'''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__  :      int        =					logging.get_logger(__name__)

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







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''layoutlmv3'''






  def __init__(      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Tuple=5_0265  ,							UpperCAmelCase__			:       Union[str, Any]=768  ,							UpperCAmelCase__			:       List[Any]=12  ,							UpperCAmelCase__			:       Union[str, Any]=12  ,							UpperCAmelCase__			:       Optional[Any]=3072  ,							UpperCAmelCase__			:       Optional[Any]="gelu"  ,							UpperCAmelCase__			:       List[Any]=0.1  ,							UpperCAmelCase__			:       Any=0.1  ,							UpperCAmelCase__			:       Any=512  ,							UpperCAmelCase__			:       int=2  ,							UpperCAmelCase__			:       Dict=0.02  ,							UpperCAmelCase__			:       List[str]=1e-5  ,							UpperCAmelCase__			:       Any=1  ,							UpperCAmelCase__			:       Tuple=0  ,							UpperCAmelCase__			:       str=2  ,							UpperCAmelCase__			:       List[str]=1024  ,							UpperCAmelCase__			:       str=128  ,							UpperCAmelCase__			:       List[Any]=128  ,							UpperCAmelCase__			:       str=True  ,							UpperCAmelCase__			:       Union[str, Any]=32  ,							UpperCAmelCase__			:       List[Any]=128  ,							UpperCAmelCase__			:       str=64  ,							UpperCAmelCase__			:       Optional[Any]=256  ,							UpperCAmelCase__			:       Tuple=True  ,							UpperCAmelCase__			:       Optional[Any]=True  ,							UpperCAmelCase__			:       List[Any]=True  ,							UpperCAmelCase__			:       Union[str, Any]=224  ,							UpperCAmelCase__			:       Tuple=3  ,							UpperCAmelCase__			:       Optional[Any]=16  ,							UpperCAmelCase__			:       Union[str, Any]=None  ,							**UpperCAmelCase__			:       Any  ,							)				->Optional[Any]:
        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__  ,							)
        UpperCAmelCase_            =  max_ad_position_embeddings
        UpperCAmelCase_            =  coordinate_size
        UpperCAmelCase_            =  shape_size
        UpperCAmelCase_            =  has_relative_attention_bias
        UpperCAmelCase_            =  rel_pos_bins
        UpperCAmelCase_            =  max_rel_pos
        UpperCAmelCase_            =  has_spatial_attention_bias
        UpperCAmelCase_            =  rel_ad_pos_bins
        UpperCAmelCase_            =  max_rel_ad_pos
        UpperCAmelCase_            =  text_embed
        UpperCAmelCase_            =  visual_embed
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  patch_size
        UpperCAmelCase_            =  classifier_dropout







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	version.parse('''1.12'''					)






  @property
  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Mapping[str, Mapping[int, str]]:
        # 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  lowerCAmelCase__ (      self			:       List[Any]  )				->float:
        return 1e-5






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        return 12






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       "ProcessorMixin"  ,							UpperCAmelCase__			:       int = -1  ,							UpperCAmelCase__			:       int = -1  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       Optional["TensorType"] = None  ,							UpperCAmelCase__			:       int = 3  ,							UpperCAmelCase__			:       int = 40  ,							UpperCAmelCase__			:       int = 40  ,							)				->Mapping[str, Any]:
        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
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  processor.tokenizer.num_special_tokens_to_add(UpperCAmelCase__  )
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  [[''' '''.join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

        # Generate dummy bounding boxes
        UpperCAmelCase_            =  [[[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)
        UpperCAmelCase_            =  self._generate_dummy_images(UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  dict(
            processor(
                UpperCAmelCase__  ,							text=UpperCAmelCase__  ,							boxes=UpperCAmelCase__  ,							return_tensors=UpperCAmelCase__  ,							)  )

        return inputs






'''simple docstring'''
lowercase__  :      Union[str, Any]        =					"\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__  :      str        =					[{"type": "code", "content": INSTALL_CONTENT}]
lowercase__  :      Any        =					{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





