'''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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

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



     def __init__(					self				,				A_=None				,				A_=None				,				**A_						) ->				Tuple:






           """simple docstring"""
           if "feature_extractor" in kwargs:
                 warnings.warn(
                     '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                     ''' instead.'''				,				A_				,				)
                 _lowerCamelCase			    =				kwargs.pop('''feature_extractor'''						)

           _lowerCamelCase			    =				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__(A_				,				A_						)



     def __call__(					self				,				A_				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = True				,				A_ = False				,				A_ = None				,				A_ = None				,				A_ = 0				,				A_ = None				,				A_ = None				,				A_ = None				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = False				,				A_ = True				,				A_ = None				,				**A_				,				) ->				BatchEncoding:






           """simple docstring"""
           # 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
           _lowerCamelCase			    =				self.image_processor(images=A_				,				return_tensors=A_						)

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

           _lowerCamelCase			    =				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=A_				,				add_special_tokens=A_				,				padding=A_				,				truncation=A_				,				max_length=A_				,				stride=A_				,				pad_to_multiple_of=A_				,				return_token_type_ids=A_				,				return_attention_mask=A_				,				return_overflowing_tokens=A_				,				return_special_tokens_mask=A_				,				return_offsets_mapping=A_				,				return_length=A_				,				verbose=A_				,				return_tensors=A_				,				**A_				,				)

           # add pixel values
           _lowerCamelCase			    =				features.pop('''pixel_values'''						)
           if return_overflowing_tokens is True:
                 _lowerCamelCase			    =				self.get_overflowing_images(A_				,				encoded_inputs['''overflow_to_sample_mapping''']						)
           _lowerCamelCase			    =				images

           return encoded_inputs



     def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           for sample_idx in overflow_to_sample_mapping:
                 images_with_overflow.append(images[sample_idx]						)

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

           return images_with_overflow



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				Any:






           """simple docstring"""
           return self.tokenizer.batch_decode(*A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				*A_				,				**A_						) ->				Any:






           """simple docstring"""
           return self.tokenizer.decode(*A_				,				**A_						)



     @property
     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           return ["input_ids", "bbox", "attention_mask", "image"]



     @property
     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,				A_				,				)
           return self.image_processor_class





     @property
     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,				A_				,				)
           return self.image_processor
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
     # Create universe of discourse in Python using linspace ()
     snake_case__				= np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

     # Create two fuzzy sets by defining any membership function
     # (trapmf(), gbellmf(), gaussmf(), etc).
     snake_case__				= [0, 25, 50]
     snake_case__				= [25, 50, 75]
     snake_case__				= fuzz.membership.trimf(X, abca)
     snake_case__				= fuzz.membership.trimf(X, abca)

     # Compute the different operations using inbuilt functions.
     snake_case__				= np.ones(75)
     snake_case__				= np.zeros((75,))
     # 1. Union = max(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_or(X, young, X, middle_aged)[1]
     # 2. Intersection = min(µA(x), µB(x))
     snake_case__				= fuzz.fuzzy_and(X, young, X, middle_aged)[1]
     # 3. Complement (A) = (1- min(µA(x))
     snake_case__				= fuzz.fuzzy_not(young)
     # 4. Difference (A/B) = min(µA(x),(1- µB(x)))
     snake_case__				= fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
     # 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
     snake_case__				= young + middle_aged - (young * middle_aged)
     # 6. Algebraic Product = (µA(x) * µB(x))
     snake_case__				= young * middle_aged
     # 7. Bounded Sum = min[1,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
     # 8. Bounded difference = min[0,(µA(x), µB(x))]
     snake_case__				= fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

     # max-min composition
     # max-product composition

     # Plot each set A, set B and each operation result using plot() and subplot().
     from matplotlib import pyplot as plt

     plt.figure()

     plt.subplot(4, 3, 1)
     plt.plot(X, young)
     plt.title('Young')
     plt.grid(True)

     plt.subplot(4, 3, 2)
     plt.plot(X, middle_aged)
     plt.title('Middle aged')
     plt.grid(True)

     plt.subplot(4, 3, 3)
     plt.plot(X, union)
     plt.title('union')
     plt.grid(True)

     plt.subplot(4, 3, 4)
     plt.plot(X, intersection)
     plt.title('intersection')
     plt.grid(True)

     plt.subplot(4, 3, 5)
     plt.plot(X, complement_a)
     plt.title('complement_a')
     plt.grid(True)

     plt.subplot(4, 3, 6)
     plt.plot(X, difference)
     plt.title('difference a/b')
     plt.grid(True)

     plt.subplot(4, 3, 7)
     plt.plot(X, alg_sum)
     plt.title('alg_sum')
     plt.grid(True)

     plt.subplot(4, 3, 8)
     plt.plot(X, alg_product)
     plt.title('alg_product')
     plt.grid(True)

     plt.subplot(4, 3, 9)
     plt.plot(X, bdd_sum)
     plt.title('bdd_sum')
     plt.grid(True)

     plt.subplot(4, 3, 10)
     plt.plot(X, bdd_difference)
     plt.title('bdd_difference')
     plt.grid(True)

     plt.subplots_adjust(hspace=0.5)
     plt.show()