






"""simple docstring"""





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  _SCREAMING_SNAKE_CASE					:				int					    =						'''pt'''
elif is_tf_available():
  _SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						'''tf'''
else:
  _SCREAMING_SNAKE_CASE					:				Any					    =						'''jax'''






class      a						(					__snake_case  ,      unittest.TestCase		):
    SCREAMING_SNAKE_CASE						:     Tuple						= PerceiverTokenizer
    SCREAMING_SNAKE_CASE						:     List[str]						= False






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[str]:
      super().setUp()
      lowerCamelCase_         =						PerceiverTokenizer()
      tokenizer.save_pretrained(self.tmpdirname					)






    @cached_property
    def    UpperCamelCase       (       self     :							Optional[Any]					)     ->		List[Any]:
      return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						**__SCREAMING_SNAKE_CASE     :							Any					)     ->		PerceiverTokenizer:
      return self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							str=False       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=20       ,						__SCREAMING_SNAKE_CASE     :							str=5					)     ->		Tuple[str, list]:
      # XXX The default common tokenizer tests assume that every ID is decodable on its own.
      # This assumption is invalid for Perceiver because single bytes might not be
      # valid utf-8 (byte 128 for instance).
      # Here we're overriding the smallest possible method to provide
      # a clean sequence without making the same assumption.

      lowerCamelCase_         =						[]
      for i in range(len(__SCREAMING_SNAKE_CASE					)					):
        try:
          lowerCamelCase_         =						tokenizer.decode([i]       ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE					)
        except UnicodeDecodeError:
          pass
        toks.append((i, tok)					)

      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: re.match(r'^[ a-zA-Z]+$'       ,						t[1]					)       ,						__SCREAMING_SNAKE_CASE					)					)
      lowerCamelCase_         =						list(filter(lambda __SCREAMING_SNAKE_CASE					: [t[0]] == tokenizer.encode(t[1]       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)       ,						__SCREAMING_SNAKE_CASE					)					)
      if max_length is not None and len(__SCREAMING_SNAKE_CASE					) > max_length:
        lowerCamelCase_         =						toks[:max_length]
      if min_length is not None and len(__SCREAMING_SNAKE_CASE					) < min_length and len(__SCREAMING_SNAKE_CASE					) > 0:
        while len(__SCREAMING_SNAKE_CASE					) < min_length:
          lowerCamelCase_         =						toks + toks
        # toks_str = [t[1] for t in toks]
      lowerCamelCase_         =						[t[0] for t in toks]

      # Ensure consistency
      lowerCamelCase_         =						tokenizer.decode(__SCREAMING_SNAKE_CASE       ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE					)
      if " " not in output_txt and len(__SCREAMING_SNAKE_CASE					) > 1:
        lowerCamelCase_         =						(
            tokenizer.decode([toks_ids[0]]       ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE					)
            + ' '
            + tokenizer.decode(toks_ids[1:]       ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE					)
        )
      if with_prefix_space:
        lowerCamelCase_         =						' ' + output_txt
      lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
      return output_txt, output_ids






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Optional[Any]:
      lowerCamelCase_         =						self.perceiver_tokenizer
      lowerCamelCase_         =						'Unicode €.'
      lowerCamelCase_         =						tokenizer(__SCREAMING_SNAKE_CASE					)
      lowerCamelCase_         =						[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
      self.assertEqual(encoded['input_ids']       ,						__SCREAMING_SNAKE_CASE					)

      # decoding
      lowerCamelCase_         =						tokenizer.decode(__SCREAMING_SNAKE_CASE					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'[CLS]Unicode €.[SEP]'					)

      lowerCamelCase_         =						tokenizer('e è é ê ë'					)
      lowerCamelCase_         =						[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
      self.assertEqual(encoded['input_ids']       ,						__SCREAMING_SNAKE_CASE					)
      # decoding
      lowerCamelCase_         =						tokenizer.decode(__SCREAMING_SNAKE_CASE					)
      self.assertEqual(__SCREAMING_SNAKE_CASE       ,						'[CLS]e è é ê ë[SEP]'					)

      # encode/decode, but with `encode` instead of `__call__`
      self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'					)					)       ,						'[CLS]e è é ê ë[SEP]'					)






    def    UpperCamelCase       (       self     :							List[Any]					)     ->		Optional[int]:
      lowerCamelCase_         =						self.perceiver_tokenizer
      lowerCamelCase_         =						['A long paragraph for summarization.', 'Another paragraph for summarization.']
      # fmt: off
      lowerCamelCase_         =						[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
      # fmt: on
      lowerCamelCase_         =						tokenizer(__SCREAMING_SNAKE_CASE       ,						padding=__SCREAMING_SNAKE_CASE       ,						return_tensors=__SCREAMING_SNAKE_CASE					)
      self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      if FRAMEWORK != "jax":
        lowerCamelCase_         =						list(batch.input_ids.numpy()[0]					)
      else:
        lowerCamelCase_         =						list(batch.input_ids.tolist()[0]					)

      self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

      self.assertEqual((2, 38)       ,						batch.input_ids.shape					)
      self.assertEqual((2, 38)       ,						batch.attention_mask.shape					)






    def    UpperCamelCase       (       self     :							str					)     ->		str:
      lowerCamelCase_         =						self.perceiver_tokenizer
      lowerCamelCase_         =						['A long paragraph for summarization.', 'Another paragraph for summarization.']
      lowerCamelCase_         =						tokenizer(__SCREAMING_SNAKE_CASE       ,						padding=__SCREAMING_SNAKE_CASE       ,						return_tensors=__SCREAMING_SNAKE_CASE					)
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn('input_ids'       ,						__SCREAMING_SNAKE_CASE					)
      self.assertIn('attention_mask'       ,						__SCREAMING_SNAKE_CASE					)
      self.assertNotIn('decoder_input_ids'       ,						__SCREAMING_SNAKE_CASE					)
      self.assertNotIn('decoder_attention_mask'       ,						__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Any					)     ->		Any:
      lowerCamelCase_         =						self.perceiver_tokenizer
      lowerCamelCase_         =						[
          'Summary of the text.',
          'Another summary.',
      ]
      lowerCamelCase_         =						tokenizer(
          text_target=__SCREAMING_SNAKE_CASE       ,						max_length=32       ,						padding='max_length'       ,						truncation=__SCREAMING_SNAKE_CASE       ,						return_tensors=__SCREAMING_SNAKE_CASE					)
      self.assertEqual(32       ,						targets['input_ids'].shape[1]					)






    def    UpperCamelCase       (       self     :							int					)     ->		Union[str, Any]:
      # safety check on max_len default value so we are sure the test works
      lowerCamelCase_         =						self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
          self.assertNotEqual(tokenizer.model_max_length       ,						42					)

        # Now let's start the test
      lowerCamelCase_         =						self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
          # Isolate this from the other tests because we save additional tokens/etc
          lowerCamelCase_         =						tempfile.mkdtemp()

          lowerCamelCase_         =						' He is very happy, UNwant\u00E9d,running'
          lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						after_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

          shutil.rmtree(__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						self.get_tokenizers(model_max_length=42					)
      for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
          # Isolate this from the other tests because we save additional tokens/etc
          lowerCamelCase_         =						tempfile.mkdtemp()

          lowerCamelCase_         =						' He is very happy, UNwant\u00E9d,running'
          tokenizer.add_tokens(['bim', 'bambam']					)
          lowerCamelCase_         =						tokenizer.additional_special_tokens
          additional_special_tokens.append('new_additional_special_token'					)
          tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}					)
          lowerCamelCase_         =						tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE					)
          lowerCamelCase_         =						after_tokenizer.encode(__SCREAMING_SNAKE_CASE       ,						add_special_tokens=__SCREAMING_SNAKE_CASE					)
          self.assertListEqual(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          self.assertIn('new_additional_special_token'       ,						after_tokenizer.additional_special_tokens					)
          self.assertEqual(after_tokenizer.model_max_length       ,						42					)

          lowerCamelCase_         =						tokenizer.__class__.from_pretrained(__SCREAMING_SNAKE_CASE       ,						model_max_length=43					)
          self.assertEqual(tokenizer.model_max_length       ,						43					)

          shutil.rmtree(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[Any]:
      lowerCamelCase_         =						[]
      if self.test_slow_tokenizer:
        tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())					)

      if self.test_rust_tokenizer:
        tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())					)

      for tokenizer_class, tokenizer_utils in tokenizer_list:
        with tempfile.TemporaryDirectory() as tmp_dir:
          tokenizer_utils.save_pretrained(__SCREAMING_SNAKE_CASE					)

          with open(os.path.join(__SCREAMING_SNAKE_CASE       ,						'special_tokens_map.json'					)       ,						encoding='utf-8'					) as json_file:
            lowerCamelCase_         =						json.load(__SCREAMING_SNAKE_CASE					)

          with open(os.path.join(__SCREAMING_SNAKE_CASE       ,						'tokenizer_config.json'					)       ,						encoding='utf-8'					) as json_file:
            lowerCamelCase_         =						json.load(__SCREAMING_SNAKE_CASE					)

          lowerCamelCase_         =						[F'''<extra_id_{i}>''' for i in range(125					)]

          lowerCamelCase_         =						added_tokens_extra_ids + [
              'an_additional_special_token'
          ]
          lowerCamelCase_         =						added_tokens_extra_ids + [
              'an_additional_special_token'
          ]

          with open(os.path.join(__SCREAMING_SNAKE_CASE       ,						'special_tokens_map.json'					)       ,						'w'       ,						encoding='utf-8'					) as outfile:
            json.dump(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
          with open(os.path.join(__SCREAMING_SNAKE_CASE       ,						'tokenizer_config.json'					)       ,						'w'       ,						encoding='utf-8'					) as outfile:
            json.dump(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)

          # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
          # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
          # "special_tokens_map.json" files
          lowerCamelCase_         =						tokenizer_class.from_pretrained(
              __SCREAMING_SNAKE_CASE       ,						)
          self.assertIn(
              'an_additional_special_token'       ,						tokenizer_without_change_in_init.additional_special_tokens					)
          self.assertEqual(
              ['an_additional_special_token']       ,						tokenizer_without_change_in_init.convert_ids_to_tokens(
                  tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']					)					)       ,						)

          # Now we test that we can change the value of additional_special_tokens in the from_pretrained
          lowerCamelCase_         =						added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'       ,						lstrip=__SCREAMING_SNAKE_CASE					)]
          lowerCamelCase_         =						tokenizer_class.from_pretrained(
              __SCREAMING_SNAKE_CASE       ,						additional_special_tokens=__SCREAMING_SNAKE_CASE       ,						)

          self.assertIn('a_new_additional_special_token'       ,						tokenizer.additional_special_tokens					)
          self.assertEqual(
              ['a_new_additional_special_token']       ,						tokenizer.convert_ids_to_tokens(
                  tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']					)					)       ,						)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		Optional[Any]:
      lowerCamelCase_         =						self.perceiver_tokenizer
      self.assertEqual(tokenizer.decode([178]					)       ,						'�'					)






    def    UpperCamelCase       (       self     :							str					)     ->		Optional[Any]:
      pass






    def    UpperCamelCase       (       self     :							int					)     ->		Tuple:
      pass






    def    UpperCamelCase       (       self     :							List[str]					)     ->		Union[str, Any]:
      pass






    def    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      pass






    def    UpperCamelCase       (       self     :							str					)     ->		int:
      # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
      # strings and special added tokens as tokens
      lowerCamelCase_         =						self.get_tokenizers(fast=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE					)
      for tokenizer in tokenizers:
        with self.subTest(F'''{tokenizer.__class__.__name__}'''					):
          lowerCamelCase_         =						['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
          lowerCamelCase_         =						tokenizer.convert_tokens_to_string(__SCREAMING_SNAKE_CASE					)

          self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)












"""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]		,				),
      ] )




