


"""simple docstring"""
from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore





class   __A  (__lowerCamelCase):





							'''simple docstring'''

							__lowercase:		Any				   =     c_float



							@property
							def 		lowerCAmelCase							(					self				:   Dict							)       ->str:



														"""simple docstring"""



														return sizeof(self.value							)







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE = 0					)     ->					int:
							# < 0 is possible if using a dynamic axis
							if dimension <= 0:
														snake_case_     							=		fixed_dimension

							dimension -= num_token_to_add
							return dimension







def     _a					(   _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					)     ->					int:
							return num_parameters * dtype.size







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

							try:
														return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE__					)
							except (ValueError, OSError, KeyError):
														snake_case_     							=		None, None
														try:
																					snake_case_     							=		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
														except (OSError, KeyError):
																					pass
														try:
																					snake_case_     							=		AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__					)
														except (OSError, KeyError):
																					pass

														if tokenizer is not None and feature_extractor is not None:
																					raise ValueError(
																					    f"""Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""					)
														elif tokenizer is None and feature_extractor is None:
																					return None
														elif tokenizer is not None:
																					return tokenizer
														else:
																					return feature_extractor










from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE__   ,   -1)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  if not balanced_parentheses(SCREAMING_SNAKE_CASE__):
    raise ValueError("""Mismatched parentheses""")
  __snake_case:		Stack[str]					    =		Stack()
  __snake_case:		Any					    =		[]
  for char in expression_str:
    if char.isalpha() or char.isdigit():
      postfix.append(SCREAMING_SNAKE_CASE__)
    elif char == "(":
      stack.push(SCREAMING_SNAKE_CASE__)
    elif char == ")":
      while not stack.is_empty() and stack.peek() != "(":
        postfix.append(stack.pop())
      stack.pop()
    else:
      while not stack.is_empty() and precedence(SCREAMING_SNAKE_CASE__) <= precedence(stack.peek()):
        postfix.append(stack.pop())
      stack.push(SCREAMING_SNAKE_CASE__)
  while not stack.is_empty():
    postfix.append(stack.pop())
  return " ".join(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      __UpperCAmelCase			:  Optional[int]										=				"a+b*(c^d-e)^(f+g*h)-i"

      print("Infix to Postfix Notation demonstration:\n")
      print("Infix notation: " + expression)
      print("Postfix notation: " + infix_to_postfix(expression))






