
import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
						# Create universe of discourse in Python using linspace ()
						__a  			=					np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)

						# Create two fuzzy sets by defining any membership function
						# (trapmf(), gbellmf(), gaussmf(), etc).
						__a  			=					[0, 2_5, 5_0]
						__a  			=					[2_5, 5_0, 7_5]
						__a  			=					fuzz.membership.trimf(X, abca)
						__a  			=					fuzz.membership.trimf(X, abca)

						# Compute the different operations using inbuilt functions.
						__a  			=					np.ones(7_5)
						__a  			=					np.zeros((7_5,))
						# 1. Union = max(µA(x), µB(x))
						__a  			=					fuzz.fuzzy_or(X, young, X, middle_aged)[1]
						# 2. Intersection = min(µA(x), µB(x))
						__a  			=					fuzz.fuzzy_and(X, young, X, middle_aged)[1]
						# 3. Complement (A) = (1- min(µA(x))
						__a  			=					fuzz.fuzzy_not(young)
						# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
						__a  			=					fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
						# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
						__a  			=					young + middle_aged - (young * middle_aged)
						# 6. Algebraic Product = (µA(x) * µB(x))
						__a  			=					young * middle_aged
						# 7. Bounded Sum = min[1,(µA(x), µB(x))]
						__a  			=					fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
						# 8. Bounded difference = min[0,(µA(x), µB(x))]
						__a  			=					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, 1_0)
						plt.plot(X, bdd_difference)
						plt.title('bdd_difference')
						plt.grid(True)

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










"""simple docstring"""






import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			lowerCamelCase__      =			["""image_processor""", """tokenizer"""]
			lowerCamelCase__      =			"""BlipImageProcessor"""
			lowerCamelCase__      =			"""AutoTokenizer"""
			def __init__(  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							super().__init__(lowercase  ,      lowercase  )

							# add QFormer tokenizer
							_lowerCamelCase     :			int    =    qformer_tokenizer
			def __call__(  self  ,      lowercase = None  ,      lowercase = None  ,      lowercase = True  ,      lowercase = False  ,      lowercase = None  ,      lowercase = None  ,      lowercase = 0  ,      lowercase = None  ,      lowercase = None  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = False  ,      lowercase = True  ,      lowercase = None  ,      **lowercase  ,      ):
							if images is None and text is None:
											raise ValueError('You have to specify at least images or text.'  )

							_lowerCamelCase     :			int    =    BatchFeature()

							if text is not None:
											_lowerCamelCase     :			List[str]    =    self.tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											encoding.update(lowercase  )
											_lowerCamelCase     :			List[str]    =    self.qformer_tokenizer(
											    text=lowercase  ,      add_special_tokens=lowercase  ,      padding=lowercase  ,      truncation=lowercase  ,      max_length=lowercase  ,      stride=lowercase  ,      pad_to_multiple_of=lowercase  ,      return_attention_mask=lowercase  ,      return_overflowing_tokens=lowercase  ,      return_special_tokens_mask=lowercase  ,      return_offsets_mapping=lowercase  ,      return_token_type_ids=lowercase  ,      return_length=lowercase  ,      verbose=lowercase  ,      return_tensors=lowercase  ,      **lowercase  ,      )
											_lowerCamelCase     :			List[Any]    =    qformer_text_encoding.pop('input_ids'  )
											_lowerCamelCase     :			Tuple    =    qformer_text_encoding.pop('attention_mask'  )

							if images is not None:
											_lowerCamelCase     :			int    =    self.image_processor(lowercase  ,      return_tensors=lowercase  )
											encoding.update(lowercase  )

							return encoding
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.batch_decode(*lowercase  ,      **lowercase  )
			def 					A_    (  self  ,      *lowercase  ,      **lowercase  ):
							return self.tokenizer.decode(*lowercase  ,      **lowercase  )
			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.tokenizer.model_input_names
							_lowerCamelCase     :			Any    =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )
			def 					A_    (  self  ,      lowercase  ,      **lowercase  ):
							if os.path.isfile(lowercase  ):
											raise ValueError(F'''Provided path ({save_directory}) should be a directory, not a file'''  )
							os.makedirs(lowercase  ,      exist_ok=lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    os.path.join(lowercase  ,      'qformer_tokenizer'  )
							self.qformer_tokenizer.save_pretrained(lowercase  )
							return super().save_pretrained(lowercase  ,      **lowercase  )






			@classmethod
			def 					A_    (  cls  ,      lowercase  ,      **lowercase  ):
							_lowerCamelCase     :			Union[str, Any]    =    AutoTokenizer.from_pretrained(lowercase  ,      subfolder='qformer_tokenizer'  )
							_lowerCamelCase     :			Dict    =    cls._get_arguments_from_pretrained(lowercase  ,      **lowercase  )
							args.append(lowercase  )
							return cls(*lowercase  )