





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


lowercase_          =		logging.get_logger(__name__)

lowercase_          =		{
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		Optional[int]			 =	'vit_msn'


   def __init__(   self      :    Optional[int]      ,					a      :    Any=768      ,					a      :    List[Any]=12      ,					a      :    str=12      ,					a      :    Any=3_072      ,					a      :    List[str]="gelu"      ,					a      :    str=0.0      ,					a      :    int=0.0      ,					a      :    Optional[int]=0.02      ,					a      :    Any=1E-0_6      ,					a      :    str=224      ,					a      :    Tuple=16      ,					a      :    Dict=3      ,					a      :    Optional[Any]=True      ,					**a      :    Union[str, Any]      ,					)->       int:





     """simple docstring"""







     super().__init__(**a	)

     lowercase__           =  hidden_size
     lowercase__           =  num_hidden_layers
     lowercase__           =  num_attention_heads
     lowercase__           =  intermediate_size
     lowercase__           =  hidden_act
     lowercase__           =  hidden_dropout_prob
     lowercase__           =  attention_probs_dropout_prob
     lowercase__           =  initializer_range
     lowercase__           =  layer_norm_eps
     lowercase__           =  image_size
     lowercase__           =  patch_size
     lowercase__           =  num_channels
     lowercase__           =  qkv_bias









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       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if is_torch_version('<'		,						'2.0.0'		) or not hasattr(_SCREAMING_SNAKE_CASE		,						'_dynamo'		):
    return False
  return isinstance(_SCREAMING_SNAKE_CASE		,						torch._dynamo.eval_frame.OptimizedModule		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = True		)    ->       Dict:
  lowercase__           =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

  lowercase__           =  is_compiled_module(_SCREAMING_SNAKE_CASE		)
  if is_compiled:
    lowercase__           =  model
    lowercase__           =  model._orig_mod

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

  while isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
    lowercase__           =  model.module

  if not keep_fpaa_wrapper:
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'forward'		)
    lowercase__           =  model.__dict__.pop('_original_forward'		,						_SCREAMING_SNAKE_CASE		)
    if original_forward is not None:
      while hasattr(_SCREAMING_SNAKE_CASE		,						'__wrapped__'		):
        lowercase__           =  forward.__wrapped__
        if forward == original_forward:
          break
      lowercase__           =  forward
    if getattr(_SCREAMING_SNAKE_CASE		,						'_converted_to_transformer_engine'		,						_SCREAMING_SNAKE_CASE		):
      convert_model(_SCREAMING_SNAKE_CASE		,						to_transformer_engine=_SCREAMING_SNAKE_CASE		)

  if is_compiled:
    lowercase__           =  model
    lowercase__           =  compiled_model

  return model




def       __UpperCamelCase  ()    ->       Tuple:
  PartialState().wait_for_everyone()




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




@contextmanager
def       __UpperCamelCase  (**_SCREAMING_SNAKE_CASE		)    ->       Optional[Any]:
  for key, value in kwargs.items():
    lowercase__           =  str(_SCREAMING_SNAKE_CASE		)

  yield

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       List[str]:
  if not hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		) and not hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    lowercase__           =  getattr(_SCREAMING_SNAKE_CASE		,						'__class__'		,						_SCREAMING_SNAKE_CASE		)
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__qualname__'		):
    return obj.__qualname__
  if hasattr(_SCREAMING_SNAKE_CASE		,						'__name__'		):
    return obj.__name__
  return str(_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Dict:
  for key, value in source.items():
    if isinstance(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		):
      lowercase__           =  destination.setdefault(_SCREAMING_SNAKE_CASE		,						{}		)
      merge_dicts(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
    else:
      lowercase__           =  value

  return destination




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE = None		)    ->       bool:
  if port is None:
    lowercase__           =  29500
  with socket.socket(socket.AF_INET		,						socket.SOCK_STREAM		) as s:
    return s.connect_ex(('localhost', port)		) == 0


