



"""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


_UpperCAmelCase     =			{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 :					Union[str, Any]       ,		lowerCAmelCase :					Dict		)     ->					Any:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[Any]     			=torchvision.models.resnetaaa(pretrained=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=list(model.children()		)[:-2]
     SCREAMING_SNAKE_CASE_: Optional[int]     			=nn.Sequential(*lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]		)





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					str		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.pool(self.model(lowerCAmelCase		)		)
     SCREAMING_SNAKE_CASE_: Any     			=torch.flatten(lowerCAmelCase       ,		start_dim=2		)
     SCREAMING_SNAKE_CASE_: int     			=out.transpose(1       ,		2		).contiguous()
     return out  # BxNx2048







class 					a	(  UpperCAmelCase__       ):



  def __init__(						self :					str       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Tuple       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					List[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=[json.loads(lowerCAmelCase		) for l in open(lowerCAmelCase		)]
     SCREAMING_SNAKE_CASE_: Optional[int]     			=os.path.dirname(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=tokenizer
     SCREAMING_SNAKE_CASE_: Any     			=labels
     SCREAMING_SNAKE_CASE_: Any     			=len(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=max_seq_length

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=transforms



  def __len__(						self :					Union[str, Any]		)     ->					int:


     '''simple docstring'''




     return len(self.data		)



  def __getitem__(						self :					int       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]       ,		add_special_tokens=lowerCAmelCase		)		)
     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=sentence[0], sentence[1:-1], sentence[-1]
     SCREAMING_SNAKE_CASE_: int     			=sentence[: self.max_seq_length]

     SCREAMING_SNAKE_CASE_: Tuple     			=torch.zeros(self.n_classes		)
     SCREAMING_SNAKE_CASE_: str     			=1

     SCREAMING_SNAKE_CASE_: int     			=Image.open(os.path.join(self.data_dir       ,		self.data[index]["""img"""]		)		).convert("""RGB"""		)
     SCREAMING_SNAKE_CASE_: int     			=self.transforms(lowerCAmelCase		)

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





  def 	lowerCamelCase__       (						self :					Any		)     ->					List[Any]:


     '''simple docstring'''




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







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=[len(row["""sentence"""] ) for row in batch]
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=len(lowercase ), max(lowercase )

   SCREAMING_SNAKE_CASE_: Tuple     			=torch.zeros(lowercase							,			lowercase							,			dtype=torch.long )
   SCREAMING_SNAKE_CASE_: Tuple     			=torch.zeros(lowercase							,			lowercase							,			dtype=torch.long )

   for i_batch, (input_row, length) in enumerate(zip(lowercase							,			lowercase ) ):
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=input_row["""sentence"""]
      SCREAMING_SNAKE_CASE_: List[Any]     			=1

   SCREAMING_SNAKE_CASE_: Any     			=torch.stack([row["""image"""] for row in batch] )
   SCREAMING_SNAKE_CASE_: Tuple     			=torch.stack([row["""label"""] for row in batch] )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.stack([row["""image_start_token"""] for row in batch] )
   SCREAMING_SNAKE_CASE_: Optional[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      __magic_name__				(							):
   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      __magic_name__				(							):
   return transforms.Compose(
       [
           transforms.Resize(256 ),
           transforms.CenterCrop(224 ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]							,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]							,			),
       ] )








"""simple docstring"""
from math import pi





def      __magic_name__				(							lowercase							,			lowercase ):
   return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
   print(arc_length(9_0, 1_0))



