

'''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


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}
class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Dict        ="conditional_detr"
			__A  :	Tuple        =["past_key_values"]
			__A  :	Optional[int]        ={
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}






			def __init__(  self			,_snake_case=True			,_snake_case=None			,_snake_case=3			,_snake_case=3_00			,_snake_case=6			,_snake_case=20_48			,_snake_case=8			,_snake_case=6			,_snake_case=20_48			,_snake_case=8			,_snake_case=0.0			,_snake_case=0.0			,_snake_case=True			,_snake_case="relu"			,_snake_case=2_56			,_snake_case=0.1			,_snake_case=0.0			,_snake_case=0.0			,_snake_case=0.02			,_snake_case=1.0			,_snake_case=False			,_snake_case="sine"			,_snake_case="resnet50"			,_snake_case=True			,_snake_case=False			,_snake_case=2			,_snake_case=5			,_snake_case=2			,_snake_case=1			,_snake_case=1			,_snake_case=2			,_snake_case=5			,_snake_case=2			,_snake_case=0.25			,**_snake_case			,):
				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_     :      Union[str, Any]						=  CONFIG_MAPPING["resnet"](out_features=["stage4"]     )
					elif isinstance(_snake_case			,_snake_case     ):
						UpperCAmelCase_     :      Dict						=  backbone_config.get("model_type"     )
						UpperCAmelCase_     :      Any						=  CONFIG_MAPPING[backbone_model_type]
						UpperCAmelCase_     :      List[str]						=  config_class.from_dict(_snake_case     )

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






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.encoder_attention_heads






			@property
			def 		UpperCamelCase__							(  self     ):
				return self.d_model






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  copy.deepcopy(self.__dict__     )
				if self.backbone_config is not None:
					UpperCAmelCase_     :      List[str]						=  self.backbone_config.to_dict()
				UpperCAmelCase_     :      Union[str, Any]						=  self.__class__.model_type
				return output




class 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	Optional[Any]        =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






def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Dict     							= set()

       # Replace all the whitespace in our sentence
       snake_case			:  List[Any]     							= input_str.replace(" "							,   ""       )
       for alpha in input_str:
              if "a" <= alpha.lower() <= "z":
                     frequency.add(alpha.lower()       )
       return len(__lowerCamelCase       ) == 26


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       snake_case			:  Optional[int]     							= [False] * 26
       for char in input_str:
              if char.islower():
                     snake_case			:  Dict     							= True
              elif char.isupper():
                     snake_case			:  Optional[Any]     							= True
       return all(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					str = "The quick brown fox jumps over the lazy dog"							,   ):
       return len({char for char in input_str.lower() if char.isalpha()}       ) == 26


def      UpperCamelCase   (       ):
       from timeit import timeit

       snake_case			:  Tuple     							= "from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
       print(timeit("is_pangram()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_faster()"							,   setup=__lowerCamelCase       )       )
       print(timeit("is_pangram_fastest()"							,   setup=__lowerCamelCase       )       )
       # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
       # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()





