




'''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			:	Tuple							 =     {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        UpperCamelCase_					(				nn.Module  ):

    '''simple docstring'''






    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

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





    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Any)		->Optional[int]:





           '''simple docstring'''

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


class        UpperCamelCase_					(				lowercase_  ):

    '''simple docstring'''






    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Any      ,				UpperCAmelCase__   :			Union[str, Any])		->List[str]:





           '''simple docstring'''

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

           A__   				=					transforms


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





           '''simple docstring'''

           return len(self.data)


    def __getitem__(    self   :			Tuple      ,				UpperCAmelCase__   :			Union[str, Any])		->int:





           '''simple docstring'''

           A__   				=					torch.LongTensor(self.tokenizer.encode(self.data[index]['''text''']      ,				add_special_tokens=UpperCamelCase__))
           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(UpperCamelCase__)

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





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

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







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> Any:



       """simple docstring"""

       A__   				=					[len(row['''sentence''']		) for row in batch]
       A__				,							A__   				=					len(lowercase_		), max(lowercase_		)

       A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)
       A__   				=					torch.zeros(lowercase_				,       lowercase_				,       dtype=torch.long		)

       for i_batch, (input_row, length) in enumerate(zip(lowercase_				,       lowercase_		)		):
              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 SCREAMING_SNAKE_CASE       ( )     -> int:



       """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 SCREAMING_SNAKE_CASE       ( )     -> Dict:



       """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]				,       ),
           ]		)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer


@require_torch
@require_sentencepiece
@require_tokenizers
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


