



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     :							Any  							=				logging.get_logger(__name__)

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





class  __magic_name__   (							_a						):
		lowercase      :       List[str]          ="""layoutlmv3"""






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







								super().__init__(
								    vocab_size=snake_case_				,		hidden_size=snake_case_				,		num_hidden_layers=snake_case_				,		num_attention_heads=snake_case_				,		intermediate_size=snake_case_				,		hidden_act=snake_case_				,		hidden_dropout_prob=snake_case_				,		attention_probs_dropout_prob=snake_case_				,		max_position_embeddings=snake_case_				,		type_vocab_size=snake_case_				,		initializer_range=snake_case_				,		layer_norm_eps=snake_case_				,		pad_token_id=snake_case_				,		bos_token_id=snake_case_				,		eos_token_id=snake_case_				,		**snake_case_				,		)
								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  __magic_name__   (							_a						):
		lowercase      :       Tuple          =version.parse('''1.12'''						)






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     int      )				->					Mapping[str, Mapping[int, str]]:
								'''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      :     List[str]      )				->					float:
								'''simple docstring'''







								return 1e-5






		@property
		def SCREAMING_SNAKE_CASE_			(						self      :     List[Any]      )				->					int:
								'''simple docstring'''







								return 12







		def SCREAMING_SNAKE_CASE_			(						self      :     Tuple				,		UpperCamelCase__      :     Optional[Any]				,		UpperCamelCase__      :     Union[str, Any] = -1				,		UpperCamelCase__      :     Dict = -1				,		UpperCamelCase__      :     Optional[int] = False				,		UpperCamelCase__      :     Optional[int] = None				,		UpperCamelCase__      :     str = 3				,		UpperCamelCase__      :     str = 40				,		UpperCamelCase__      :     Any = 40				,		)				->					Mapping[str, Any]:
								'''simple docstring'''







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

								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								UpperCAmelCase         =     compute_effective_axis_dimension(
								    snake_case_				,		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(snake_case_      )
								UpperCAmelCase         =     compute_effective_axis_dimension(
								    snake_case_				,		fixed_dimension=OnnxConfig.default_fixed_sequence				,		num_token_to_add=snake_case_      )
								# 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(snake_case_				,		snake_case_				,		snake_case_				,		snake_case_      )

								UpperCAmelCase         =     dict(
								    processor(
								        snake_case_				,		text=snake_case_				,		boxes=snake_case_				,		return_tensors=snake_case_				,		)      )

								return inputs








from __future__ import annotations

def    lowerCamelCase_(lowerCamelCase_    ) ->       int:
						UpperCAmelCase         =     len(lowerCamelCase_    ) // 2

						# choose the middle 3 elements
						UpperCAmelCase         =     lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]    ) == 2:
																		m -= 1
												return peak(lst[m:]    )

						# decreasing
						else:
												if len(lst[:m]    ) == 2:
																		m += 1
												return peak(lst[:m]    )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



