



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


a_      :      Tuple    =     logging.get_logger(__name__)

a_      :      Union[str, Any]    =     {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Any      						=					'''layoutlmv3'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE=5_0_2_6_5		,				SCREAMING_SNAKE_CASE=7_6_8		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=1_2		,				SCREAMING_SNAKE_CASE=3_0_7_2		,				SCREAMING_SNAKE_CASE="gelu"		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=0.1		,				SCREAMING_SNAKE_CASE=5_1_2		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=0.02		,				SCREAMING_SNAKE_CASE=1e-5		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=0		,				SCREAMING_SNAKE_CASE=2		,				SCREAMING_SNAKE_CASE=1_0_2_4		,				SCREAMING_SNAKE_CASE=1_2_8		,				SCREAMING_SNAKE_CASE=1_2_8		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=3_2		,				SCREAMING_SNAKE_CASE=1_2_8		,				SCREAMING_SNAKE_CASE=6_4		,				SCREAMING_SNAKE_CASE=2_5_6		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=2_2_4		,				SCREAMING_SNAKE_CASE=3		,				SCREAMING_SNAKE_CASE=1_6		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE		,				) ->			Optional[int]:
  super().__init__(
      vocab_size=SCREAMING_SNAKE_CASE		,				hidden_size=SCREAMING_SNAKE_CASE		,				num_hidden_layers=SCREAMING_SNAKE_CASE		,				num_attention_heads=SCREAMING_SNAKE_CASE		,				intermediate_size=SCREAMING_SNAKE_CASE		,				hidden_act=SCREAMING_SNAKE_CASE		,				hidden_dropout_prob=SCREAMING_SNAKE_CASE		,				attention_probs_dropout_prob=SCREAMING_SNAKE_CASE		,				max_position_embeddings=SCREAMING_SNAKE_CASE		,				type_vocab_size=SCREAMING_SNAKE_CASE		,				initializer_range=SCREAMING_SNAKE_CASE		,				layer_norm_eps=SCREAMING_SNAKE_CASE		,				pad_token_id=SCREAMING_SNAKE_CASE		,				bos_token_id=SCREAMING_SNAKE_CASE		,				eos_token_id=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE		,				)
  a__												= max_ad_position_embeddings
  a__												= coordinate_size
  a__												= shape_size
  a__												= has_relative_attention_bias
  a__												= rel_pos_bins
  a__												= max_rel_pos
  a__												= has_spatial_attention_bias
  a__												= rel_ad_pos_bins
  a__												= max_rel_ad_pos
  a__												= text_embed
  a__												= visual_embed
  a__												= input_size
  a__												= num_channels
  a__												= patch_size
  a__												= classifier_dropout





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    int      						=					version.parse('''1.12'''				)

 @property
 def 						_UpperCAmelCase       (	self      ) ->			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 						_UpperCAmelCase       (	self      ) ->			float:
  return 1e-5

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return 1_2



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = -1		,				SCREAMING_SNAKE_CASE = -1		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = 3		,				SCREAMING_SNAKE_CASE = 4_0		,				SCREAMING_SNAKE_CASE = 4_0		,				) ->			Mapping[str, Any]:
  setattr(processor.image_processor		,				'''apply_ocr'''		,				SCREAMING_SNAKE_CASE      )

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

  # Generate dummy bounding boxes
  a__												= [[[4_8, 8_4, 7_3, 1_2_8]]] * 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__												= self._generate_dummy_images(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  a__												= dict(
      processor(
          SCREAMING_SNAKE_CASE		,				text=SCREAMING_SNAKE_CASE		,				boxes=SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				)      )

  return inputs










a_      :      str    =     6_55_21






def     __a					(		__UpperCAmelCase       ):
 a__												= 1
 a__												= 0
 for plain_chr in plain_text:
  a__												= (a + ord(__UpperCAmelCase       )) % MOD_ADLER
  a__												= (b + a) % MOD_ADLER
 return (b << 16) | a





