


"""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 				SCREAMING_SNAKE_CASE__    (		nn.Module    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__						):






         """simple docstring"""





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







       def 	lowercase__							(						self		,					snake_case__						):






         """simple docstring"""





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


class 				SCREAMING_SNAKE_CASE__    (		lowercase    ):
       """simple docstring"""


       def __init__(						self		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase					:					Tuple								=							transforms

       def __len__(						self						):






         """simple docstring"""





         return len(self.data						)

       def __getitem__(						self		,					snake_case__						):






         """simple docstring"""





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

         lowerCAmelCase					:					Tuple								=							torch.zeros(self.n_classes						)
         lowerCAmelCase					:					List[Any]								=							1

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

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







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							Counter()
         for row in self.data:
           label_freqs.update(row["label"]						)
         return label_freqs

def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[int]    ):


  '''simple docstring'''







  lowerCAmelCase					:					Optional[int]								=							[len(row["sentence"]    ) for row in batch]
  lowerCAmelCase					:					Tuple								=							len(SCREAMING_SNAKE_CASE    ), max(SCREAMING_SNAKE_CASE    )

  lowerCAmelCase					:					Dict								=							torch.zeros(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				dtype=torch.long    )
  lowerCAmelCase					:					Any								=							torch.zeros(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				dtype=torch.long    )

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

  lowerCAmelCase					:					List[str]								=							torch.stack([row["image"] for row in batch]    )
  lowerCAmelCase					:					Optional[Any]								=							torch.stack([row["label"] for row in batch]    )
  lowerCAmelCase					:					int								=							torch.stack([row["image_start_token"] for row in batch]    )
  lowerCAmelCase					:					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  a__      (    ):


  '''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  a__      (    ):


  '''simple docstring'''







  return transforms.Compose(
      [
          transforms.Resize(2_5_6    ),
          transforms.CenterCrop(2_2_4    ),
          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"""







import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):  # picklable for multiprocessing


  '''simple docstring'''







  return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def  a__      (    ):


  '''simple docstring'''







  with parallel_backend("spark"    ):
    assert ParallelBackendConfig.backend_name == "spark"

  lowerCAmelCase					:					List[str]								=							[1, 2, 3]
  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=2    )

  with pytest.raises(SCREAMING_SNAKE_CASE    ):
    with parallel_backend("unsupported backend"    ):
      map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=-1    )





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc"    ,				[2, -1]    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Tuple								=							[1, 2]
  lowerCAmelCase					:					int								=							{"a": 1, "b": 2}
  lowerCAmelCase					:					List[str]								=							{"a": [1, 2], "b": [3, 4]}
  lowerCAmelCase					:					Dict								=							{"a": {"1": 1}, "b": 2}
  lowerCAmelCase					:					Tuple								=							{"a": 1, "b": 2, "c": 3, "d": 4}
  lowerCAmelCase					:					Any								=							[2, 3]
  lowerCAmelCase					:					Any								=							{"a": 2, "b": 3}
  lowerCAmelCase					:					Optional[int]								=							{"a": [2, 3], "b": [4, 5]}
  lowerCAmelCase					:					Optional[int]								=							{"a": {"1": 2}, "b": 3}
  lowerCAmelCase					:					str								=							{"a": 2, "b": 3, "c": 4, "d": 5}

  with parallel_backend("spark"    ):
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa
    assert map_nested(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				num_proc=SCREAMING_SNAKE_CASE    ) == expected_map_nested_sa





