






'''simple docstring'''

import copy
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


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[Any]      =						'''conditional_detr'''
 __a			:						Tuple      =						['''past_key_values''']
 __a			:						Any      =						{
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }
 def __init__(							self			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     __lowerCAmelCase=3			,     __lowerCAmelCase=3_00			,     __lowerCAmelCase=6			,     __lowerCAmelCase=20_48			,     __lowerCAmelCase=8			,     __lowerCAmelCase=6			,     __lowerCAmelCase=20_48			,     __lowerCAmelCase=8			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=True			,     __lowerCAmelCase="relu"			,     __lowerCAmelCase=2_56			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.0			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1.0			,     __lowerCAmelCase=False			,     __lowerCAmelCase="sine"			,     __lowerCAmelCase="resnet50"			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=2			,     __lowerCAmelCase=5			,     __lowerCAmelCase=2			,     __lowerCAmelCase=1			,     __lowerCAmelCase=1			,     __lowerCAmelCase=2			,     __lowerCAmelCase=5			,     __lowerCAmelCase=2			,     __lowerCAmelCase=0.25			,     **__lowerCAmelCase			,     ):
       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.""" )
                   UpperCamelCase_				:   int					    =							CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
             elif isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
                   UpperCamelCase_				:   List[Any]					    =							backbone_config.get("""model_type""" )
                   UpperCamelCase_				:   Union[str, Any]					    =							CONFIG_MAPPING[backbone_model_type]
                   UpperCamelCase_				:   Any					    =							config_class.from_dict(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							use_timm_backbone
       UpperCamelCase_				:   int					    =							backbone_config
       UpperCamelCase_				:   Dict					    =							num_channels
       UpperCamelCase_				:   Optional[Any]					    =							num_queries
       UpperCamelCase_				:   List[Any]					    =							d_model
       UpperCamelCase_				:   Any					    =							encoder_ffn_dim
       UpperCamelCase_				:   Dict					    =							encoder_layers
       UpperCamelCase_				:   List[str]					    =							encoder_attention_heads
       UpperCamelCase_				:   str					    =							decoder_ffn_dim
       UpperCamelCase_				:   str					    =							decoder_layers
       UpperCamelCase_				:   str					    =							decoder_attention_heads
       UpperCamelCase_				:   Optional[int]					    =							dropout
       UpperCamelCase_				:   Tuple					    =							attention_dropout
       UpperCamelCase_				:   Optional[int]					    =							activation_dropout
       UpperCamelCase_				:   List[Any]					    =							activation_function
       UpperCamelCase_				:   str					    =							init_std
       UpperCamelCase_				:   Dict					    =							init_xavier_std
       UpperCamelCase_				:   Optional[int]					    =							encoder_layerdrop
       UpperCamelCase_				:   Optional[Any]					    =							decoder_layerdrop
       UpperCamelCase_				:   Union[str, Any]					    =							encoder_layers
       UpperCamelCase_				:   List[str]					    =							auxiliary_loss
       UpperCamelCase_				:   Dict					    =							position_embedding_type
       UpperCamelCase_				:   Optional[Any]					    =							backbone
       UpperCamelCase_				:   Any					    =							use_pretrained_backbone
       UpperCamelCase_				:   Union[str, Any]					    =							dilation
       # Hungarian matcher
       UpperCamelCase_				:   Dict					    =							class_cost
       UpperCamelCase_				:   Dict					    =							bbox_cost
       UpperCamelCase_				:   Tuple					    =							giou_cost
       # Loss coefficients
       UpperCamelCase_				:   Tuple					    =							mask_loss_coefficient
       UpperCamelCase_				:   Optional[Any]					    =							dice_loss_coefficient
       UpperCamelCase_				:   Union[str, Any]					    =							cls_loss_coefficient
       UpperCamelCase_				:   Union[str, Any]					    =							bbox_loss_coefficient
       UpperCamelCase_				:   Optional[int]					    =							giou_loss_coefficient
       UpperCamelCase_				:   List[str]					    =							focal_alpha
       super().__init__(is_encoder_decoder=__lowerCAmelCase			,     **__lowerCAmelCase )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.encoder_attention_heads
 @property
 def   _UpperCAmelCase       (							self ):
       return self.d_model





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[Any]					    =							copy.deepcopy(self.__dict__ )
       if self.backbone_config is not None:
             UpperCamelCase_				:   str					    =							self.backbone_config.to_dict()
       UpperCamelCase_				:   Optional[int]					    =							self.__class__.model_type
       return output






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						str      =						version.parse('''1.11'''						)
 @property
 def   _UpperCAmelCase       (							self ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ] )
 @property
 def   _UpperCAmelCase       (							self ):
       return 1E-5





 @property
 def   _UpperCAmelCase       (							self ):
       return 12














'''simple docstring'''

from datetime import datetime

import matplotlib.pyplot as plt
import torch



def 							snake_case					(     a_							: List[Any]	)       ->				Any:





      """simple docstring"""

      for param in module.parameters():
            UpperCamelCase_				:   Dict					    =							False



def 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							"""cuda""" if torch.cuda.is_available() else """cpu"""
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            UpperCamelCase_				:   int					    =							"""mps"""
      if device == "mps":
            print(
                """WARNING: MPS currently doesn't seem to work, and messes up backpropagation without any visible torch"""
                """ errors. I recommend using CUDA on a colab notebook or CPU instead if you're facing inexplicable issues"""
                """ with generations."""	)
      return device



def 							snake_case					(     a_							: str	)       ->				Dict:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							plt.imshow(a_	)
      fig.axes.get_xaxis().set_visible(a_	)
      fig.axes.get_yaxis().set_visible(a_	)
      plt.show()



def 							snake_case					(     )       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							datetime.now()
      UpperCamelCase_				:   int					    =							current_time.strftime("""%H:%M:%S"""	)
      return timestamp






