





'''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


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Optional[Any]						=    {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''layoutlmv3'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-5						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase=1024						,	_UpperCAmelCase=128						,	_UpperCAmelCase=128						,	_UpperCAmelCase=True						,	_UpperCAmelCase=32						,	_UpperCAmelCase=128						,	_UpperCAmelCase=64						,	_UpperCAmelCase=256						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=True						,	_UpperCAmelCase=224						,	_UpperCAmelCase=3						,	_UpperCAmelCase=16						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(
              vocab_size=_UpperCAmelCase						,	hidden_size=_UpperCAmelCase						,	num_hidden_layers=_UpperCAmelCase						,	num_attention_heads=_UpperCAmelCase						,	intermediate_size=_UpperCAmelCase						,	hidden_act=_UpperCAmelCase						,	hidden_dropout_prob=_UpperCAmelCase						,	attention_probs_dropout_prob=_UpperCAmelCase						,	max_position_embeddings=_UpperCAmelCase						,	type_vocab_size=_UpperCAmelCase						,	initializer_range=_UpperCAmelCase						,	layer_norm_eps=_UpperCAmelCase						,	pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          __A						: Optional[Any]      =       max_ad_position_embeddings
          __A						: str      =       coordinate_size
          __A						: Tuple      =       shape_size
          __A						: List[str]      =       has_relative_attention_bias
          __A						: Dict      =       rel_pos_bins
          __A						: Dict      =       max_rel_pos
          __A						: Union[str, Any]      =       has_spatial_attention_bias
          __A						: Any      =       rel_ad_pos_bins
          __A						: Dict      =       max_rel_ad_pos
          __A						: Union[str, Any]      =       text_embed
          __A						: Union[str, Any]      =       visual_embed
          __A						: List[Any]      =       input_size
          __A						: Tuple      =       num_channels
          __A						: int      =       patch_size
          __A						: Optional[int]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       version.parse('''1.12'''							)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''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):

          '''simple docstring'''






          return 1e-5


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return 12


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = -1						,	_UpperCAmelCase = -1						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 3						,	_UpperCAmelCase = 40						,	_UpperCAmelCase = 40						,	):

          '''simple docstring'''






          setattr(processor.image_processor						,	'apply_ocr'						,	_UpperCAmelCase)

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

          # Generate dummy bounding boxes
          __A						: Any      =       [[[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)
          __A						: List[Any]      =       self._generate_dummy_images(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase)

          __A						: Any      =       dict(
              processor(
                  _UpperCAmelCase						,	text=_UpperCAmelCase						,	boxes=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	))

          return inputs






'''simple docstring'''




from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray






def        _lowerCAmelCase			(  __snake_case  : float ,							__snake_case  : float ,							__snake_case  : bool = False			)     ->     list[float]:
     if radian_mode:
          return [magnitude * cos(__snake_case			), magnitude * sin(__snake_case			)]
     return [magnitude * cos(radians(__snake_case			)			), magnitude * sin(radians(__snake_case			)			)]






def        _lowerCAmelCase			(  __snake_case  : NDArray[floataa] ,							__snake_case  : NDArray[floataa] ,							__snake_case  : float = 10**-1			)     ->     bool:
     __A						: NDArray[floataa]      =       cross(__snake_case ,							__snake_case			)
     __A						: float      =       sum(__snake_case			)
     return abs(__snake_case			) < eps


if __name__ == "__main__":
  # Test to check if it works
  lowercase__    :      List[Any]						=    array(
      [
          polar_force(718.4, 1_80 - 30),
          polar_force(879.54, 45),
          polar_force(1_00, -90),
      ]
  )

  lowercase__    :      NDArray[floataa]						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem 1 in image_data/2D_problems.jpg
  lowercase__    :      Dict						=    array(
      [
          polar_force(30 * 9.81, 15),
          polar_force(2_15, 1_80 - 45),
          polar_force(2_64, 90 - 30),
      ]
  )

  lowercase__    :      Any						=    array([[0, 0], [0, 0], [0, 0]])

  assert in_static_equilibrium(forces, location)

  # Problem in image_data/2D_problems_1.jpg
  lowercase__    :      Optional[int]						=    array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

  lowercase__    :      Optional[int]						=    array([[0, 0], [6, 0], [10, 0], [12, 0]])

  assert in_static_equilibrium(forces, location)

  import doctest

  doctest.testmod()