






"""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


A_    =							logging.get_logger(__name__)

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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
       snake_case_     =							'layoutlmv3'


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






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
       snake_case_     =							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      :						str							):
             '''simple docstring'''





             return 1e-5


       @property
       def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
             '''simple docstring'''





             return 12


       def 						_UpperCamelCase       (			self      :						List[str]			,    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
             A__						:  Dict		  =  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
             A__						:  Any		  =  processor.tokenizer.num_special_tokens_to_add(snake_case							)
             A__						:  Dict		  =  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
             A__						:  Any		  =  [[""" """.join([processor.tokenizer.unk_token]							) * seq_length]] * batch_size

             # Generate dummy bounding boxes
             A__						:  Optional[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)
             A__						:  Optional[Any]		  =  self._generate_dummy_images(snake_case			,    snake_case			,    snake_case			,    snake_case							)

             A__						:  Any		  =  dict(
                 processor(
                     snake_case			,    text=snake_case			,    boxes=snake_case			,    return_tensors=snake_case			,    )							)

             return inputs














"""simple docstring"""



from __future__ import annotations

A_    =							10




def      _lowerCAmelCase		(							UpperCAmelCase__       :					list[int]      )		->list[int]:
						A__						:  Any		  =  1
						A__						:  Optional[int]		  =  max(UpperCAmelCase__      )
						while placement <= max_digit:
												# declare and initialize empty buckets
												A__						:  list[list]		  =  [[] for _ in range(UpperCAmelCase__      )]
												# split list_of_ints between the buckets
												for i in list_of_ints:
																		A__						:  List[Any]		  =  int((i / placement) % RADIX      )
																		buckets[tmp].append(UpperCAmelCase__      )
												# put each buckets' contents into list_of_ints
												A__						:  Union[str, Any]		  =  0
												for b in range(UpperCAmelCase__      ):
																		for i in buckets[b]:
																								A__						:  str		  =  i
																								a += 1
        # move to next
												placement *= RADIX
						return list_of_ints


if __name__ == "__main__":
						import doctest

						doctest.testmod()






