




'''simple docstring'''



from math import factorial







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     str  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				real
      if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[1] * rank
      else:
         _SCREAMING_SNAKE_CASE	=				rank



   def __repr__(   self :     List[Any]   ):

      """simple docstring"""
      return (
          F"""{self.real}+"""
          F"""{"+".join(str(__lowerCamelCase   )+"E"+str(n+1   )for n,dual in enumerate(self.duals   )   )}"""
      )



   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				self.duals.copy()
      while cur[-1] == 0:
         cur.pop(-1   )
      return Dual(self.real  ,				__lowerCamelCase   )



   def __add__(   self :     int  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      if not isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         return Dual(self.real + other  ,				self.duals   )
      _SCREAMING_SNAKE_CASE	=				self.duals.copy()
      _SCREAMING_SNAKE_CASE	=				other.duals.copy()
      if len(__lowerCamelCase   ) > len(__lowerCamelCase   ):
         o_dual.extend([1] * (len(__lowerCamelCase   ) - len(__lowerCamelCase   ))   )
      elif len(__lowerCamelCase   ) < len(__lowerCamelCase   ):
         s_dual.extend([1] * (len(__lowerCamelCase   ) - len(__lowerCamelCase   ))   )
      _SCREAMING_SNAKE_CASE	=				[]
      for i in range(len(__lowerCamelCase   )   ):
         new_duals.append(s_dual[i] + o_dual[i]   )
      return Dual(self.real + other.real  ,				__lowerCamelCase   )

   lowerCamelCase_      =						__add__



   def __sub__(   self :     List[str]  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      return self + other * -1



   def __mul__(   self :     Optional[Any]  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      if not isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[]
         for i in self.duals:
            new_duals.append(i * other   )
         return Dual(self.real * other  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				[0] * (len(self.duals   ) + len(other.duals   ) + 1)
      for i, item in enumerate(self.duals   ):
         for j, jtem in enumerate(other.duals   ):
            new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals   )   ):
         new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals   )   ):
         new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real  ,				__lowerCamelCase   )

   lowerCamelCase_      =						__mul__



   def __truediv__(   self :     Optional[Any]  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      if not isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[]
         for i in self.duals:
            new_duals.append(i / other   )
         return Dual(self.real / other  ,				__lowerCamelCase   )
      raise ValueError



   def __floordiv__(   self :     Tuple  ,				__lowerCamelCase :     List[str]   ):

      """simple docstring"""
      if not isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[]
         for i in self.duals:
            new_duals.append(i // other   )
         return Dual(self.real // other  ,				__lowerCamelCase   )
      raise ValueError



   def __pow__(   self :     Tuple  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      if n < 0 or isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         raise ValueError("power must be a positive integer"   )
      if n == 0:
         return 1
      if n == 1:
         return self
      _SCREAMING_SNAKE_CASE	=				self
      for _ in range(n - 1   ):
         x *= self
      return x


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Optional[int]  )			->			Optional[Any]:
   if not callable(__A  ):
      raise ValueError("differentiate() requires a function as input for func"  )
   if not isinstance(__A	,					(float, int)  ):
      raise ValueError("differentiate() requires a float as input for position"  )
   if not isinstance(__A	,					__A  ):
      raise ValueError("differentiate() requires an int as input for order"  )
   _SCREAMING_SNAKE_CASE	=				Dual(__A	,					1  )
   _SCREAMING_SNAKE_CASE	=				func(__A  )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(__A  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 def 			SCREAMING_SNAKE_CASE_			(							__A    :			List[Any]  )			->			List[str]:
    return y**2 * y**4






 print(differentiate(f, 9, 2))







'''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


lowerCamelCase_			=						{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 		lowercase_   (			nn.Module       ):


   """simple docstring"""

   def __init__(   self :     Optional[Any]  ,				__lowerCamelCase :     int   ):

      """simple docstring"""
      super().__init__()
      _SCREAMING_SNAKE_CASE	=				torchvision.models.resnetaaa(pretrained=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				list(model.children()   )[:-2]
      _SCREAMING_SNAKE_CASE	=				nn.Sequential(*__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Optional[Any]   ):

      """simple docstring"""
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      _SCREAMING_SNAKE_CASE	=				self.pool(self.model(__lowerCamelCase   )   )
      _SCREAMING_SNAKE_CASE	=				torch.flatten(__lowerCamelCase  ,				start_dim=2   )
      _SCREAMING_SNAKE_CASE	=				out.transpose(1  ,				2   ).contiguous()
      return out  # BxNx2048







class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     List[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[json.loads(__lowerCamelCase   ) for l in open(__lowerCamelCase   )]
      _SCREAMING_SNAKE_CASE	=				os.path.dirname(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				tokenizer
      _SCREAMING_SNAKE_CASE	=				labels
      _SCREAMING_SNAKE_CASE	=				len(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				max_seq_length

      _SCREAMING_SNAKE_CASE	=				transforms



   def __len__(   self :     int   ):

      """simple docstring"""
      return len(self.data   )



   def __getitem__(   self :     str  ,				__lowerCamelCase :     Any   ):

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

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

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

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



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				Counter()
      for row in self.data:
         label_freqs.update(row["label"]   )
      return label_freqs


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[Any]  )			->			Dict:
   _SCREAMING_SNAKE_CASE	=				[len(row["sentence"]  ) for row in batch]
   _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				len(__A  ), max(__A  )

   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )
   _SCREAMING_SNAKE_CASE	=				torch.zeros(__A	,					__A	,					dtype=torch.long  )

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

   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["label"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				torch.stack([row["image_start_token"] for row in batch]  )
   _SCREAMING_SNAKE_CASE	=				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_			(							)			->			Optional[int]:
   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_56  ),
           transforms.CenterCrop(2_24  ),
           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]	,					),
       ]  )

