




"""simple docstring"""





import numpy

# List of input, output pairs
A   =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
A   =       (((515, 22, 13), 555), ((61, 35, 49), 150))
A   =       [2, 4, 1, 5]
A   =       len(train_data)
A   =       0.009

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase="train"							)			->			Optional[int]:


			"""simple docstring"""
			return calculate_hypothesis_value(__snake_case				,       __snake_case							) - output(
			    __snake_case				,       __snake_case							)

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: List[Any]		=					0
			for i in range(len(__snake_case							) - 1							):
						hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
			hyp_val += parameter_vector[0]
			return hyp_val

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""







			if data_set == "train":
						return train_data[example_no][1]
			elif data_set == "test":
						return test_data[example_no][1]
			return None

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Tuple:


			"""simple docstring"""







			if data_set == "train":
						return _hypothesis_value(train_data[example_no][0]							)
			elif data_set == "test":
						return _hypothesis_value(test_data[example_no][0]							)
			return None

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase=m							)			->			List[str]:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					0
			for i in range(__snake_case							):
						if index == -1:
									summation_value += _error(__snake_case							)
						else:
									summation_value += _error(__snake_case							) * train_data[i][0][index]
			return summation_value

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					summation_of_cost_derivative(__snake_case				,       __snake_case							) / m
			return cost_derivative_value

def 					_UpperCamelCase    (			)			->			List[str]:


			"""simple docstring"""
			global parameter_vector
			# Tune these values to set a tolerance value for predicted output
			__UpperCAmelCase	: int		=					0.000002
			__UpperCAmelCase	: Union[str, Any]		=					0
			__UpperCAmelCase	: str		=					0
			while True:
						j += 1
						__UpperCAmelCase	: str		=					[0, 0, 0, 0]
						for i in range(0				,       len(__snake_case							)							):
									__UpperCAmelCase	: Tuple		=					get_cost_derivative(i - 1							)
									__UpperCAmelCase	: Any		=					(
									    parameter_vector[i] - LEARNING_RATE * cost_derivative
									)
						if numpy.allclose(
						    __snake_case				,       __snake_case				,       atol=__snake_case				,       rtol=__snake_case				,       ):
									break
						__UpperCAmelCase	: str		=					temp_parameter_vector
			print(("Number of iterations:", j)							)

def 					_UpperCamelCase    (			)			->			Tuple:


			"""simple docstring"""







			for i in range(len(__snake_case							)							):
						print(("Actual output value:", output(__snake_case				,       "test"							))							)
						print(("Hypothesis output:", calculate_hypothesis_value(__snake_case				,       "test"							))							)


if __name__ == "__main__":
					run_gradient_descent()
					print("""\nTesting gradient descent for a linear hypothesis function.\n""")
					test_gradient_descent()








"""simple docstring"""





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



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""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.")
												__UpperCAmelCase	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


