


"""simple docstring"""




import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


lowercase_			     =  {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class 					__lowerCAmelCase					(   nn.Module       ):


		'''simple docstring'''



		def __init__(  self							, _a      ):
				super().__init__()
				__a           =			torchvision.models.resnetaaa(pretrained=_a      )
				__a           =			list(model.children()      )[:-2]
				__a           =			nn.Sequential(*_a      )
				__a           =			nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]      )





		def 			__UpperCAmelCase       (  self							, _a      ):
				# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
				__a           =			self.pool(self.model(_a      )      )
				__a           =			torch.flatten(_a							, start_dim=2      )
				__a           =			out.transpose(1							, 2      ).contiguous()
				return out  # BxNx2048







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a							, _a							, _a							, _a      ):
				__a           =			[json.loads(_a      ) for l in open(_a      )]
				__a           =			os.path.dirname(_a      )
				__a           =			tokenizer
				__a           =			labels
				__a           =			len(_a      )
				__a           =			max_seq_length

				__a           =			transforms

		def __len__(  self      ):
				return len(self.data      )

		def __getitem__(  self							, _a      ):
				__a           =			torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']							, add_special_tokens=_a      )      )
				__a      ,     __a      ,     __a           =			sentence[0], sentence[1:-1], sentence[-1]
				__a           =			sentence[: self.max_seq_length]

				__a           =			torch.zeros(self.n_classes      )
				__a           =			1

				__a           =			Image.open(os.path.join(self.data_dir							, self.data[index]['''img''']      )      ).convert('''RGB'''      )
				__a           =			self.transforms(_a      )

				return {
				    "image_start_token": start_token,
				    "image_end_token": end_token,
				    "sentence": sentence,
				    "image": image,
				    "label": label,
				}





		def 			__UpperCAmelCase       (  self      ):
				__a           =			Counter()
				for row in self.data:
						label_freqs.update(row['''label''']      )
				return label_freqs







def        lowercase							(		lowerCAmelCase__ :     Any			) ->      List[str]:
		__a           =			[len(row['''sentence''']			) for row in batch]
		__a      ,     __a           =			len(lowerCAmelCase__			), max(lowerCAmelCase__			)

		__a           =			torch.zeros(lowerCAmelCase__    ,					lowerCAmelCase__    ,					dtype=torch.long			)
		__a           =			torch.zeros(lowerCAmelCase__    ,					lowerCAmelCase__    ,					dtype=torch.long			)

		for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__    ,					lowerCAmelCase__			)			):
				__a           =			input_row['''sentence''']
				__a           =			1

		__a           =			torch.stack([row['''image'''] for row in batch]			)
		__a           =			torch.stack([row['''label'''] for row in batch]			)
		__a           =			torch.stack([row['''image_start_token'''] for row in batch]			)
		__a           =			torch.stack([row['''image_end_token'''] for row in batch]			)

		return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor

def        lowercase							(		) ->      Tuple:
		return [
		    "Crime",
		    "Drama",
		    "Thriller",
		    "Action",
		    "Comedy",
		    "Romance",
		    "Documentary",
		    "Short",
		    "Mystery",
		    "History",
		    "Family",
		    "Adventure",
		    "Fantasy",
		    "Sci-Fi",
		    "Western",
		    "Horror",
		    "Sport",
		    "War",
		    "Music",
		    "Musical",
		    "Animation",
		    "Biography",
		    "Film-Noir",
		]




def        lowercase							(		) ->      Dict:
		return transforms.Compose(
		    [
		        transforms.Resize(256			),
		        transforms.CenterCrop(224			),
		        transforms.ToTensor(),
		        transforms.Normalize(
		            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]    ,					std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]    ,					),
		    ]			)







import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
      # Create universe of discourse in Python using linspace ()
      UpperCamelCase__											=					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).
      UpperCamelCase__											=					[0, 2_5, 5_0]
      UpperCamelCase__											=					[2_5, 5_0, 7_5]
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)
      UpperCamelCase__											=					fuzz.membership.trimf(X, abca)

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



