

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


a		=logging.get_logger(__name__)

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






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  List[str]						      =      '''conditional_detr'''
							_UpperCAmelCase    :  int						      =      ['''past_key_values''']
							_UpperCAmelCase    :  Optional[int]						      =      {
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							}
							def __init__(		self		:			Dict   ,SCREAMING_SNAKE_CASE__		:			int=True   ,SCREAMING_SNAKE_CASE__		:			List[str]=None   ,SCREAMING_SNAKE_CASE__		:			List[str]=3   ,SCREAMING_SNAKE_CASE__		:			int=3_0_0   ,SCREAMING_SNAKE_CASE__		:			str=6   ,SCREAMING_SNAKE_CASE__		:			Dict=2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=8   ,SCREAMING_SNAKE_CASE__		:			int=6   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=2_0_4_8   ,SCREAMING_SNAKE_CASE__		:			List[Any]=8   ,SCREAMING_SNAKE_CASE__		:			int=0.0   ,SCREAMING_SNAKE_CASE__		:			Tuple=0.0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]="relu"   ,SCREAMING_SNAKE_CASE__		:			List[Any]=2_5_6   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.1   ,SCREAMING_SNAKE_CASE__		:			Any=0.0   ,SCREAMING_SNAKE_CASE__		:			List[Any]=0.0   ,SCREAMING_SNAKE_CASE__		:			str=0.02   ,SCREAMING_SNAKE_CASE__		:			Tuple=1.0   ,SCREAMING_SNAKE_CASE__		:			str=False   ,SCREAMING_SNAKE_CASE__		:			Dict="sine"   ,SCREAMING_SNAKE_CASE__		:			int="resnet50"   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=True   ,SCREAMING_SNAKE_CASE__		:			str=False   ,SCREAMING_SNAKE_CASE__		:			Tuple=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=5   ,SCREAMING_SNAKE_CASE__		:			int=2   ,SCREAMING_SNAKE_CASE__		:			List[str]=1   ,SCREAMING_SNAKE_CASE__		:			int=1   ,SCREAMING_SNAKE_CASE__		:			str=2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=5   ,SCREAMING_SNAKE_CASE__		:			int=2   ,SCREAMING_SNAKE_CASE__		:			Dict=0.25   ,**SCREAMING_SNAKE_CASE__		:			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				:				str					       = CONFIG_MAPPING['resnet'](out_features=['stage4'])
									elif isinstance(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__):
										__lowerCamelCase				:				int					       = backbone_config.get('model_type')
										__lowerCamelCase				:				Optional[int]					       = CONFIG_MAPPING[backbone_model_type]
										__lowerCamelCase				:				int					       = config_class.from_dict(SCREAMING_SNAKE_CASE__)

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


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






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  int						      =      version.parse('''1.11'''				)
							@property
							def     lowerCAmelCase     (		self		:			Optional[int]):
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								        ('pixel_mask', {0: 'batch'}),
								    ])
							@property
							def     lowerCAmelCase     (		self		:			Optional[Any]):
								return 1E-5


							@property
							def     lowerCAmelCase     (		self		:			str):
								return 1_2









"""simple docstring"""

from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403




def __lowercase			(							_a		,			_a		,			_a = "x"		,			_a = 10**-10		,			_a = 1		,			):
    snake_case_ :     Any				=	symbols(_a    )
    snake_case_ :     int				=	lambdify(_a		,			_a    )
    snake_case_ :     Optional[Any]				=	lambdify(_a		,			diff(_a		,			_a    )    )

    snake_case_ :     Optional[Any]				=	starting_point

    while True:
        if diff_function(_a    ) != 0:
            snake_case_ :     Optional[int]				=	prev_guess - multiplicity * func(_a    ) / diff_function(
                _a    )
        else:
            raise ZeroDivisionError('''Could not find root'''    ) from None

        # Precision is checked by comparing the difference of consecutive guesses
        if abs(next_guess - prev_guess    ) < precision:
            return next_guess

        snake_case_ :     int				=	next_guess


# Let's Execute
if __name__ == "__main__":
     # Find root of trigonometric function
     # Find value of pi
     print(f'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

     # Find root of polynomial
     # Find fourth Root of 5
     print(f'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

     # Find value of e
     print(
         '''The root of log(y) - 1 = 0 is ''',
         f'{newton_raphson("log(y) - 1", 2, variable="y")}',
     )

     # Exponential Roots
     print(
         '''The root of exp(x) - 1 = 0 is''',
         f'{newton_raphson("exp(x) - 1", 10, precision=0.005)}',
     )

     # Find root of cos(x)
     print(f'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')





