


'''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


lowerCAmelCase    :     Any					=						logging.get_logger(__name__)

lowerCAmelCase    :     Dict					=						{
    """microsoft/layoutlmv3-base""": """https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json""",
}
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "layoutlmv3"







     def __init__( self      ,	snake_case__=5_0265      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=512      ,	snake_case__=2      ,	snake_case__=0.02      ,	snake_case__=1E-5      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	snake_case__=1024      ,	snake_case__=128      ,	snake_case__=128      ,	snake_case__=True      ,	snake_case__=32      ,	snake_case__=128      ,	snake_case__=64      ,	snake_case__=256      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=224      ,	snake_case__=3      ,	snake_case__=16      ,	snake_case__=None      ,	**snake_case__      ,	):

            '''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__      ,	)
            _lowerCAmelCase    :							str   			=       max_ad_position_embeddings
            _lowerCAmelCase    :							Dict   			=       coordinate_size
            _lowerCAmelCase    :							str   			=       shape_size
            _lowerCAmelCase    :							Optional[Any]   			=       has_relative_attention_bias
            _lowerCAmelCase    :							Optional[Any]   			=       rel_pos_bins
            _lowerCAmelCase    :							Any   			=       max_rel_pos
            _lowerCAmelCase    :							Dict   			=       has_spatial_attention_bias
            _lowerCAmelCase    :							Any   			=       rel_ad_pos_bins
            _lowerCAmelCase    :							Union[str, Any]   			=       max_rel_ad_pos
            _lowerCAmelCase    :							Optional[int]   			=       text_embed
            _lowerCAmelCase    :							Dict   			=       visual_embed
            _lowerCAmelCase    :							List[Any]   			=       input_size
            _lowerCAmelCase    :							Tuple   			=       num_channels
            _lowerCAmelCase    :							str   			=       patch_size
            _lowerCAmelCase    :							int   			=       classifier_dropout
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    version.parse("1.12"	)







     @property
     def a	( self   ):

            '''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 a	( self   ):

            '''simple docstring'''

            return 1E-5







     @property
     def a	( self   ):

            '''simple docstring'''

            return 12







     def a	( self      ,	snake_case__      ,	snake_case__ = -1      ,	snake_case__ = -1      ,	snake_case__ = False      ,	snake_case__ = None      ,	snake_case__ = 3      ,	snake_case__ = 40      ,	snake_case__ = 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
            _lowerCAmelCase    :							Any   			=       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
            _lowerCAmelCase    :							List[Any]   			=       processor.tokenizer.num_special_tokens_to_add(snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       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
            _lowerCAmelCase    :							List[str]   			=       [[' '.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

            # Generate dummy bounding boxes
            _lowerCAmelCase    :							int   			=       [[[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)
            _lowerCAmelCase    :							Optional[Any]   			=       self._generate_dummy_images(snake_case__      ,	snake_case__      ,	snake_case__      ,	snake_case__   )

            _lowerCAmelCase    :							int   			=       dict(
                processor(
                    snake_case__      ,	text=snake_case__      ,	boxes=snake_case__      ,	return_tensors=snake_case__      ,	)   )

            return inputs





'''simple docstring'''







lowerCAmelCase    :     Optional[int]					=						[sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_00_00)]






def 			lowercase				(_A					):







       """simple docstring"""







       _lowerCAmelCase    :							str   			=       0
       while number:
              # Increased Speed Slightly by checking every 5 digits together.
              sum_of_digits_squared += DIGITS_SQUARED[number % 1_0_0_0_0_0]
              number //= 1_0_0_0_0_0

       return sum_of_digits_squared


# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.

# The other one ends with 1 and has only one element 1.

# So 58 and 1 are chosen to be declared at the starting.

# Changed dictionary to an array to quicken the solution
lowerCAmelCase    :     list[bool | None]					=						[None] * 10_00_00_00
lowerCAmelCase    :     List[str]					=						True
lowerCAmelCase    :     Union[str, Any]					=						False






def 			lowercase				(_A					):







       """simple docstring"""







       if CHAINS[number - 1] is not None:
              return CHAINS[number - 1]  # type: ignore

       _lowerCAmelCase    :							Any   			=       chain(next_number(_A					)					)
       _lowerCAmelCase    :							List[str]   			=       number_chain

       while number < 1_0_0_0_0_0_0_0:
              _lowerCAmelCase    :							Tuple   			=       number_chain
              number *= 1_0

       return number_chain






def 			lowercase				(_A = 1_0_0_0_0_0_0_0					):







       """simple docstring"""







       for i in range(1   ,						_A					):
              if CHAINS[i] is None:
                     chain(i + 1					)

       return CHAINS[:number].count(_A					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(F'''{solution() = }''')

