




'''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


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     ):


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


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				self.pool(self.model(lowercase_					)					)
									snake_case_									=				torch.flatten(lowercase_       ,				start_dim=2					)
									snake_case_									=				out.transpose(1       ,				2					).contiguous()
									return out  # BxNx2048







class 						a				(   lowerCamelCase__     ):


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

									snake_case_									=				transforms


				def __len__(		self    :					List[str]					):
									return len(self.data					)


				def __getitem__(		self    :					Dict       ,				lowercase_    :					Any					):
									snake_case_									=				torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']       ,				add_special_tokens=lowercase_					)					)
									snake_case_			,snake_case_			,snake_case_									=				sentence[0], sentence[1:-1], sentence[-1]
									snake_case_									=				sentence[: self.max_seq_length]

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

									snake_case_									=				Image.open(os.path.join(self.data_dir       ,				self.data[index]['''img''']					)					).convert('''RGB'''					)
									snake_case_									=				self.transforms(lowercase_					)

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


				def   A_ (		self    :					List[Any]					):
									snake_case_									=				Counter()
									for row in self.data:
														label_freqs.update(row['''label''']					)
									return label_freqs







def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				[len(row['''sentence''']	) for row in batch]
					snake_case_			,snake_case_									=				len(_A	), max(_A	)

					snake_case_									=				torch.zeros(_A,      _A,      dtype=torch.long	)
					snake_case_									=				torch.zeros(_A,      _A,      dtype=torch.long	)

					for i_batch, (input_row, length) in enumerate(zip(_A,      _A	)	):
										snake_case_									=				input_row['''sentence''']
										snake_case_									=				1

					snake_case_									=				torch.stack([row['''image'''] for row in batch]	)
					snake_case_									=				torch.stack([row['''label'''] for row in batch]	)
					snake_case_									=				torch.stack([row['''image_start_token'''] for row in batch]	)
					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 		__magic_name__    (	)		->					List[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 		__magic_name__    (	)		->					Dict:

					'''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],      ),
					    ]	)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
