




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 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       List[Any]:
   if is_torch_version("""<"""						,"""2.0.0"""					) or not hasattr(lowercase						,"""_dynamo"""					):
      return False
   return isinstance(lowercase						,torch._dynamo.eval_frame.OptimizedModule					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase = True					)  ->       List[Any]:
   snake_case				:    List[Any]           =	(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

   snake_case				:    str           =	is_compiled_module(lowercase					)
   if is_compiled:
      snake_case				:    List[Any]           =	model
      snake_case				:    Union[str, Any]           =	model._orig_mod

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

   while isinstance(lowercase						,lowercase					):
      snake_case				:    Union[str, Any]           =	model.module

   if not keep_fpaa_wrapper:
      snake_case				:    List[Any]           =	getattr(lowercase						,"""forward"""					)
      snake_case				:    Tuple           =	model.__dict__.pop("""_original_forward"""						,lowercase					)
      if original_forward is not None:
         while hasattr(lowercase						,"""__wrapped__"""					):
            snake_case				:    Optional[int]           =	forward.__wrapped__
            if forward == original_forward:
               break
         snake_case				:    Any           =	forward
      if getattr(lowercase						,"""_converted_to_transformer_engine"""						,lowercase					):
         convert_model(lowercase						,to_transformer_engine=lowercase					)

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

   return model




def 						SCREAMING_SNAKE_CASE__ (   )  ->       str:
   PartialState().wait_for_everyone()




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       str:
   if PartialState().distributed_type == DistributedType.TPU:
      xm.save(lowercase						,lowercase					)
   elif PartialState().local_process_index == 0:
      torch.save(lowercase						,lowercase					)




@contextmanager
def 						SCREAMING_SNAKE_CASE__ (   **lowercase					)  ->       Union[str, Any]:
   for key, value in kwargs.items():
      snake_case				:    Optional[Any]           =	str(lowercase					)

   yield

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




def 						SCREAMING_SNAKE_CASE__ (   lowercase					)  ->       Optional[Any]:
   if not hasattr(lowercase						,"""__qualname__"""					) and not hasattr(lowercase						,"""__name__"""					):
      snake_case				:    str           =	getattr(lowercase						,"""__class__"""						,lowercase					)
   if hasattr(lowercase						,"""__qualname__"""					):
      return obj.__qualname__
   if hasattr(lowercase						,"""__name__"""					):
      return obj.__name__
   return str(lowercase					)




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       Dict:
   for key, value in source.items():
      if isinstance(lowercase						,lowercase					):
         snake_case				:    str           =	destination.setdefault(lowercase						,{}					)
         merge_dicts(lowercase						,lowercase					)
      else:
         snake_case				:    str           =	value

   return destination




def 						SCREAMING_SNAKE_CASE__ (   lowercase = None					)  ->       bool:
   if port is None:
      snake_case				:    Optional[Any]           =	29500
   with socket.socket(socket.AF_INET						,socket.SOCK_STREAM					) as s:
      return s.connect_ex(("""localhost""", port)					) == 0











from typing import List

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


lowerCamelCase	:	Optional[int]				=   logging.get_logger(__name__)

lowerCamelCase	:	Tuple				=   {
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""efficientformer"""

      def __init__(				self    ,				A = [3, 2, 6, 4]    ,				A = [4_8, 9_6, 2_2_4, 4_4_8]    ,				A = [True, True, True, True]    ,				A = 4_4_8    ,				A = 3_2    ,				A = 4    ,				A = 7    ,				A = 5    ,				A = 8    ,				A = 4    ,				A = 0.0    ,				A = 1_6    ,				A = 3    ,				A = 3    ,				A = 3    ,				A = 2    ,				A = 1    ,				A = 0.0    ,				A = 1    ,				A = True    ,				A = True    ,				A = 1e-5    ,				A = "gelu"    ,				A = 0.02    ,				A = 1e-1_2    ,				A = 2_2_4    ,				A = 1e-0_5    ,				**A    ,				)		->    None:
         super().__init__(**A     )

         snake_case				:    Dict           =	hidden_act
         snake_case				:    int           =	hidden_dropout_prob
         snake_case				:    Any           =	hidden_sizes
         snake_case				:    Optional[Any]           =	num_hidden_layers
         snake_case				:    List[Any]           =	num_attention_heads
         snake_case				:    List[Any]           =	initializer_range
         snake_case				:    str           =	layer_norm_eps
         snake_case				:    Dict           =	patch_size
         snake_case				:    Optional[int]           =	num_channels
         snake_case				:    int           =	depths
         snake_case				:    Optional[int]           =	mlp_expansion_ratio
         snake_case				:    Any           =	downsamples
         snake_case				:    Dict           =	dim
         snake_case				:    Optional[int]           =	key_dim
         snake_case				:    Union[str, Any]           =	attention_ratio
         snake_case				:    Any           =	resolution
         snake_case				:    Dict           =	pool_size
         snake_case				:    Any           =	downsample_patch_size
         snake_case				:    Tuple           =	downsample_stride
         snake_case				:    Any           =	downsample_pad
         snake_case				:    Union[str, Any]           =	drop_path_rate
         snake_case				:    List[str]           =	num_metaad_blocks
         snake_case				:    Union[str, Any]           =	distillation
         snake_case				:    List[str]           =	use_layer_scale
         snake_case				:    int           =	layer_scale_init_value
         snake_case				:    Union[str, Any]           =	image_size
         snake_case				:    Dict           =	batch_norm_eps





