






"""simple docstring"""





import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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


_SCREAMING_SNAKE_CASE					:				List[str]					    =						'''▁'''

_SCREAMING_SNAKE_CASE					:				Tuple					    =						get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Dict						= BigBirdTokenizer
    SCREAMING_SNAKE_CASE						:     List[Any]						= BigBirdTokenizerFast
    SCREAMING_SNAKE_CASE						:     str						= True
    SCREAMING_SNAKE_CASE						:     List[str]						= True






    def    UpperCamelCase       (       self     :							int					)     ->		Dict:
      super().setUp()

      lowerCamelCase_         =						self.tokenizer_class(__SCREAMING_SNAKE_CASE       ,						keep_accents=__SCREAMING_SNAKE_CASE					)
      tokenizer.save_pretrained(self.tmpdirname					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[Any]:
      lowerCamelCase_         =						'<s>'
      lowerCamelCase_         =						1

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






    def    UpperCamelCase       (       self     :							Union[str, Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						list(self.get_tokenizer().get_vocab().keys()					)

      self.assertEqual(vocab_keys[0]       ,						'<unk>'					)
      self.assertEqual(vocab_keys[1]       ,						'<s>'					)
      self.assertEqual(vocab_keys[-1]       ,						'[MASK]'					)
      self.assertEqual(len(__SCREAMING_SNAKE_CASE					)       ,						1004					)






    def    UpperCamelCase       (       self     :							Any					)     ->		str:
      self.assertEqual(self.get_tokenizer().vocab_size       ,						1000					)






    def    UpperCamelCase       (       self     :							int					)     ->		Dict:
      if not self.test_rust_tokenizer:
        return

      lowerCamelCase_         =						self.get_tokenizer()
      lowerCamelCase_         =						self.get_rust_tokenizer()

      lowerCamelCase_         =						'I was born in 92000, and this is falsé.'

      lowerCamelCase_         =						tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.tokenize(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_rust_tokenizer()
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						rust_tokenizer.encode(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple					)     ->		Any:
      lowerCamelCase_         =						BigBirdTokenizer(__SCREAMING_SNAKE_CASE       ,						keep_accents=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						tokenizer.tokenize('This is a test'					)
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						['▁This', '▁is', '▁a', '▁t', 'est']					)

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)       ,						[285, 46, 10, 170, 382]       ,						)

      lowerCamelCase_         =						tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE       ,						[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '9',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              'é',
              '.',
          ]       ,						)
      lowerCamelCase_         =						tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE       ,						[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]       ,						)

      lowerCamelCase_         =						tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE					)
      self.assertListEqual(
          __SCREAMING_SNAKE_CASE       ,						[
              SPIECE_UNDERLINE + 'I',
              SPIECE_UNDERLINE + 'was',
              SPIECE_UNDERLINE + 'b',
              'or',
              'n',
              SPIECE_UNDERLINE + 'in',
              SPIECE_UNDERLINE + '',
              '<unk>',
              '2',
              '0',
              '0',
              '0',
              ',',
              SPIECE_UNDERLINE + 'and',
              SPIECE_UNDERLINE + 'this',
              SPIECE_UNDERLINE + 'is',
              SPIECE_UNDERLINE + 'f',
              'al',
              's',
              '<unk>',
              '.',
          ]       ,						)






    @cached_property
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		int:
      return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'					)






    @slow
    def    UpperCamelCase       (       self     :							Tuple					)     ->		str:
      lowerCamelCase_         =						'Hello World!'
      lowerCamelCase_         =						[65, 18536, 2260, 101, 66]

      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE					)					)






    @slow
    def    UpperCamelCase       (       self     :							List[str]					)     ->		str:
      lowerCamelCase_         =						(
          'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
          ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
      )
      # fmt: off
      lowerCamelCase_         =						[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
      # fmt: on
      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE					)					)






    @require_torch
    @slow
    def    UpperCamelCase       (       self     :							Any					)     ->		Optional[int]:
      import torch

      from transformers import BigBirdConfig, BigBirdModel

      # Build sequence
      lowerCamelCase_         =						list(self.big_tokenizer.get_vocab().keys()					)[:10]
      lowerCamelCase_         =						' '.join(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'       ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						self.big_tokenizer.batch_encode_plus(
          [sequence + ' ' + sequence]       ,						return_tensors='pt'       ,						return_token_type_ids=__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						BigBirdConfig(attention_type='original_full'					)
      lowerCamelCase_         =						BigBirdModel(__SCREAMING_SNAKE_CASE					)

      assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

      with torch.no_grad():
        model(**__SCREAMING_SNAKE_CASE					)
        model(**__SCREAMING_SNAKE_CASE					)






    @slow
    def    UpperCamelCase       (       self     :							str					)     ->		Optional[int]:
      lowerCamelCase_         =						BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'					)
      lowerCamelCase_         =						tokenizer.decode(tokenizer('Paris is the [MASK].'					).input_ids					)

      self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'					)






    @slow
    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Union[str, Any]:
      # fmt: off
      lowerCamelCase_         =						{'input_ids': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 66, 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], [65, 484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 66, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 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=__SCREAMING_SNAKE_CASE       ,						model_name='google/bigbird-roberta-base'       ,						revision='215c99f1600e06f83acce68422f2035b2b5c3510'       ,						)












"""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


_SCREAMING_SNAKE_CASE					:				List[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     :							int       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		List[Any]:
      super().__init__()
      lowerCamelCase_         =						torchvision.models.resnetaaa(pretrained=__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						list(model.children()					)[:-2]
      lowerCamelCase_         =						nn.Sequential(*__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Any					)     ->		Any:
      # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
      lowerCamelCase_         =						self.pool(self.model(__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						torch.flatten(__SCREAMING_SNAKE_CASE       ,						start_dim=2					)
      lowerCamelCase_         =						out.transpose(1       ,						2					).contiguous()
      return out  # BxNx2048






class      a						(					__snake_case		):






    def __init__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      lowerCamelCase_         =						[json.loads(__SCREAMING_SNAKE_CASE					) for l in open(__SCREAMING_SNAKE_CASE					)]
      lowerCamelCase_         =						os.path.dirname(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						tokenizer
      lowerCamelCase_         =						labels
      lowerCamelCase_         =						len(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						max_seq_length

      lowerCamelCase_         =						transforms






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






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

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

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

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






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







def      lowerCamelCase__       (							_lowerCamelCase       :		Union[str, Any] )      ->				str:
  lowerCamelCase_         =						[len(row['sentence'] ) for row in batch]
  lowerCamelCase_     ,	lowerCamelCase_         =						len(_lowerCamelCase ), max(_lowerCamelCase )

  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )
  lowerCamelCase_         =						torch.zeros(_lowerCamelCase		,				_lowerCamelCase		,				dtype=torch.long )

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

  lowerCamelCase_         =						torch.stack([row['image'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['label'] for row in batch] )
  lowerCamelCase_         =						torch.stack([row['image_start_token'] for row in batch] )
  lowerCamelCase_         =						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__       (							)      ->				List[str]:
  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__       (							)      ->				Union[str, Any]:
  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]		,				),
      ] )




