


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


UpperCAmelCase_   :				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 				SCREAMING_SNAKE_CASE__						(       nn.Module    ):
      def __init__( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str  )	->    Optional[Any]:
             super().__init__()
             a_      :	Any							=						torchvision.models.resnetaaa(pretrained=SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						list(model.children()  )[:-2]
             a_      :	Any							=						nn.Sequential(*SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )

      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Tuple:
             # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
             a_      :	Union[str, Any]							=						self.pool(self.model(SCREAMING_SNAKE_CASE__  )  )
             a_      :	int							=						torch.flatten(SCREAMING_SNAKE_CASE__  ,							start_dim=2  )
             a_      :	Optional[int]							=						out.transpose(1  ,							2  ).contiguous()
             return out  # BxNx2048
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : Tuple  )	->    int:
             a_      :	Dict							=						[json.loads(SCREAMING_SNAKE_CASE__  ) for l in open(SCREAMING_SNAKE_CASE__  )]
             a_      :	Optional[Any]							=						os.path.dirname(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						tokenizer
             a_      :	Tuple							=						labels
             a_      :	List[Any]							=						len(SCREAMING_SNAKE_CASE__  )
             a_      :	Dict							=						max_seq_length

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

             a_      :	Optional[int]							=						torch.zeros(self.n_classes  )
             a_      :	Tuple							=						1

             a_      :	Optional[Any]							=						Image.open(os.path.join(self.data_dir  ,							self.data[index]['img']  )  ).convert('RGB'  )
             a_      :	List[str]							=						self.transforms(SCREAMING_SNAKE_CASE__  )

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

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    int:
             a_      :	Dict							=						Counter()
             for row in self.data:
                    label_freqs.update(row['label']  )
             return label_freqs







def  SCREAMING_SNAKE_CASE_		(    __A				:    List[Any]       )			->	Union[str, Any]:

       """simple docstring"""

       a_      :	Tuple							=						[len(row['sentence']       ) for row in batch]
       a_							,	a_      :	Optional[int]							=						len(__A       ), max(__A       )

       a_      :	Union[str, Any]							=						torch.zeros(__A						,					__A						,					dtype=torch.long       )
       a_      :	Optional[Any]							=						torch.zeros(__A						,					__A						,					dtype=torch.long       )

       for i_batch, (input_row, length) in enumerate(zip(__A						,					__A       )       ):
              a_      :	Tuple							=						input_row['sentence']
              a_      :	List[str]							=						1

       a_      :	Dict							=						torch.stack([row['image'] for row in batch]       )
       a_      :	Optional[int]							=						torch.stack([row['label'] for row in batch]       )
       a_      :	int							=						torch.stack([row['image_start_token'] for row in batch]       )
       a_      :	List[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  SCREAMING_SNAKE_CASE_		(    )			->	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  SCREAMING_SNAKE_CASE_		(    )			->	Optional[int]:

       """simple docstring"""

       return transforms.Compose(
           [
               transforms.Resize(2_56       ),
               transforms.CenterCrop(2_24       ),
               transforms.ToTensor(),
               transforms.Normalize(
                   mean=[0.46777044, 0.44531429, 0.40661017]						,					std=[0.12221994, 0.12145835, 0.14380469]						,					),
           ]       )






import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					unittest.TestCase    ):
      snake_case__       :  List[str]       =		MvpTokenizer
      snake_case__       :  Dict       =		MvpTokenizerFast
      snake_case__       :  Any       =		True
      snake_case__       :  Optional[int]       =		filter_roberta_detectors
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Optional[int]:
             super().setUp()
             a_      :	Any							=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             a_      :	int							=						dict(zip(SCREAMING_SNAKE_CASE__  ,							range(len(SCREAMING_SNAKE_CASE__  )  )  )  )
             a_      :	int							=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             a_      :	str							=						{'unk_token': '<unk>'}

             a_      :	Tuple							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['vocab_file']  )
             a_      :	Optional[int]							=						os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['merges_file']  )
             with open(self.vocab_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write(json.dumps(SCREAMING_SNAKE_CASE__  ) + '\n'  )
             with open(self.merges_file  ,							'w'  ,							encoding='utf-8'  ) as fp:
                    fp.write('\n'.join(SCREAMING_SNAKE_CASE__  )  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    List[Any]:
             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Any  ,							**SCREAMING_SNAKE_CASE__       : Optional[Any]  )	->    List[str]:
             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,							**SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Union[str, Any]:
             return "lower newer", "lower newer"
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    str:
             return MvpTokenizer.from_pretrained('RUCAIBox/mvp'  )
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    Optional[int]:
             return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             a_      :	List[Any]							=						[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							max_length=len(SCREAMING_SNAKE_CASE__  )  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

                    self.assertEqual((2, 9)  ,							batch.input_ids.shape  )
                    self.assertEqual((2, 9)  ,							batch.attention_mask.shape  )
                    a_      :	Optional[Any]							=						batch.input_ids.tolist()[0]
                    self.assertListEqual(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    # Test that special tokens are reset
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Optional[Any]:
             a_      :	Any							=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Optional[Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    # check if input_ids are returned and no labels
                    self.assertIn('input_ids'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertIn('attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('labels'  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertNotIn('decoder_attention_mask'  ,							SCREAMING_SNAKE_CASE__  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Union[str, Any]							=						[
                 'Summary of the text.',
                 'Another summary.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	List[str]							=						tokenizer(text_target=SCREAMING_SNAKE_CASE__  ,							max_length=3_2  ,							padding='max_length'  ,							return_tensors='pt'  )
                    self.assertEqual(3_2  ,							targets['input_ids'].shape[1]  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Dict							=						tokenizer(
                        ['I am a small frog' * 1_0_2_4, 'I am a small frog']  ,							padding=SCREAMING_SNAKE_CASE__  ,							truncation=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    self.assertIsInstance(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    self.assertEqual(batch.input_ids.shape  ,							(2, 1_0_2_4)  )
      @require_torch
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    Tuple:
             a_      :	Dict							=						['A long paragraph for summarization.']
             a_      :	Dict							=						[
                 'Summary of the text.',
             ]
             for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                    a_      :	Union[str, Any]							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							text_target=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )
                    a_      :	Dict							=						inputs['input_ids']
                    a_      :	str							=						inputs['labels']
                    self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                    self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                    self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    Union[str, Any]:
             pass

      def  SCREAMING_SNAKE_CASE							( self       : Any  )	->    int:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""  ):
                           a_      :	List[str]							=						self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	Tuple							=						self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							**SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						'A, <mask> AllenNLP sentence.'
                           a_      :	Optional[Any]							=						tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )
                           a_      :	int							=						tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__  ,							add_special_tokens=SCREAMING_SNAKE_CASE__  ,							return_token_type_ids=SCREAMING_SNAKE_CASE__  )

                           # token_type_ids should put 0 everywhere
                           self.assertEqual(sum(tokens_r['token_type_ids']  )  ,							sum(tokens_p['token_type_ids']  )  )

                           # attention_mask should put 1 everywhere, so sum over length should be 1
                           self.assertEqual(
                               sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )  ,							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )  ,							)

                           a_      :	str							=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                           a_      :	Any							=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                           # Rust correctly handles the space before the mask while python doesnt
                           self.assertSequenceEqual(tokens_p['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )
                           self.assertSequenceEqual(tokens_r['input_ids']  ,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]  )

                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                           self.assertSequenceEqual(
                               SCREAMING_SNAKE_CASE__  ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )


