


import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_A			=						get_tests_dir('fixtures/test_sentencepiece_no_bos.model')






@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase__	( A_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  PegasusTokenizer
    UpperCAmelCase__						:		Tuple              =  PegasusTokenizerFast
    UpperCAmelCase__						:		str              =  True
    UpperCAmelCase__						:		Optional[int]              =  True






    def _a (      self   )  ->	int:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __UpperCamelCase   							=PegasusTokenizer(A_   )
         tokenizer.save_pretrained(self.tmpdirname   )






    @cached_property
    def _a (      self   )  ->	Union[str, Any]:
         return PegasusTokenizer.from_pretrained('google/pegasus-large'   )






    def _a (      self ,       **A_   )  ->	PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname ,       **A_   )






    def _a (      self ,       A_   )  ->	Tuple:
         return ("This is a test", "This is a test")






    def _a (      self   )  ->	List[Any]:
         __UpperCamelCase   							='</s>'
         __UpperCamelCase   							=1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_   ) ,       A_   )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_   ) ,       A_   )






    def _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase   							=list(self.get_tokenizer().get_vocab().keys()   )

         self.assertEqual(vocab_keys[0] ,       '<pad>'   )
         self.assertEqual(vocab_keys[1] ,       '</s>'   )
         self.assertEqual(vocab_keys[-1] ,       'v'   )
         self.assertEqual(len(A_   ) ,       1103   )






    def _a (      self   )  ->	List[Any]:
         self.assertEqual(self.get_tokenizer().vocab_size ,       1103   )






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
         __UpperCamelCase   							=self.tokenizer_class.from_pretrained(self.tmpdirname   )
         __UpperCamelCase   							=(
             'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
             ' </s> <pad> <pad> <pad>'
         )
         __UpperCamelCase   							=rust_tokenizer([raw_input_str] ,       return_tensors=A_ ,       add_special_tokens=A_   ).input_ids[0]
         __UpperCamelCase   							=py_tokenizer([raw_input_str] ,       return_tensors=A_ ,       add_special_tokens=A_   ).input_ids[0]
         self.assertListEqual(A_ ,       A_   )






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         __UpperCamelCase   							='<mask_1> To ensure a <mask_2> flow of bank resolutions.'
         __UpperCamelCase   							=[2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
         __UpperCamelCase   							=tokenizer([raw_input_str] ,       return_tensors=A_   ).input_ids[0]
         self.assertListEqual(A_ ,       A_   )






    def _a (      self   )  ->	Optional[Any]:
         __UpperCamelCase   							=self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 96103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1024
         __UpperCamelCase   							='To ensure a smooth flow of bank resolutions.'
         __UpperCamelCase   							=[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
         __UpperCamelCase   							=tokenizer([raw_input_str] ,       return_tensors=A_   ).input_ids[0]
         self.assertListEqual(A_ ,       A_   )
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]   ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






    @require_torch
    def _a (      self   )  ->	List[Any]:
         __UpperCamelCase   							=['This is going to be way too long.' * 150, 'short example']
         __UpperCamelCase   							=['not super long but more than 5 tokens', 'tiny']
         __UpperCamelCase   							=self._large_tokenizer(A_ ,       padding=A_ ,       truncation=A_ ,       return_tensors='pt'   )
         __UpperCamelCase   							=self._large_tokenizer(
             text_target=A_ ,       max_length=5 ,       padding=A_ ,       truncation=A_ ,       return_tensors='pt'   )

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(A_   ) == 2  # input_ids, attention_mask.






    @slow
    def _a (      self   )  ->	Dict:
         # fmt: off
         __UpperCamelCase   							={'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=A_ ,       model_name='google/bigbird-pegasus-large-arxiv' ,       revision='ba85d0851d708441f91440d509690f1ab6353415' ,       )






@require_sentencepiece
@require_tokenizers
class    UpperCAmelCase__	( A_       ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict              =  PegasusTokenizer
    UpperCAmelCase__						:		Optional[Any]              =  PegasusTokenizerFast
    UpperCAmelCase__						:		Union[str, Any]              =  True
    UpperCAmelCase__						:		Union[str, Any]              =  True






    def _a (      self   )  ->	Union[str, Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __UpperCamelCase   							=PegasusTokenizer(A_ ,       offset=0 ,       mask_token_sent=A_ ,       mask_token='[MASK]'   )
         tokenizer.save_pretrained(self.tmpdirname   )






    @cached_property
    def _a (      self   )  ->	Union[str, Any]:
         return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'   )






    def _a (      self ,       **A_   )  ->	PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname ,       **A_   )






    def _a (      self ,       A_   )  ->	Optional[Any]:
         return ("This is a test", "This is a test")






    def _a (      self   )  ->	int:
         __UpperCamelCase   							=self.rust_tokenizer_class.from_pretrained(self.tmpdirname   )
         __UpperCamelCase   							=self.tokenizer_class.from_pretrained(self.tmpdirname   )
         __UpperCamelCase   							=(
             'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
             ' <pad> <pad> <pad>'
         )
         __UpperCamelCase   							=rust_tokenizer([raw_input_str] ,       return_tensors=A_ ,       add_special_tokens=A_   ).input_ids[0]
         __UpperCamelCase   							=py_tokenizer([raw_input_str] ,       return_tensors=A_ ,       add_special_tokens=A_   ).input_ids[0]
         self.assertListEqual(A_ ,       A_   )






    @require_torch
    def _a (      self   )  ->	int:
         __UpperCamelCase   							=['This is going to be way too long.' * 1000, 'short example']
         __UpperCamelCase   							=['not super long but more than 5 tokens', 'tiny']
         __UpperCamelCase   							=self._large_tokenizer(A_ ,       padding=A_ ,       truncation=A_ ,       return_tensors='pt'   )
         __UpperCamelCase   							=self._large_tokenizer(
             text_target=A_ ,       max_length=5 ,       padding=A_ ,       truncation=A_ ,       return_tensors='pt'   )

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(A_   ) == 2  # input_ids, attention_mask.






    def _a (      self   )  ->	List[str]:
         __UpperCamelCase   							=(
             'This is an example string that is used to test the original TF implementation against the HF'
             ' implementation'
         )

         __UpperCamelCase   							=self._large_tokenizer(A_   ).input_ids

         self.assertListEqual(
             A_ ,       [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1] ,       )









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			=						{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 ,       A_   )  ->	Optional[int]:
         super().__init__()
         __UpperCamelCase   							=torchvision.models.resnetaaa(pretrained=A_   )
         __UpperCamelCase   							=list(model.children()   )[:-2]
         __UpperCamelCase   							=nn.Sequential(*A_   )
         __UpperCamelCase   							=nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]   )






    def _a (      self ,       A_   )  ->	int:
         # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
         __UpperCamelCase   							=self.pool(self.model(A_   )   )
         __UpperCamelCase   							=torch.flatten(A_ ,       start_dim=2   )
         __UpperCamelCase   							=out.transpose(1 ,       2   ).contiguous()
         return out  # BxNx2048






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         __UpperCamelCase   							=[json.loads(A_   ) for l in open(A_   )]
         __UpperCamelCase   							=os.path.dirname(A_   )
         __UpperCamelCase   							=tokenizer
         __UpperCamelCase   							=labels
         __UpperCamelCase   							=len(A_   )
         __UpperCamelCase   							=max_seq_length

         __UpperCamelCase   							=transforms






    def __len__(      self   )  ->	Any:
         return len(self.data   )






    def __getitem__(      self ,       A_   )  ->	Union[str, Any]:
         __UpperCamelCase   							=torch.LongTensor(self.tokenizer.encode(self.data[index]['text'] ,       add_special_tokens=A_   )   )
         __UpperCamelCase							,				__UpperCamelCase							,				__UpperCamelCase   							=sentence[0], sentence[1:-1], sentence[-1]
         __UpperCamelCase   							=sentence[: self.max_seq_length]

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

         __UpperCamelCase   							=Image.open(os.path.join(self.data_dir ,       self.data[index]['img']   )   ).convert('RGB'   )
         __UpperCamelCase   							=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]:
         __UpperCamelCase   							=Counter()
         for row in self.data:
              label_freqs.update(row['label']   )
         return label_freqs

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Union[str, Any]    ):
     __UpperCamelCase   							=[len(row['sentence']    ) for row in batch]
     __UpperCamelCase							,				__UpperCamelCase   							=len(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )

     __UpperCamelCase   							=torch.zeros(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					dtype=torch.long    )
     __UpperCamelCase   							=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__    )    ):
          __UpperCamelCase   							=input_row['sentence']
          __UpperCamelCase   							=1

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





