





"""simple docstring"""






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__lowercase     (     __A							):







					'''simple docstring'''




					__lowerCAmelCase					=					['image_processor', 'tokenizer']
					__lowerCAmelCase					=					'LayoutLMv2ImageProcessor'
					__lowerCAmelCase					=					('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')







					def __init__(  self		,     _UpperCAmelCase=None		,     _UpperCAmelCase=None		,     **_UpperCAmelCase       ):
									if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''		,     __lowercase		,     )
													__a	:  str         =					kwargs.pop('''feature_extractor'''       )

									__a	:  Union[str, Any]         =					image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
													raise ValueError('''You need to specify an `image_processor`.'''       )
									if tokenizer is None:
													raise ValueError('''You need to specify a `tokenizer`.'''       )

									super().__init__(__lowercase		,     __lowercase       )







					def __call__(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = True		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = 0		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = None		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = False		,     _UpperCAmelCase = True		,     _UpperCAmelCase = None		,     **_UpperCAmelCase		,     ):
									# verify input
									if self.image_processor.apply_ocr and (boxes is not None):
													raise ValueError(
													    '''You cannot provide bounding boxes '''
													    '''if you initialized the image processor with apply_ocr set to True.'''       )

									if self.image_processor.apply_ocr and (word_labels is not None):
													raise ValueError(
													    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''       )

									if return_overflowing_tokens is True and return_offsets_mapping is False:
													raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''       )

									# first, apply the image processor
									__a	:  List[Any]         =					self.image_processor(images=__lowercase		,     return_tensors=__lowercase       )

									# second, apply the tokenizer
									if text is not None and self.image_processor.apply_ocr and text_pair is None:
													if isinstance(__lowercase		,     __lowercase       ):
																	__a	:  Optional[Any]         =					[text]  # add batch dimension (as the image processor always adds a batch dimension)
													__a	:  Optional[int]         =					features['''words''']

									__a	:  str         =					self.tokenizer(
									    text=text if text is not None else features['''words''']		,     text_pair=text_pair if text_pair is not None else None		,     boxes=boxes if boxes is not None else features['''boxes''']		,     word_labels=__lowercase		,     add_special_tokens=__lowercase		,     padding=__lowercase		,     truncation=__lowercase		,     max_length=__lowercase		,     stride=__lowercase		,     pad_to_multiple_of=__lowercase		,     return_token_type_ids=__lowercase		,     return_attention_mask=__lowercase		,     return_overflowing_tokens=__lowercase		,     return_special_tokens_mask=__lowercase		,     return_offsets_mapping=__lowercase		,     return_length=__lowercase		,     verbose=__lowercase		,     return_tensors=__lowercase		,     **__lowercase		,     )

									# add pixel values
									__a	:  Dict         =					features.pop('''pixel_values'''       )
									if return_overflowing_tokens is True:
													__a	:  List[Any]         =					self.get_overflowing_images(__lowercase		,     encoded_inputs['''overflow_to_sample_mapping''']       )
									__a	:  Tuple         =					images

									return encoded_inputs







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
									__a	:  List[str]         =					[]
									for sample_idx in overflow_to_sample_mapping:
													images_with_overflow.append(images[sample_idx]       )

									if len(__lowercase       ) != len(__lowercase       ):
													raise ValueError(
													    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
													    f""" {len(__lowercase       )} and {len(__lowercase       )}"""       )

									return images_with_overflow







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.batch_decode(*__lowercase		,     **__lowercase       )







					def 	_lowerCamelCase						(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									return self.tokenizer.decode(*__lowercase		,     **__lowercase       )







					@property
					def 	_lowerCamelCase						(  self       ):
									return ["input_ids", "bbox", "attention_mask", "image"]







					@property
					def 	_lowerCamelCase						(  self       ):
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     __lowercase		,     )
									return self.image_processor_class







					@property
					def 	_lowerCamelCase						(  self       ):
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''		,     __lowercase		,     )
									return self.image_processor






"""simple docstring"""






def       __A (     a_  :int)						->						Union[str, Any]:
				__a	:  int         =					[]
				__a	:  Dict         =					[]
				__a	:  str         =					{
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				__a	:  Tuple         =					len(a_) if (len(a_) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8)				,    '''Stack'''.center(a_)				,    '''Postfix'''.center(a_)				,    sep=''' | '''				,    )
				print('''-''' * (print_width * 3 + 7))

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(a_)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(a_)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(a_) == 0:
																stack.append(a_)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(a_) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop())  # pop stack & add to Postfix
																stack.append(a_)  # push x to stack

								print(
								    x.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				while len(a_) > 0:  # while stack is not empty
								post_fix.append(stack.pop())  # pop stack & add to Postfix
								print(
								    ''' '''.center(8)				,    (''''''.join(a_)).ljust(a_)				,    (''''''.join(a_)).ljust(a_)				,    sep=''' | '''				,    )  # Output in tabular format

				return "".join(a_)  # return Postfix as str

def       __A (     a_  :int)						->						List[Any]:
				__a	:  Dict         =					list(infix[::-1])  # reverse the infix equation

				for i in range(len(a_)):
								if infix[i] == "(":
												__a	:  Union[str, Any]         =					''')'''  # change "(" to ")"
								elif infix[i] == ")":
												__a	:  List[str]         =					'''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(a_)))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		A	     =					input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
		A	     =					''''''.join(Infix.split())  # Remove spaces from the input
		print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')