






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       lowercase__	(					A_:						Any		)	->				str:







       """simple docstring"""






       if is_torch_version("""<"""  ,      """2.0.0"""		) or not hasattr(A_  ,      """_dynamo"""		):
              return False
       return isinstance(A_  ,      torch._dynamo.eval_frame.OptimizedModule		)







def       lowercase__	(					A_:						Optional[Any]  ,      A_:						List[Any] = True		)	->				str:







       """simple docstring"""






       __UpperCAmelCase     				=(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

       __UpperCAmelCase     				=is_compiled_module(A_		)
       if is_compiled:
              __UpperCAmelCase     				=model
              __UpperCAmelCase     				=model._orig_mod

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

       while isinstance(A_  ,      A_		):
              __UpperCAmelCase     				=model.module

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

       if is_compiled:
              __UpperCAmelCase     				=model
              __UpperCAmelCase     				=compiled_model

       return model







def       lowercase__	(					)	->				List[str]:







       """simple docstring"""






       PartialState().wait_for_everyone()







def       lowercase__	(					A_:						Optional[int]  ,      A_:						Union[str, Any]		)	->				Dict:







       """simple docstring"""






       if PartialState().distributed_type == DistributedType.TPU:
              xm.save(A_  ,      A_		)
       elif PartialState().local_process_index == 0:
              torch.save(A_  ,      A_		)







@contextmanager
def       lowercase__	(					**A_:						Any		)	->				List[Any]:







       """simple docstring"""






       for key, value in kwargs.items():
              __UpperCAmelCase     				=str(A_		)

       yield

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







def       lowercase__	(					A_:						Any		)	->				List[Any]:







       """simple docstring"""






       if not hasattr(A_  ,      """__qualname__"""		) and not hasattr(A_  ,      """__name__"""		):
              __UpperCAmelCase     				=getattr(A_  ,      """__class__"""  ,      A_		)
       if hasattr(A_  ,      """__qualname__"""		):
              return obj.__qualname__
       if hasattr(A_  ,      """__name__"""		):
              return obj.__name__
       return str(A_		)







def       lowercase__	(					A_:						Optional[Any]  ,      A_:						Dict		)	->				List[str]:







       """simple docstring"""






       for key, value in source.items():
              if isinstance(A_  ,      A_		):
                     __UpperCAmelCase     				=destination.setdefault(A_  ,      {}		)
                     merge_dicts(A_  ,      A_		)
              else:
                     __UpperCAmelCase     				=value

       return destination







def       lowercase__	(					A_:						Optional[int] = None		)	->				Optional[Any]:







       """simple docstring"""






       if port is None:
              __UpperCAmelCase     				=29500
       with socket.socket(socket.AF_INET  ,      socket.SOCK_STREAM		) as s:
              return s.connect_ex(("""localhost""", port)		) == 0







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    'table-transformer'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Union[str, Any]  					=    {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    Any=1_0_0			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[Any]=2_0_4_8			,						lowerCamelCase__	:    List[str]=8			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[str]=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=8			,						lowerCamelCase__	:    Union[str, Any]=0.0			,						lowerCamelCase__	:    Any=0.0			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]="relu"			,						lowerCamelCase__	:    Optional[int]=2_5_6			,						lowerCamelCase__	:    List[str]=0.1			,						lowerCamelCase__	:    str=0.0			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Dict=0.02			,						lowerCamelCase__	:    Union[str, Any]=1.0			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[int]="sine"			,						lowerCamelCase__	:    str="resnet50"			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    Any=1			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    List[str]=1			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Tuple=2			,						lowerCamelCase__	:    List[str]=0.1			,						**lowerCamelCase__	:    List[str]			,						) ->			Dict:
         """simple docstring"""





         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
                     A_							       =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
               elif isinstance(lowerCamelCase__			,						lowerCamelCase__       ):
                     A_							       =		backbone_config.get('''model_type'''       )
                     A_							       =		CONFIG_MAPPING[backbone_model_type]
                     A_							       =		config_class.from_dict(lowerCamelCase__       )
               # set timm attributes to None
               A_       ,A_       ,A_							       =		None, None, None

         A_							       =		use_timm_backbone
         A_							       =		backbone_config
         A_							       =		num_channels
         A_							       =		num_queries
         A_							       =		d_model
         A_							       =		encoder_ffn_dim
         A_							       =		encoder_layers
         A_							       =		encoder_attention_heads
         A_							       =		decoder_ffn_dim
         A_							       =		decoder_layers
         A_							       =		decoder_attention_heads
         A_							       =		dropout
         A_							       =		attention_dropout
         A_							       =		activation_dropout
         A_							       =		activation_function
         A_							       =		init_std
         A_							       =		init_xavier_std
         A_							       =		encoder_layerdrop
         A_							       =		decoder_layerdrop
         A_							       =		encoder_layers
         A_							       =		auxiliary_loss
         A_							       =		position_embedding_type
         A_							       =		backbone
         A_							       =		use_pretrained_backbone
         A_							       =		dilation
         # Hungarian matcher
         A_							       =		class_cost
         A_							       =		bbox_cost
         A_							       =		giou_cost
         # Loss coefficients
         A_							       =		mask_loss_coefficient
         A_							       =		dice_loss_coefficient
         A_							       =		bbox_loss_coefficient
         A_							       =		giou_loss_coefficient
         A_							       =		eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase__			,						**lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         return self.encoder_attention_heads





   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			int:
         """simple docstring"""





         return self.d_model



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    version.parse('1.11'			)

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-5





   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         return 1_2





