





"""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


SCREAMING_SNAKE_CASE__ =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__ =       {
    "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 		lowercase ( _UpperCAmelCase ):
      _SCREAMING_SNAKE_CASE         =				'layoutlm'





      def __init__(	self				,						lowercase=30_522				,						lowercase=768				,						lowercase=12				,						lowercase=12				,						lowercase=3_072				,						lowercase="gelu"				,						lowercase=0.1				,						lowercase=0.1				,						lowercase=512				,						lowercase=2				,						lowercase=0.02				,						lowercase=1e-12				,						lowercase=0				,						lowercase="absolute"				,						lowercase=True				,						lowercase=1_024				,						**lowercase				,						)		->					List[str]:
            super().__init__(pad_token_id=lowercase				,						**lowercase			)
            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 		lowercase ( _UpperCAmelCase ):





      def __init__(	self				,						lowercase				,						lowercase = "default"				,						lowercase = None				,						)		->					int:
            super().__init__(lowercase				,						task=lowercase				,						patching_specs=lowercase			)
            lowerCAmelCase  						=	config.max_ad_position_embeddings - 1





      @property
      def    _snake_case      (	self			)		->					Mapping[str, Mapping[int, str]]:
            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    _snake_case      (	self				,						lowercase				,						lowercase = -1				,						lowercase = -1				,						lowercase = False				,						lowercase = None				,						)		->					Mapping[str, Any]:
            lowerCAmelCase  						=	super().generate_dummy_inputs(
                lowercase				,						batch_size=lowercase				,						seq_length=lowercase				,						is_pair=lowercase				,						framework=lowercase			)

            # 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(lowercase				,						1				,						1			)
            return input_dict













"""simple docstring"""





from scipy.constants import g


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						float							,						SCREAMING_SNAKE_CASE					:						float      ):







      '''simple docstring'''




      if mass < 0:
            # handling of negative values of mass
            raise ValueError("""The mass of a body cannot be negative"""      )
      if height < 0:
            # handling of negative values of height
            raise ValueError("""The height above the ground cannot be negative"""      )
      return mass * g * height


if __name__ == "__main__":
       from doctest import testmod

       testmod(name="potential_energy")






