
'''simple docstring'''






import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


UpperCAmelCase_       :   int   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Optional[Any]   =       {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'detr'
    lowerCAmelCase_				    =		['past_key_values']
    lowerCAmelCase_				    =		{
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }



    def __init__(	self      :       Optional[int],__A      :       Tuple=True,__A      :       List[Any]=None,__A      :       List[str]=3,__A      :       Any=1_0_0,__A      :       Union[str, Any]=6,__A      :       Union[str, Any]=2_0_4_8,__A      :       str=8,__A      :       Dict=6,__A      :       Tuple=2_0_4_8,__A      :       Optional[int]=8,__A      :       int=0.0,__A      :       Union[str, Any]=0.0,__A      :       Optional[Any]=True,__A      :       Union[str, Any]="relu",__A      :       str=2_5_6,__A      :       int=0.1,__A      :       List[str]=0.0,__A      :       Union[str, Any]=0.0,__A      :       int=0.02,__A      :       Union[str, Any]=1.0,__A      :       str=False,__A      :       int="sine",__A      :       List[str]="resnet50",__A      :       List[Any]=True,__A      :       Tuple=False,__A      :       Dict=1,__A      :       Optional[Any]=5,__A      :       Optional[int]=2,__A      :       List[str]=1,__A      :       int=1,__A      :       str=5,__A      :       List[Any]=2,__A      :       Union[str, Any]=0.1,**__A      :       Union[str, Any],):
          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."     )
                      _lowerCamelCase				:   int								=   CONFIG_MAPPING["resnet"](out_features=["stage4"]     )
                elif isinstance(__A,__A     ):
                      _lowerCamelCase				:   Union[str, Any]								=   backbone_config.get("model_type"     )
                      _lowerCamelCase				:   Optional[Any]								=   CONFIG_MAPPING[backbone_model_type]
                      _lowerCamelCase				:   Optional[int]								=   config_class.from_dict(__A     )
                # set timm attributes to None
                _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   List[Any]								=   None, None, None

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



    @property
    def   lowerCamelCase_  (	self      :       Tuple     ):
          return self.encoder_attention_heads



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          return self.d_model



    @classmethod
    def   lowerCamelCase_  (	cls      :       str,__A      :       PretrainedConfig,**__A      :       Optional[Any]     ):
          return cls(backbone_config=__A,**__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   copy.deepcopy(self.__dict__     )
          if output["backbone_config"] is not None:
                _lowerCamelCase				:   List[Any]								=   self.backbone_config.to_dict()
          _lowerCamelCase				:   List[str]								=   self.__class__.model_type
          return output


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		version.parse('1.11' )



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                  ("pixel_mask", {0: "batch"}),
              ]     )



    @property
    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          return 1e-5



    @property
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return 1_2

'''simple docstring'''






import random
from typing import Any







def 		A_       (	_lowerCAmelCase   :    list							):



      """simple docstring"""




      for _ in range(len(_lowerCAmelCase							)							):
            _lowerCamelCase				:   Any								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase				:   List[str]								=   random.randint(0				,	len(_lowerCAmelCase							) - 1							)
            _lowerCamelCase						,		_lowerCamelCase				:   Union[str, Any]								=   data[b], data[a]
      return data


if __name__ == "__main__":
 UpperCAmelCase_       :   Any   =       [0, 1, 2, 3, 4, 5, 6, 7]
 UpperCAmelCase_       :   Dict   =       ['python', 'says', 'hello', '!']
 print('Fisher-Yates Shuffle:')
 print('List', integers, strings)
 print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))