


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							:					Any     						=     logging.get_logger(__name__)

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






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[str]										=	"""layoutlmv3"""


					def __init__(						self	:		str     ,    SCREAMING_SNAKE_CASE	:		Dict=5_0_2_6_5     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		str=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=1_2     ,    SCREAMING_SNAKE_CASE	:		int=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		Dict=1e-5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1     ,    SCREAMING_SNAKE_CASE	:		Dict=0     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2     ,    SCREAMING_SNAKE_CASE	:		Any=1_0_2_4     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2_8     ,    SCREAMING_SNAKE_CASE	:		int=1_2_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3_2     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_2_8     ,    SCREAMING_SNAKE_CASE	:		Dict=6_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=True     ,    SCREAMING_SNAKE_CASE	:		Dict=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		List[str]=3     ,    SCREAMING_SNAKE_CASE	:		List[str]=1_6     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    **SCREAMING_SNAKE_CASE	:		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_     ,    )
											__snake_case				=		max_ad_position_embeddings
											__snake_case				=		coordinate_size
											__snake_case				=		shape_size
											__snake_case				=		has_relative_attention_bias
											__snake_case				=		rel_pos_bins
											__snake_case				=		max_rel_pos
											__snake_case				=		has_spatial_attention_bias
											__snake_case				=		rel_ad_pos_bins
											__snake_case				=		max_rel_ad_pos
											__snake_case				=		text_embed
											__snake_case				=		visual_embed
											__snake_case				=		input_size
											__snake_case				=		num_channels
											__snake_case				=		patch_size
											__snake_case				=		classifier_dropout






class     UpperCamelCase(  _a   ):
					snake_case_		:				Tuple										=	version.parse("""1.12"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	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	:		Tuple						)			->	float:




											'''simple docstring'''

											return 1e-5


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

											return 1_2






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any] = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = False     ,    SCREAMING_SNAKE_CASE	:		int = None     ,    SCREAMING_SNAKE_CASE	:		List[Any] = 3     ,    SCREAMING_SNAKE_CASE	:		int = 4_0     ,    SCREAMING_SNAKE_CASE	:		Tuple = 4_0     ,    )			->	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
											__snake_case				=		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
											__snake_case				=		processor.tokenizer.num_special_tokens_to_add(snake_case_						)
											__snake_case				=		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
											__snake_case				=		[[" ".join([processor.tokenizer.unk_token]						) * seq_length]] * batch_size

											# Generate dummy bounding boxes
											__snake_case				=		[[[4_8, 8_4, 7_3, 1_2_8]]] * 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)
											__snake_case				=		self._generate_dummy_images(snake_case_     ,    snake_case_     ,    snake_case_     ,    snake_case_						)

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

											return inputs










from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





