
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


lowerCamelCase__     :   Optional[int]	=							logging.get_logger(__name__)

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: List[Any]	=		"layoutlmv3"

						def __init__(			self       ,							a=5_02_65       ,							a=7_68       ,							a=12       ,							a=12       ,							a=30_72       ,							a="gelu"       ,							a=0.1       ,							a=0.1       ,							a=5_12       ,							a=2       ,							a=0.02       ,							a=1e-5       ,							a=1       ,							a=0       ,							a=2       ,							a=10_24       ,							a=1_28       ,							a=1_28       ,							a=True       ,							a=32       ,							a=1_28       ,							a=64       ,							a=2_56       ,							a=True       ,							a=True       ,							a=True       ,							a=2_24       ,							a=3       ,							a=16       ,							a=None       ,							**a       ,							)					->      List[Any]:


								'''simple docstring'''

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[Any]	=		version.parse("1.12"	)

						@property
						def  A_				(			self				)					->      Optional[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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								return 1e-5

						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								return 12







						def  A_				(			self       ,							a       ,							a = -1       ,							a = -1       ,							a = False       ,							a = None       ,							a = 3       ,							a = 40       ,							a = 40       ,							)					->      List[Any]:


								'''simple docstring'''

								setattr(processor.image_processor       ,							"""apply_ocr"""       ,							a				)

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

								# Generate dummy bounding boxes
								_UpperCamelCase						    =	[[[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)
								_UpperCamelCase						    =	self._generate_dummy_images(a       ,							a       ,							a       ,							a				)

								_UpperCamelCase						    =	dict(
								    processor(
								        a       ,							text=a       ,							boxes=a       ,							return_tensors=a       ,							)				)

								return inputs





from math import isclose, sqrt


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float, float]:

		"""simple docstring"""






		_UpperCamelCase						    =	point_y / 4 / point_x
		_UpperCamelCase						    =	2 * normal_gradient / (1 + normal_gradient * normal_gradient)
		_UpperCamelCase						    =	(1 - normal_gradient * normal_gradient) / (
		    1 + normal_gradient * normal_gradient
		)
		_UpperCamelCase						    =	(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

		# to find the next point, solve the simultaeneous equations:
		# y^2 + 4x^2 = 100
		# y - b = m * (x - a)
		# ==> A x^2 + B x + C = 0
		_UpperCamelCase						    =	outgoing_gradient**2 + 4
		_UpperCamelCase						    =	2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
		_UpperCamelCase						    =	(point_y - outgoing_gradient * point_x) ** 2 - 1_0_0

		_UpperCamelCase						    =	(
		    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)
		_UpperCamelCase						    =	(
		    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term )
		) / (2 * quadratic_term)

		# two solutions, one of which is our input point
		_UpperCamelCase						    =	x_minus if isclose(lowerCAmelCase						,  lowerCAmelCase ) else x_plus
		_UpperCamelCase						    =	point_y + outgoing_gradient * (next_x - point_x)

		return next_x, next_y, outgoing_gradient


def 							__A(lowerCAmelCase = 1.4						,  lowerCAmelCase = -9.6 )		->				int:

		"""simple docstring"""






		_UpperCamelCase						    =	0
		_UpperCamelCase						    =	first_x_coord
		_UpperCamelCase						    =	first_y_coord
		_UpperCamelCase						    =	(10.1 - point_y) / (0.0 - point_x)

		while not (-0.01 <= point_x <= 0.01 and point_y > 0):
				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	next_point(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
				num_reflections += 1

		return num_reflections


if __name__ == "__main__":
		print(F"""{solution() = }""")



