"""simple docstring"""


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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


a							=logging.get_logger(__name__)

a							={
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   int  							=       '''layoutlm'''

				def __init__(      self	, _lowerCamelCase=30522	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=2	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=0	, _lowerCamelCase="absolute"	, _lowerCamelCase=True	, _lowerCamelCase=1024	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, **_lowerCamelCase      )
										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=type_vocab_size
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=position_embedding_type
										lowerCamelCase__					=use_cache
										lowerCamelCase__					=max_ad_position_embeddings






class    __UpperCAmelCase						(	__lowerCAmelCase	):

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase = "default"	, _lowerCamelCase = None	, ):
										super().__init__(_lowerCamelCase	, task=_lowerCamelCase	, patching_specs=_lowerCamelCase      )
										lowerCamelCase__					=config.max_ad_position_embeddings - 1

				@property
				def 		_a	(      self      ):
										return OrderedDict(
										    [
										        ("input_ids", {0: "batch", 1: "sequence"}),
										        ("bbox", {0: "batch", 1: "sequence"}),
										        ("attention_mask", {0: "batch", 1: "sequence"}),
										        ("token_type_ids", {0: "batch", 1: "sequence"}),
										    ]      )






				def 		_a	(      self	, _lowerCamelCase	, _lowerCamelCase = -1	, _lowerCamelCase = -1	, _lowerCamelCase = False	, _lowerCamelCase = None	, ):
										lowerCamelCase__					=super().generate_dummy_inputs(
										    _lowerCamelCase	, batch_size=_lowerCamelCase	, seq_length=_lowerCamelCase	, is_pair=_lowerCamelCase	, framework=_lowerCamelCase      )

										# Generate a dummy bbox
										lowerCamelCase__					=[48, 84, 73, 128]

										if not framework == TensorType.PYTORCH:
																raise NotImplementedError("Exporting LayoutLM to ONNX is currently only supported for PyTorch."      )

										if not is_torch_available():
																raise ValueError("Cannot generate dummy inputs without PyTorch installed."      )
										import torch

										lowerCamelCase__					,      lowerCamelCase__					=input_dict["input_ids"].shape
										lowerCamelCase__					=torch.tensor([*[box] * seq_length]      ).tile(_lowerCamelCase	, 1	, 1      )
										return input_dict







from __future__ import annotations

from pathlib import Path







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



						'''simple docstring'''






						return pointa[0] * pointa[1] - pointa[1] * pointa[0]







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       bool:



						'''simple docstring'''






						UpperCAmelCase_     							=     (xa, ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     (xa - xa, ya - ya)
						UpperCAmelCase_     							=     -vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)
						UpperCAmelCase_     							=     +vector_product(__UpperCAmelCase							,					__UpperCAmelCase	) / vector_product(
						    __UpperCAmelCase							,					__UpperCAmelCase	)

						return a > 0 and b > 0 and a + b < 1







def 				A			(		__UpperCAmelCase = "p102_triangles.txt"	)       ->       int:



						'''simple docstring'''






						UpperCAmelCase_     							=     Path(__UpperCAmelCase	).parent.joinpath(__UpperCAmelCase	).read_text(encoding='''utf-8'''	)

						UpperCAmelCase_     							=     []
						for line in data.strip().split('''\n'''	):
												triangles.append([int(__UpperCAmelCase	) for number in line.split(''','''	)]	)

						UpperCAmelCase_     							=     0
						UpperCAmelCase_     							=     42

						for triangle in triangles:
												ret += contains_origin(*__UpperCAmelCase	)

						return ret


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





