
'''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_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Dict     			=						git.Repo(search_parent_directories=__SCREAMING_SNAKE_CASE       )
    snake_case_ :    Optional[int]     			=						{
        "repo_id": str(__SCREAMING_SNAKE_CASE       ),
        "repo_sha": str(repo.head.object.hexsha       ),
        "repo_branch": str(repo.active_branch       ),
    }

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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    if params.n_gpu <= 0:
        snake_case_ :    int     			=						0
        snake_case_ :    int     			=						-1
        snake_case_ :    str     			=						True
        snake_case_ :    Union[str, Any]     			=						False
        return

    assert torch.cuda.is_available()

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

        snake_case_ :    Dict     			=						int(os.environ["WORLD_SIZE"]       )
        snake_case_ :    int     			=						int(os.environ["N_GPU_NODE"]       )
        snake_case_ :    Optional[Any]     			=						int(os.environ["RANK"]       )

        # number of nodes / node ID
        snake_case_ :    Optional[Any]     			=						params.world_size // params.n_gpu_per_node
        snake_case_ :    List[Any]     			=						params.global_rank // params.n_gpu_per_node
        snake_case_ :    List[str]     			=						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

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

    # summary
    snake_case_ :    Tuple     			=						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_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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






'''simple docstring'''
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


a_											=						["small", "medium", "large"]

a_											=						"lm_head.decoder.weight"
a_											=						"lm_head.weight"

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						torch.load(__SCREAMING_SNAKE_CASE       )
    snake_case_ :    List[Any]     			=						d.pop(__SCREAMING_SNAKE_CASE       )
    os.makedirs(__SCREAMING_SNAKE_CASE,		exist_ok=__SCREAMING_SNAKE_CASE       )
    torch.save(__SCREAMING_SNAKE_CASE,		os.path.join(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )       )


if __name__ == "__main__":
  a_											=						argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  a_											=						parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    a_											=						os.path.join(args.dialogpt_path, f"""{MODEL}_ft.pkl""")
    a_											=						f"""./DialoGPT-{MODEL}"""
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )




