






'''simple docstring'''

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


A:      Union[str, Any]  				=				logging.get_logger(__name__)

A:      Optional[Any]  				=				{
    "RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
    "RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
    "RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
    "RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
    "RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
    "RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
    "RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
    "RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      int        =  'rwkv'
	SCREAMING_SNAKE_CASE_					:      int        =  {'max_position_embeddings': 'context_length'}
	def __init__(							self  ,      _lowercase=5_0277  ,      _lowercase=1024  ,      _lowercase=4096  ,      _lowercase=32  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=1E-5  ,      _lowercase=0  ,      _lowercase=0  ,      _lowercase=6  ,      _lowercase=False  ,      _lowercase=True  ,      **_lowercase  ,      )						->  int:
							lowercase_      :   Any        =			vocab_size
							lowercase_      :   List[str]        =			context_length
							lowercase_      :   List[str]        =			hidden_size
							lowercase_      :   Any        =			num_hidden_layers
							lowercase_      :   Any        =			attention_hidden_size if attention_hidden_size is not None else hidden_size
							lowercase_      :   Tuple        =			intermediate_size if intermediate_size is not None else 4 * hidden_size
							lowercase_      :   List[Any]        =			layer_norm_epsilon
							lowercase_      :   Any        =			rescale_every
							lowercase_      :   List[str]        =			use_cache

							lowercase_      :   Dict        =			bos_token_id
							lowercase_      :   List[str]        =			eos_token_id

							super().__init__(
							    tie_word_embeddings=_lowercase  ,      bos_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      **_lowercase       )








'''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:      Tuple  				=				logging.getLogger(__name__)






def 	_UpperCAmelCase    (  a		:	str							)			->			List[Any]:





						"""simple docstring"""
						lowercase_      :   List[str]        =			git.Repo(search_parent_directories=a							)
						lowercase_      :   Union[str, Any]        =			{
						    '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 	_UpperCAmelCase    (  a		:	str							)			->			Union[str, Any]:





						"""simple docstring"""
						if params.n_gpu <= 0:
												lowercase_      :   int        =			0
												lowercase_      :   Union[str, Any]        =			-1
												lowercase_      :   List[str]        =			True
												lowercase_      :   Optional[Any]        =			False
												return

						assert torch.cuda.is_available()

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

												lowercase_      :   Dict        =			int(os.environ['WORLD_SIZE']							)
												lowercase_      :   Union[str, Any]        =			int(os.environ['N_GPU_NODE']							)
												lowercase_      :   Optional[int]        =			int(os.environ['RANK']							)

												# number of nodes / node ID
												lowercase_      :   int        =			params.world_size // params.n_gpu_per_node
												lowercase_      :   str        =			params.global_rank // params.n_gpu_per_node
												lowercase_      :   Dict        =			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

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

						# summary
						lowercase_      :   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 	_UpperCAmelCase    (  a		:	Dict							)			->			Optional[int]:





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