''])
								lowerCAmelCase__					:    str												=					int(os.environ['''RANK'''])

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

				# summary
				lowerCAmelCase__					:    Dict												=					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__		(   lowerCamelCase_		:		Union[str, Any]):





				'''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 arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
__snake_case    :		Dict  =HfArgumentParser(InitializationArguments)
__snake_case    :		Tuple  =parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
__snake_case    :		Optional[int]  =AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case    :		List[str]  ={
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
__snake_case    :		List[Any]  =AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
__snake_case    :		int  =AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)

