

import math
import random




def 					_lowercase     (				a_				:					float					,a_				:					bool = False				)				->	float:




					'''simple docstring'''







					if deriv:
										return value * (1 - value)
					return 1 / (1 + math.exp(-value				))


# Initial Value
A__						=							0.02




def 					_lowercase     (				a_				:					int					,a_				:					int				)				->	float:




					'''simple docstring'''







					__magic_name__							=    float(2 * (random.randint(1					,1_0_0				)) - 1				)

					for _ in range(a_				):
										# Forward propagation
										__magic_name__							=    sigmoid_function(INITIAL_VALUE * weight				)
										# How much did we miss?
										__magic_name__							=    (expected / 1_0_0) - layer_a
										# Error delta
										__magic_name__							=    layer_1_error * sigmoid_function(a_					,a_				)
										# Update weight
										weight += INITIAL_VALUE * layer_1_delta

					return layer_a * 1_0_0


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						A__						=							int(input("Expected value: "))
						A__						=							int(input("Number of propagations: "))
						print(forward_propagation(expected, number_propagations))







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


A__						=							logging.get_logger(__name__)

A__						=							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





						super().__init__(
						    vocab_size=__UpperCamelCase  , hidden_size=__UpperCamelCase  , num_hidden_layers=__UpperCamelCase  , num_attention_heads=__UpperCamelCase  , intermediate_size=__UpperCamelCase  , hidden_act=__UpperCamelCase  , hidden_dropout_prob=__UpperCamelCase  , attention_probs_dropout_prob=__UpperCamelCase  , max_position_embeddings=__UpperCamelCase  , type_vocab_size=__UpperCamelCase  , initializer_range=__UpperCamelCase  , layer_norm_eps=__UpperCamelCase  , pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase  , )
						__magic_name__							=    max_ad_position_embeddings
						__magic_name__							=    coordinate_size
						__magic_name__							=    shape_size
						__magic_name__							=    has_relative_attention_bias
						__magic_name__							=    rel_pos_bins
						__magic_name__							=    max_rel_pos
						__magic_name__							=    has_spatial_attention_bias
						__magic_name__							=    rel_ad_pos_bins
						__magic_name__							=    max_rel_ad_pos
						__magic_name__							=    text_embed
						__magic_name__							=    visual_embed
						__magic_name__							=    input_size
						__magic_name__							=    num_channels
						__magic_name__							=    patch_size
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]      )
						else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_batch  , num_token_to_add=0      )
						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__magic_name__							=    processor.tokenizer.num_special_tokens_to_add(__UpperCamelCase      )
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_sequence  , num_token_to_add=__UpperCamelCase      )
						# Generate dummy inputs according to compute batch and sequence
						__magic_name__							=    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__magic_name__							=    [[[48, 84, 73, 1_28]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




