

"""simple docstring"""


import operator as op






def     a__    (    __SCREAMING_SNAKE_CASE )	->	Tuple:
		__lowerCAmelCase:       Any          =			[]
		__lowerCAmelCase:       int          =			lambda __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE : int(x / y )  # noqa: E731 integer division operation
		__lowerCAmelCase:       Tuple          =			{
		    "^": op.pow,
		    "*": op.mul,
		    "/": div,
		    "+": op.add,
		    "-": op.sub,
		}  # operators & their respective operation

		# print table header
		print("Symbol".center(8 )					, "Action".center(1_2 )					, "Stack"					, sep=" | " )
		print("-" * (3_0 + len(UpperCamelCase__ )) )

		for x in post_fix:
				if x.isdigit():  # if x in digit
						stack.append(UpperCamelCase__ )  # append x to stack
						# output in tabular format
						print(x.rjust(8 )					, ("push(" + x + ")").ljust(1_2 )					, ",".join(UpperCamelCase__ )					, sep=" | " )
				else:
						__lowerCAmelCase:       Any          =			stack.pop()  # pop stack
						# output in tabular format
						print("".rjust(8 )					, ("pop(" + b + ")").ljust(1_2 )					, ",".join(UpperCamelCase__ )					, sep=" | " )

						__lowerCAmelCase:       Union[str, Any]          =			stack.pop()  # pop stack
						# output in tabular format
						print("".rjust(8 )					, ("pop(" + a + ")").ljust(1_2 )					, ",".join(UpperCamelCase__ )					, sep=" | " )

						stack.append(
						    str(opr[x](int(UpperCamelCase__ )					, int(UpperCamelCase__ ) ) ) )  # evaluate the 2 values popped from stack & push result to stack
						# output in tabular format
						print(
						    x.rjust(8 )					, ("push(" + a + x + b + ")").ljust(1_2 )					, ",".join(UpperCamelCase__ )					, sep=" | "					, )

		return int(stack[0] )


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









from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A				:    int       =   logging.get_logger(__name__)

__A				:    Tuple       =   {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 	A_       (a_			):
	UpperCAmelCase__					=			'''big_bird'''
	def __init__(    self							,					_A=5_0_3_5_8							,					_A=7_6_8							,					_A=1_2							,					_A=1_2							,					_A=3_0_7_2							,					_A="gelu_new"							,					_A=0.1							,					_A=0.1							,					_A=4_0_9_6							,					_A=2							,					_A=0.02							,					_A=1E-12							,					_A=True							,					_A=0							,					_A=1							,					_A=2							,					_A=6_6							,					_A="block_sparse"							,					_A=True							,					_A=False							,					_A=6_4							,					_A=3							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    pad_token_id=_A							,					bos_token_id=_A							,					eos_token_id=_A							,					sep_token_id=_A							,					**_A							,					)

				UpperCAmelCase   		=				vocab_size
				UpperCAmelCase   		=				max_position_embeddings
				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   		=				initializer_range
				UpperCAmelCase   		=				type_vocab_size
				UpperCAmelCase   		=				layer_norm_eps
				UpperCAmelCase   		=				use_cache

				UpperCAmelCase   		=				rescale_embeddings
				UpperCAmelCase   		=				attention_type
				UpperCAmelCase   		=				use_bias
				UpperCAmelCase   		=				block_size
				UpperCAmelCase   		=				num_random_blocks
				UpperCAmelCase   		=				classifier_dropout

class 	A_       (a_			):
	@property
	def      _lowercase				(    self		):






				'''simple docstring'''







				if self.task == "multiple-choice":
							UpperCAmelCase   		=				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCAmelCase   		=				{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]		)



