



'''simple docstring'''




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     :    List[Any]      						=					logging.get_logger(__name__)

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

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				"""layoutlmv3"""





	def __init__(						self	,	A=50265	,	A=768	,	A=12	,	A=12	,	A=3072	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=512	,	A=2	,	A=0.0_2	,	A=1e-5	,	A=1	,	A=0	,	A=2	,	A=1024	,	A=128	,	A=128	,	A=True	,	A=32	,	A=128	,	A=64	,	A=256	,	A=True	,	A=True	,	A=True	,	A=224	,	A=3	,	A=16	,	A=None	,	**A	,	)    ->    int:
								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  :	Optional[int]							  =	max_ad_position_embeddings
								UpperCAmelCase  :	Optional[Any]							  =	coordinate_size
								UpperCAmelCase  :	int							  =	shape_size
								UpperCAmelCase  :	str							  =	has_relative_attention_bias
								UpperCAmelCase  :	Optional[int]							  =	rel_pos_bins
								UpperCAmelCase  :	List[str]							  =	max_rel_pos
								UpperCAmelCase  :	Tuple							  =	has_spatial_attention_bias
								UpperCAmelCase  :	Union[str, Any]							  =	rel_ad_pos_bins
								UpperCAmelCase  :	Optional[Any]							  =	max_rel_ad_pos
								UpperCAmelCase  :	Optional[int]							  =	text_embed
								UpperCAmelCase  :	Union[str, Any]							  =	visual_embed
								UpperCAmelCase  :	str							  =	input_size
								UpperCAmelCase  :	Dict							  =	num_channels
								UpperCAmelCase  :	List[str]							  =	patch_size
								UpperCAmelCase  :	int							  =	classifier_dropout




class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				version.parse('1.12'    )





	@property
	def 				_lowercase(						self					)    ->    Any:

								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 				_lowercase(						self					)    ->    int:
								return 1e-5





	@property
	def 				_lowercase(						self					)    ->    str:
								return 12






	def 				_lowercase(						self	,	A	,	A = -1	,	A = -1	,	A = False	,	A = None	,	A = 3	,	A = 40	,	A = 40	,	)    ->    Union[str, Any]:
								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  :	Any							  =	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  :	Optional[int]							  =	processor.tokenizer.num_special_tokens_to_add(A					)
								UpperCAmelCase  :	Dict							  =	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  :	Union[str, Any]							  =	[[""" """.join([processor.tokenizer.unk_token]					) * seq_length]] * batch_size

								# Generate dummy bounding boxes
								UpperCAmelCase  :	List[str]							  =	[[[48, 84, 73, 128]]] * 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  :	Tuple							  =	self._generate_dummy_images(A	,	A	,	A	,	A					)

								UpperCAmelCase  :	Optional[Any]							  =	dict(
								    processor(
								        A	,	text=A	,	boxes=A	,	return_tensors=A	,	)					)

								return inputs






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def    __lowerCamelCase						(      _lowercase			,       _lowercase = True			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = math.inf			,       _lowercase = -math.inf			,       _lowercase = False			,       _lowercase = 1_0_0			,       _lowercase = 0.01			,       _lowercase = 1			,       )   ->    Any:
							UpperCAmelCase  :	Optional[int]							  =	False
							UpperCAmelCase  :	Any							  =	search_prob
							UpperCAmelCase  :	Any							  =	start_temperate
							UpperCAmelCase  :	Optional[Any]							  =	[]
							UpperCAmelCase  :	Optional[Any]							  =	0
							UpperCAmelCase  :	Optional[Any]							  =	None

							while not search_end:
														UpperCAmelCase  :	List[str]							  =	current_state.score()
														if best_state is None or current_score > best_state.score():
																					UpperCAmelCase  :	List[Any]							  =	current_state
														scores.append(_lowercase	)
														iterations += 1
														UpperCAmelCase  :	Dict							  =	None
														UpperCAmelCase  :	Union[str, Any]							  =	current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					UpperCAmelCase  :	int							  =	random.randint(0			,       len(_lowercase	) - 1	)  # picking a random neighbor
																					UpperCAmelCase  :	int							  =	neighbors.pop(_lowercase	)
																					UpperCAmelCase  :	Tuple							  =	picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												UpperCAmelCase  :	Union[str, Any]							  =	change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												UpperCAmelCase  :	int							  =	picked_neighbor
																					else:
																												UpperCAmelCase  :	Optional[Any]							  =	(math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			UpperCAmelCase  :	Optional[int]							  =	picked_neighbor
														UpperCAmelCase  :	List[Any]							  =	current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					UpperCAmelCase  :	Optional[int]							  =	True
														else:
																					UpperCAmelCase  :	Optional[int]							  =	next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(_lowercase	)			,       _lowercase	)
														plt.xlabel("""Iterations"""	)
														plt.ylabel("""Function values"""	)
														plt.show()
							return best_state


if __name__ == "__main__":



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    str:
														return (x**2) + (y**2)

							# starting the problem with initial coordinates (12, 47)
							a     :    Dict      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)

							# starting the problem with initial coordinates (12, 47)
							a     :    List[str]      						=					SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
							)
							print(
							    """The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
							    F'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
							)



							def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Optional[Any]:
														return (3 * x**2) - (6 * y)

							a     :    Tuple      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Any      						=					simulated_annealing(prob, find_max=False, visualization=True)
							print(
							    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

							a     :    List[Any]      						=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							a     :    Dict      						=					simulated_annealing(prob, find_max=True, visualization=True)


							print(
							    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
							    F'''{local_min.score()}'''
							)

