






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		:       Union[str, Any]			     = {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					:			int		,      __lowercase					:			Optional[int]					)							->      Optional[Any]:
        super().__init__()
        __UpperCAmelCase							:	List[str]           =  torchvision.models.resnetaaa(pretrained=__lowercase					)
        __UpperCAmelCase							:	str           =  list(model.children()					)[:-2]
        __UpperCAmelCase							:	Optional[Any]           =  nn.Sequential(*__lowercase					)
        __UpperCAmelCase							:	List[str]           =  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)




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


class     a    ( lowercase__   ):



      """simple docstring"""


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

        __UpperCAmelCase							:	Union[str, Any]           =  transforms
      def __len__(      self					:			Optional[int]					)							->      Dict:
        return len(self.data					)
      def __getitem__(      self					:			int		,      __lowercase					:			Optional[Any]					)							->      Any:
        __UpperCAmelCase							:	Optional[int]           =  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]		,      add_special_tokens=__lowercase					)					)
        __UpperCAmelCase      , __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  sentence[0], sentence[1:-1], sentence[-1]
        __UpperCAmelCase							:	Dict           =  sentence[: self.max_seq_length]

        __UpperCAmelCase							:	List[str]           =  torch.zeros(self.n_classes					)
        __UpperCAmelCase							:	Optional[Any]           =  1

        __UpperCAmelCase							:	Dict           =  Image.open(os.path.join(self.data_dir		,      self.data[index]["""img"""]					)					).convert("""RGB"""					)
        __UpperCAmelCase							:	Union[str, Any]           =  self.transforms(__lowercase					)

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




      def 	UpperCAmelCase      (      self					:			Optional[Any]					)							->      int:
        __UpperCAmelCase							:	str           =  Counter()
        for row in self.data:
          label_freqs.update(row["""label"""]					)
        return label_freqs






def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple		):
  __UpperCAmelCase							:	Dict           =  [len(row["""sentence"""]		) for row in batch]
  __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  len(__lowerCamelCase		), max(__lowerCamelCase		)

  __UpperCAmelCase							:	Optional[Any]           =  torch.zeros(__lowerCamelCase  ,		__lowerCamelCase  ,		dtype=torch.long		)
  __UpperCAmelCase							:	Optional[Any]           =  torch.zeros(__lowerCamelCase  ,		__lowerCamelCase  ,		dtype=torch.long		)

  for i_batch, (input_row, length) in enumerate(zip(__lowerCamelCase  ,		__lowerCamelCase		)		):
    __UpperCAmelCase							:	List[str]           =  input_row["""sentence"""]
    __UpperCAmelCase							:	Optional[Any]           =  1

  __UpperCAmelCase							:	Union[str, Any]           =  torch.stack([row["""image"""] for row in batch]		)
  __UpperCAmelCase							:	Dict           =  torch.stack([row["""label"""] for row in batch]		)
  __UpperCAmelCase							:	List[str]           =  torch.stack([row["""image_start_token"""] for row in batch]		)
  __UpperCAmelCase							:	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 				lowerCamelCase__      (    ):
  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 				lowerCamelCase__      (    ):
  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]  ,		),
      ]		)










from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


