
"""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


UpperCAmelCase__        =					logging.get_logger(__name__)

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

class  lowerCAmelCase__							(		_lowercase      ):
     __a         =   '''layoutlmv3'''







     def __init__(				self     :     Any       , _lowerCamelCase     :     str=50265       , _lowerCamelCase     :     List[Any]=768       , _lowerCamelCase     :     List[str]=12       , _lowerCamelCase     :     int=12       , _lowerCamelCase     :     Optional[Any]=3072       , _lowerCamelCase     :     Tuple="gelu"       , _lowerCamelCase     :     Optional[int]=0.1       , _lowerCamelCase     :     str=0.1       , _lowerCamelCase     :     int=512       , _lowerCamelCase     :     Dict=2       , _lowerCamelCase     :     Optional[Any]=0.0_2       , _lowerCamelCase     :     Optional[Any]=1e-5       , _lowerCamelCase     :     Union[str, Any]=1       , _lowerCamelCase     :     str=0       , _lowerCamelCase     :     Any=2       , _lowerCamelCase     :     List[Any]=1024       , _lowerCamelCase     :     List[str]=128       , _lowerCamelCase     :     List[str]=128       , _lowerCamelCase     :     Optional[int]=True       , _lowerCamelCase     :     int=32       , _lowerCamelCase     :     Any=128       , _lowerCamelCase     :     Any=64       , _lowerCamelCase     :     str=256       , _lowerCamelCase     :     Tuple=True       , _lowerCamelCase     :     Union[str, Any]=True       , _lowerCamelCase     :     Dict=True       , _lowerCamelCase     :     Tuple=224       , _lowerCamelCase     :     Union[str, Any]=3       , _lowerCamelCase     :     List[Any]=16       , _lowerCamelCase     :     Dict=None       , **_lowerCamelCase     :     Tuple       , ):
       super().__init__(
           vocab_size=_lowerCamelCase       , hidden_size=_lowerCamelCase       , num_hidden_layers=_lowerCamelCase       , num_attention_heads=_lowerCamelCase       , intermediate_size=_lowerCamelCase       , hidden_act=_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       , pad_token_id=_lowerCamelCase       , bos_token_id=_lowerCamelCase       , eos_token_id=_lowerCamelCase       , **_lowerCamelCase       , )
       _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  lowerCAmelCase__							(		_lowercase      ):
     __a         =   version.parse("""1.12"""      )







     @property
     def 	lowercase      (				self     :     List[Any]     ):
       # The order of inputs is different for question answering and sequence classification
       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 	lowercase      (				self     :     Tuple     ):
       return 1e-5







     @property
     def 	lowercase      (				self     :     Tuple     ):
       return 12







     def 	lowercase      (				self     :     Optional[int]       , _lowerCamelCase     :     List[Any]       , _lowerCamelCase     :     Dict = -1       , _lowerCamelCase     :     str = -1       , _lowerCamelCase     :     Dict = False       , _lowerCamelCase     :     int = None       , _lowerCamelCase     :     Union[str, Any] = 3       , _lowerCamelCase     :     Optional[Any] = 40       , _lowerCamelCase     :     List[str] = 40       , ):
       setattr(processor.image_processor       , '''apply_ocr'''       , _lowerCamelCase     )

       # 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(
           _lowerCamelCase       , 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(_lowerCamelCase     )
       _snake_case   	=			compute_effective_axis_dimension(
           _lowerCamelCase       , fixed_dimension=OnnxConfig.default_fixed_sequence       , num_token_to_add=_lowerCamelCase     )
       # 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   	=			[[[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)
       _snake_case   	=			self._generate_dummy_images(_lowerCamelCase       , _lowerCamelCase       , _lowerCamelCase       , _lowerCamelCase     )

       _snake_case   	=			dict(
           processor(
               _lowerCamelCase       , text=_lowerCamelCase       , boxes=_lowerCamelCase       , return_tensors=_lowerCamelCase       , )     )

       return inputs








import requests
from bsa import BeautifulSoup






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= BeautifulSoup(requests.get(__UpperCAmelCase							,  params=__UpperCAmelCase       ).content							,  '''html.parser'''       )
 a__												= soup.find('''div'''							,  attrs={'''class''': '''gs_ri'''}       )
 a__												= div.find('''div'''							,  attrs={'''class''': '''gs_fl'''}       ).find_all('''a'''       )
 return anchors[2].get_text()


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         'title': (
             'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
             'capacitance, volumetric capacitance, and energy density'
         ),
         'journal': 'Chem. Mater.',
         'volume': 30,
         'pages': '3979-3990',
         'year': 20_18,
         'hl': 'en',
     }
     print(get_citation('https://scholar.google.com/scholar_lookup', params=params))





