


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__    :List[str]       =				logging.getLogger(__name__)

def 			lowerCAmelCase__					(     a__:						str			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     git.Repo(search_parent_directories=a__			)
							_UpperCAmelCase											=     {
							    '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__:						int			)    ->						Union[str, Any]:


							'''simple docstring'''





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

							assert torch.cuda.is_available()

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

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

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

							# summary
							_UpperCAmelCase											=     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__:						int			)    ->						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			)




import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


lowerCAmelCase__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     R'\w+[.]\d+'
							_UpperCAmelCase											=     re.findall(a__     ,    a__			)
							for pat in pats:
														_UpperCAmelCase											=     key.replace(a__     ,    '_'.join(pat.split('.'			)			)			)
							return key

def 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''simple docstring'''





							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
							if (
							    any('norm' in str_ for str_ in pt_tuple_key			)
							    and (pt_tuple_key[-1] == "bias")
							    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
							    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
							):
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor
							elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('scale',)
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
														_UpperCAmelCase											=     pt_tuple_key[:-1] + ('embedding',)
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
														_UpperCAmelCase											=     pt_tensor.transpose(2     ,    3     ,    1     ,    0			)
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('kernel',)
							if pt_tuple_key[-1] == "weight":
														_UpperCAmelCase											=     pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('weight',)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							_UpperCAmelCase											=     pt_tuple_key[:-1] + ('bias',)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''simple docstring'''





							_UpperCAmelCase											=     {k: v.numpy() for k, v in pt_state_dict.items()}

							# Step 2: Since the model is stateless, get random Flax params
							_UpperCAmelCase											=     flax_model.init_weights(PRNGKey(a__			)			)

							_UpperCAmelCase											=     flatten_dict(a__			)
							_UpperCAmelCase											=     {}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														_UpperCAmelCase											=     rename_key(a__			)
														_UpperCAmelCase											=     tuple(renamed_pt_key.split('.'			)			)

														# Correctly rename weight parameters
														_UpperCAmelCase						,			_UpperCAmelCase											=     rename_key_and_reshape_tensor(a__     ,    a__     ,    a__			)

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
																												    F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

        # also add unexpected weight so that warning is thrown
														_UpperCAmelCase											=     jnp.asarray(a__			)

							return unflatten_dict(a__			)
