





'''simple docstring'''




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
  from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm






def 	_lowerCAmelCase      (  __snake_case  :			Optional[Any]      )					->					Any:
     if is_torch_version('<'			,							'2.0.0'      ) or not hasattr(__snake_case			,							'_dynamo'      ):
          return False
     return isinstance(__snake_case			,							torch._dynamo.eval_frame.OptimizedModule      )






def 	_lowerCAmelCase      (  __snake_case  :			Dict			,							__snake_case  :			bool = True      )					->					Optional[int]:
     __A						:			Optional[int]								=	(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     __A						:			Dict								=	is_compiled_module(__snake_case      )
     if is_compiled:
          __A						:			List[Any]								=	model
          __A						:			Optional[int]								=	model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(__snake_case			,							__snake_case      ):
          __A						:			Any								=	model.module

     if not keep_fpaa_wrapper:
          __A						:			Tuple								=	getattr(__snake_case			,							'forward'      )
          __A						:			Any								=	model.__dict__.pop('_original_forward'			,							__snake_case      )
          if original_forward is not None:
               while hasattr(__snake_case			,							'__wrapped__'      ):
                    __A						:			str								=	forward.__wrapped__
                    if forward == original_forward:
                         break
               __A						:			List[Any]								=	forward
          if getattr(__snake_case			,							'_converted_to_transformer_engine'			,							__snake_case      ):
               convert_model(__snake_case			,							to_transformer_engine=__snake_case      )

     if is_compiled:
          __A						:			Union[str, Any]								=	model
          __A						:			Optional[Any]								=	compiled_model

     return model






def 	_lowerCAmelCase      (  )					->					List[str]:
     PartialState().wait_for_everyone()






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			List[str]      )					->					Dict:
     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(__snake_case			,							__snake_case      )
     elif PartialState().local_process_index == 0:
          torch.save(__snake_case			,							__snake_case      )






@contextmanager
def 	_lowerCAmelCase      (  **__snake_case  :			Optional[Any]      )					->					str:
     for key, value in kwargs.items():
          __A						:			List[Any]								=	str(__snake_case      )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]






def 	_lowerCAmelCase      (  __snake_case  :			List[str]      )					->					int:
     if not hasattr(__snake_case			,							'__qualname__'      ) and not hasattr(__snake_case			,							'__name__'      ):
          __A						:			Union[str, Any]								=	getattr(__snake_case			,							'__class__'			,							__snake_case      )
     if hasattr(__snake_case			,							'__qualname__'      ):
          return obj.__qualname__
     if hasattr(__snake_case			,							'__name__'      ):
          return obj.__name__
     return str(__snake_case      )






def 	_lowerCAmelCase      (  __snake_case  :			Optional[int]			,							__snake_case  :			Tuple      )					->					str:
     for key, value in source.items():
          if isinstance(__snake_case			,							__snake_case      ):
               __A						:			Any								=	destination.setdefault(__snake_case			,							{}      )
               merge_dicts(__snake_case			,							__snake_case      )
          else:
               __A						:			str								=	value

     return destination







def 	_lowerCAmelCase      (  __snake_case  :			int = None      )					->					bool:
     if port is None:
          __A						:			List[str]								=	2_95_00
     with socket.socket(socket.AF_INET			,							socket.SOCK_STREAM      ) as s:
          return s.connect_ex(('localhost', port)      ) == 0






'''simple docstring'''




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowercase__   :				List[Any]      =   HfArgumentParser(InitializationArguments)
lowercase__   :				List[Any]      =   parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowercase__   :				Union[str, Any]      =   AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowercase__   :				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)
lowercase__   :				Optional[int]      =   AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowercase__   :				Optional[int]      =   AutoModelForCausalLM.from_config(config)

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