



'''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


lowercase_	 =						{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				,     A )					->  str:


      """simple docstring"""



      super().__init__()
      _a											=						torchvision.models.resnetaaa(pretrained=A )
      _a											=						list(model.children() )[:-2]
      _a											=						nn.Sequential(*A )
      _a											=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )



 def       a__						(self				,     A )					->  Dict:


      """simple docstring"""



      _a											=						self.pool(self.model(A ) )
      _a											=						torch.flatten(A				,     start_dim=2 )
      _a											=						out.transpose(1				,     2 ).contiguous()
      return out  # BxNx2048






class __A    (						A		):



 '''simple docstring'''







 def __init__(self				,     A				,     A				,     A				,     A				,     A )					->  List[str]:


      """simple docstring"""



      _a											=						[json.loads(A ) for l in open(A )]
      _a											=						os.path.dirname(A )
      _a											=						tokenizer
      _a											=						labels
      _a											=						len(A )
      _a											=						max_seq_length

      _a											=						transforms



 def __len__(self )					->  Optional[int]:


      """simple docstring"""



      return len(self.data )



 def __getitem__(self				,     A )					->  Dict:


      """simple docstring"""



      _a											=						torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']				,     add_special_tokens=A ) )
      _a						,					_a						,					_a											=						sentence[0], sentence[1:-1], sentence[-1]
      _a											=						sentence[: self.max_seq_length]

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

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

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



 def       a__						(self )					->  List[str]:


      """simple docstring"""



      _a											=						Counter()
      for row in self.data:
           label_freqs.update(row['''label'''] )
      return label_freqs





def lowerCAmelCase						(__A):



     """simple docstring"""



     _a											=						[len(row['''sentence''']) for row in batch]
     _a						,					_a											=						len(__A), max(__A)

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

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

     _a											=						torch.stack([row['''image'''] for row in batch])
     _a											=						torch.stack([row['''label'''] for row in batch])
     _a											=						torch.stack([row['''image_start_token'''] for row in batch])
     _a											=						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						():



     """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 lowerCAmelCase						():



     """simple docstring"""



     return transforms.Compose(
         [
             transforms.Resize(256),
             transforms.CenterCrop(224),
             transforms.ToTensor(),
             transforms.Normalize(
                 mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]						,			std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]						,			),
         ])








'''simple docstring'''





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
  import torch

  from transformers.generation import DisjunctiveConstraint






@require_torch
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						[[1, 2, 4], [1, 2, 3, 4]]
      _a											=						DisjunctiveConstraint(A )
      self.assertTrue(isinstance(dc.token_ids				,     A ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]] ) )

      with self.assertRaises(A ):
           DisjunctiveConstraint([torch.LongTensor([1, 2, 4] ), torch.LongTensor([1, 2, 3, 4, 5] )] )



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						[[1, 2], [1, 2, 3, 4]]

      with self.assertRaises(A ):
           DisjunctiveConstraint(A )  # fails here



 def       a__						(self )					->  Dict:


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      _a											=						stepped is True and completed is False and reset is False
      self.assertTrue(A )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(3 )
      _a											=						stepped is True and completed is True and reset is False
      self.assertTrue(A )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 3] )



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


      """simple docstring"""



      _a											=						[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

      _a											=						DisjunctiveConstraint(A )

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(4 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.current_seq == [1, 2, 4] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.current_seq == [1, 2, 4, 5] )

      dc.reset()

      _a						,					_a						,					_a											=						dc.update(1 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 3 )
      self.assertTrue(dc.current_seq == [1] )

      _a						,					_a						,					_a											=						dc.update(2 )
      self.assertTrue(not dc.completed )
      self.assertTrue(dc.remaining() == 2 )
      self.assertTrue(dc.current_seq == [1, 2] )

      _a						,					_a						,					_a											=						dc.update(5 )
      self.assertTrue(dc.completed )  # Completed!
      self.assertTrue(dc.remaining() == 0 )
      self.assertTrue(dc.current_seq == [1, 2, 5] )



