

"""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


SCREAMING_SNAKE_CASE_     :			Tuple	  =			logging.get_logger(__name__)

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


class 		_A       (			__a					):
      __a								=	'layoutlmv3'





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=50265    ,						SCREAMING_SNAKE_CASE__=768    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=12    ,						SCREAMING_SNAKE_CASE__=3072    ,						SCREAMING_SNAKE_CASE__="gelu"    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=0.1    ,						SCREAMING_SNAKE_CASE__=512    ,						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__=1024    ,						SCREAMING_SNAKE_CASE__=128    ,						SCREAMING_SNAKE_CASE__=128    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=32    ,						SCREAMING_SNAKE_CASE__=128    ,						SCREAMING_SNAKE_CASE__=64    ,						SCREAMING_SNAKE_CASE__=256    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=True    ,						SCREAMING_SNAKE_CASE__=224    ,						SCREAMING_SNAKE_CASE__=3    ,						SCREAMING_SNAKE_CASE__=16    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__    ,						)   ->					Union[str, Any]:
         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__    ,						)
         lowerCamelCase__						=			max_ad_position_embeddings
         lowerCamelCase__						=			coordinate_size
         lowerCamelCase__						=			shape_size
         lowerCamelCase__						=			has_relative_attention_bias
         lowerCamelCase__						=			rel_pos_bins
         lowerCamelCase__						=			max_rel_pos
         lowerCamelCase__						=			has_spatial_attention_bias
         lowerCamelCase__						=			rel_ad_pos_bins
         lowerCamelCase__						=			max_rel_ad_pos
         lowerCamelCase__						=			text_embed
         lowerCamelCase__						=			visual_embed
         lowerCamelCase__						=			input_size
         lowerCamelCase__						=			num_channels
         lowerCamelCase__						=			patch_size
         lowerCamelCase__						=			classifier_dropout






class 		_A       (			__a					):
      __a								=	version.parse('1.12'					)





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





      @property
      def 					_lowerCamelCase  (  self		)   ->					int:
         return 12





      def 					_lowerCamelCase  (  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__ = 40    ,						SCREAMING_SNAKE_CASE__ = 40    ,						)   ->					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
         lowerCamelCase__						=			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
         lowerCamelCase__						=			processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			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
         lowerCamelCase__						=			[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         lowerCamelCase__						=			[[[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__						=			self._generate_dummy_images(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			dict(
             processor(
                 SCREAMING_SNAKE_CASE__    ,						text=SCREAMING_SNAKE_CASE__    ,						boxes=SCREAMING_SNAKE_CASE__    ,						return_tensors=SCREAMING_SNAKE_CASE__    ,						)		)

         return inputs







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": op.pow,
       "*": op.mul,
       "/": div,
       "+": op.add,
       "-": op.sub,
   }  # operators & their respective operation

   # print table header
   print("Symbol".center(8   )	,    "Action".center(12   )	,    "Stack"	,    sep=" | "   )
   print("-" * (30 + len(A__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(A__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )	,    ("push(" + x + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )
      else:
         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         stack.append(
             str(opr[x](int(A__   )	,    int(A__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )	,    ("push(" + a + x + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "	,    )

   return int(stack[0]   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
     print('''\n\tResult = ''', solve(Postfix))




