




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


a													=			{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       _A				(		nn.Module     ):






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






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




class       _A				(		__lowercase     ):






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

        _UpperCAmelCase      =			transforms






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






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

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

        _UpperCAmelCase      =			Image.open(os.path.join(self.data_dir						,		self.data[index]["""img"""]			)			).convert("""RGB"""			)
        _UpperCAmelCase      =			self.transforms(_SCREAMING_SNAKE_CASE			)

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






  def 				UpperCAmelCase (    self			):
        _UpperCAmelCase      =			Counter()
        for row in self.data:
              label_freqs.update(row["""label"""]			)
        return label_freqs




def        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Dict:
      _UpperCAmelCase      =			[len(row["""sentence"""]       ) for row in batch]
      _UpperCAmelCase    ,					_UpperCAmelCase      =			len(snake_case       ), max(snake_case       )

      _UpperCAmelCase      =			torch.zeros(snake_case			,     snake_case			,     dtype=torch.long       )
      _UpperCAmelCase      =			torch.zeros(snake_case			,     snake_case			,     dtype=torch.long       )

      for i_batch, (input_row, length) in enumerate(zip(snake_case			,     snake_case       )       ):
            _UpperCAmelCase      =			input_row["""sentence"""]
            _UpperCAmelCase      =			1

      _UpperCAmelCase      =			torch.stack([row["""image"""] for row in batch]       )
      _UpperCAmelCase      =			torch.stack([row["""label"""] for row in batch]       )
      _UpperCAmelCase      =			torch.stack([row["""image_start_token"""] for row in batch]       )
      _UpperCAmelCase      =			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        _SCREAMING_SNAKE_CASE      (		)						->				str:
      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        _SCREAMING_SNAKE_CASE      (		)						->				Optional[int]:
      return transforms.Compose(
          [
              transforms.Resize(2_5_6       ),
              transforms.CenterCrop(2_2_4       ),
              transforms.ToTensor(),
              transforms.Normalize(
                  mean=[0.46777044, 0.44531429, 0.40661017]			,     std=[0.12221994, 0.12145835, 0.14380469]			,     ),
          ]       )





from __future__ import annotations

from scipy.special import comb  # type: ignore




class       _A				:






		def __init__(    self						,		_SCREAMING_SNAKE_CASE			):
								_UpperCAmelCase      =			list_of_points
								# Degree determines the flexibility of the curve.
								# Degree = 1 will produce a straight line.
								_UpperCAmelCase      =			len(_SCREAMING_SNAKE_CASE			) - 1






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."
								_UpperCAmelCase      =			[]
								for i in range(len(self.list_of_points			)			):
														# basis function for each i
														output_values.append(
														    comb(self.degree						,		_SCREAMING_SNAKE_CASE			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
								# the basis must sum up to 1 for it to produce a valid Bezier curve.
								assert round(sum(_SCREAMING_SNAKE_CASE			)						,		5			) == 1
								return output_values






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								assert 0 <= t <= 1, "Time t must be between 0 and 1."

								_UpperCAmelCase      =			self.basis_function(_SCREAMING_SNAKE_CASE			)
								_UpperCAmelCase      =			0.0
								_UpperCAmelCase      =			0.0
								for i in range(len(self.list_of_points			)			):
														# For all points, sum up the product of i-th basis function and i-th point.
														x += basis_function[i] * self.list_of_points[i][0]
														y += basis_function[i] * self.list_of_points[i][1]
								return (x, y)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE = 0.01			):
								from matplotlib import pyplot as plt  # type: ignore

								_UpperCAmelCase      =			[]  # x coordinates of points to plot
								_UpperCAmelCase      =			[]  # y coordinates of points to plot

								_UpperCAmelCase      =			0.0
								while t <= 1:
														_UpperCAmelCase      =			self.bezier_curve_function(_SCREAMING_SNAKE_CASE			)
														to_plot_x.append(value[0]			)
														to_plot_y.append(value[1]			)
														t += step_size

								_UpperCAmelCase      =			[i[0] for i in self.list_of_points]
								_UpperCAmelCase      =			[i[1] for i in self.list_of_points]

								plt.plot(
								    _SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""blue"""						,		label="""Curve of Degree """ + str(self.degree			)						,		)
								plt.scatter(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE						,		color="""red"""						,		label="""Control Points"""			)
								plt.legend()
								plt.show()


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
				BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
				BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3