


"""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 						_A  (  nn.Module  ):







    """simple docstring"""




    def __init__( self    :   Optional[int]    ,			__UpperCAmelCase    :   Optional[int]):
      super().__init__()
      a						:		List[str]											=      torchvision.models.resnetaaa(pretrained=__UpperCAmelCase)
      a						:		str											=      list(model.children())[:-2]
      a						:		Tuple											=      nn.Sequential(*__UpperCAmelCase)
      a						:		Optional[int]											=      nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds])






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      a						:		str											=      self.pool(self.model(__UpperCAmelCase))
      a						:		Any											=      torch.flatten(__UpperCAmelCase    ,			start_dim=2)
      a						:		Union[str, Any]											=      out.transpose(1    ,			2).contiguous()
      return out  # BxNx2048






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		Dict											=      [json.loads(__UpperCAmelCase) for l in open(__UpperCAmelCase)]
      a						:		Union[str, Any]											=      os.path.dirname(__UpperCAmelCase)
      a						:		Dict											=      tokenizer
      a						:		Dict											=      labels
      a						:		Any											=      len(__UpperCAmelCase)
      a						:		Tuple											=      max_seq_length

      a						:		Union[str, Any]											=      transforms






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






    def __getitem__( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   int):
      a						:		Optional[int]											=      torch.LongTensor(self.tokenizer.encode(self.data[index]["text"]    ,			add_special_tokens=__UpperCAmelCase))
      a						:		str											=      sentence[0], sentence[1:-1], sentence[-1]
      a						:		Tuple											=      sentence[: self.max_seq_length]

      a						:		Any											=      torch.zeros(self.n_classes)
      a						:		int											=      1

      a						:		Any											=      Image.open(os.path.join(self.data_dir    ,			self.data[index]["img"])).convert("RGB")
      a						:		Optional[int]											=      self.transforms(__UpperCAmelCase)

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






    def      __snake_case							( self    :   Optional[Any]):
      a						:		Union[str, Any]											=      Counter()
      for row in self.data:
        label_freqs.update(row["label"])
      return label_freqs






def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		int											=      [len(row["sentence"]	) for row in batch]
  a						:		Tuple											=      len(A_	), max(A_	)

  a						:		Union[str, Any]											=      torch.zeros(A_  ,       A_  ,       dtype=torch.long	)
  a						:		Any											=      torch.zeros(A_  ,       A_  ,       dtype=torch.long	)

  for i_batch, (input_row, length) in enumerate(zip(A_  ,       A_	)	):
    a						:		int											=      input_row["sentence"]
    a						:		Union[str, Any]											=      1

  a						:		str											=      torch.stack([row["image"] for row in batch]	)
  a						:		List[str]											=      torch.stack([row["label"] for row in batch]	)
  a						:		Union[str, Any]											=      torch.stack([row["image_start_token"] for row in batch]	)
  a						:		Any											=      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      (    )->				Optional[Any]:
  '''simple docstring'''







  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      (    )->				List[str]:
  '''simple docstring'''







  return transforms.Compose(
      [
          transforms.Resize(256	),
          transforms.CenterCrop(224	),
          transforms.ToTensor(),
          transforms.Normalize(
              mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]  ,       std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]  ,       ),
      ]	)










"""simple docstring"""







import sys
import turtle


def 					lowercase      (    A_  ,       A_	)->				tuple[float, float]:
  '''simple docstring'''







  return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       )->				None:
  '''simple docstring'''







  my_pen.up()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.down()
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)
  my_pen.goto(vertexa[0]  ,       vertexa[1]	)

  if depth == 0:
    return

  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)
  triangle(A_  ,       get_mid(A_  ,       A_	)  ,       get_mid(A_  ,       A_	)  ,       depth - 1	)


if __name__ == "__main__":
 if len(sys.argv) != 2:
  raise ValueError(
      """Correct format for using this script: """
      """python fractals.py <int:depth_for_fractal>"""
  )
 __lowercase    =				turtle.Turtle()
 my_pen.ht()
 my_pen.speed(5)
 my_pen.pencolor("""red""")

 __lowercase    =				[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
 triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))






