

from __future__ import annotations



def    __lowerCAmelCase   (    A							,  A     ):
	if len(_snake_case     ) < k or k < 0:
		raise ValueError("Invalid Input"     )
	UpperCAmelCase_												=      UpperCAmelCase_												=      sum(array[:k]     )
	for i in range(len(_snake_case     ) - k     ):
		UpperCAmelCase_												=      current_sum - array[i] + array[i + k]
		UpperCAmelCase_												=      max(_snake_case							,  _snake_case     )
	return max_sum


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

				testmod()
				_a:     List[Any]							=      [randint(-1000, 1000) for i in range(100)]
				_a:     Optional[Any]							=      randint(0, 110)
				print(F'The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}')







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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					='''layoutlmv3'''





       def __init__(				self	:Optional[int],      snake_case	:int=5_0265,      snake_case	:int=768,      snake_case	:Any=12,      snake_case	:Union[str, Any]=12,      snake_case	:List[str]=3072,      snake_case	:List[str]="gelu",      snake_case	:List[str]=0.1,      snake_case	:Optional[int]=0.1,      snake_case	:Optional[int]=512,      snake_case	:Tuple=2,      snake_case	:Optional[Any]=0.0_2,      snake_case	:Optional[int]=1e-5,      snake_case	:Union[str, Any]=1,      snake_case	:Dict=0,      snake_case	:Tuple=2,      snake_case	:Tuple=1024,      snake_case	:Optional[Any]=128,      snake_case	:Optional[Any]=128,      snake_case	:List[str]=True,      snake_case	:str=32,      snake_case	:Optional[int]=128,      snake_case	:Dict=64,      snake_case	:List[str]=256,      snake_case	:Optional[Any]=True,      snake_case	:Optional[Any]=True,      snake_case	:Dict=True,      snake_case	:Optional[Any]=224,      snake_case	:int=3,      snake_case	:int=16,      snake_case	:str=None,      **snake_case	:Dict,      ):



          """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,      )
          _lowercase              =max_ad_position_embeddings
          _lowercase              =coordinate_size
          _lowercase              =shape_size
          _lowercase              =has_relative_attention_bias
          _lowercase              =rel_pos_bins
          _lowercase              =max_rel_pos
          _lowercase              =has_spatial_attention_bias
          _lowercase              =rel_ad_pos_bins
          _lowercase              =max_rel_ad_pos
          _lowercase              =text_embed
          _lowercase              =visual_embed
          _lowercase              =input_size
          _lowercase              =num_channels
          _lowercase              =patch_size
          _lowercase              =classifier_dropout






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=version.parse('''1.12''' )





       @property
       def 				UpperCamelCase__						(				self	:Any):



          """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 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 1e-5





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 12





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:"ProcessorMixin",      snake_case	:int = -1,      snake_case	:int = -1,      snake_case	:bool = False,      snake_case	:Optional["TensorType"] = None,      snake_case	:int = 3,      snake_case	:int = 40,      snake_case	:int = 40,      ):



          """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
          _lowercase              =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
          _lowercase              =processor.tokenizer.num_special_tokens_to_add(snake_case)
          _lowercase              =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
          _lowercase              =[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          _lowercase              =[[[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)
          _lowercase              =self._generate_dummy_images(snake_case,      snake_case,      snake_case,      snake_case)

          _lowercase              =dict(
              processor(
                  snake_case,      text=snake_case,      boxes=snake_case,      return_tensors=snake_case,      ))

          return inputs


