


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_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						"layoutlmv3"



 def __init__(			self	,   _UpperCAmelCase=5_02_65	,   _UpperCAmelCase=7_68	,   _UpperCAmelCase=12	,   _UpperCAmelCase=12	,   _UpperCAmelCase=30_72	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=5_12	,   _UpperCAmelCase=2	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=1E-5	,   _UpperCAmelCase=1	,   _UpperCAmelCase=0	,   _UpperCAmelCase=2	,   _UpperCAmelCase=10_24	,   _UpperCAmelCase=1_28	,   _UpperCAmelCase=1_28	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=1_28	,   _UpperCAmelCase=64	,   _UpperCAmelCase=2_56	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=2_24	,   _UpperCAmelCase=3	,   _UpperCAmelCase=16	,   _UpperCAmelCase=None	,   **_UpperCAmelCase	,   ):
       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	,   )
       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_		(				lowerCamelCase__ ):

 '''simple docstring'''

 __snake_case				=						version.parse("1.12" )



 @property
 def 		UpperCamelCase__  (			self      ):
       # 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      ):
       return 1E-5



 @property
 def 		UpperCamelCase__  (			self      ):
       return 12





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase = -1	,   _UpperCAmelCase = -1	,   _UpperCAmelCase = False	,   _UpperCAmelCase = None	,   _UpperCAmelCase = 3	,   _UpperCAmelCase = 40	,   _UpperCAmelCase = 40	,   ):
       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
       snake_case_ =   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
       snake_case_ =   processor.tokenizer.num_special_tokens_to_add(_UpperCAmelCase      )
       snake_case_ =   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
       snake_case_ =   [[''' '''.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

       # Generate dummy bounding boxes
       snake_case_ =   [[[48, 84, 73, 1_28]]] * 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(_UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      )

       snake_case_ =   dict(
           processor(
               _UpperCAmelCase	,   text=_UpperCAmelCase	,   boxes=_UpperCAmelCase	,   return_tensors=_UpperCAmelCase	,   )      )

       return inputs



def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE = 6008_5147_5143      )->						int:




      """simple docstring"""

      try:
            snake_case_ =   int(SCREAMING_SNAKE_CASE      )
      except (TypeError, ValueError):
            raise TypeError('''Parameter n must be int or castable to int.'''      )
      if n <= 0:
            raise ValueError('''Parameter n must be greater than or equal to one.'''      )
      snake_case_ =   1
      snake_case_ =   2
      while i * i <= n:
            while n % i == 0:
                  snake_case_ =   i
                  n //= i
            i += 1
      if n > 1:
            snake_case_ =   n
      return int(SCREAMING_SNAKE_CASE      )


if __name__ == "__main__":
   print(f'''{solution() = }''')