




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DetrImageProcessor





class     SCREAMING_SNAKE_CASE__		(unittest.TestCase			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		int=7       ,						__lowerCamelCase	:		List[Any]=3       ,						__lowerCamelCase	:		List[Any]=30       ,						__lowerCamelCase	:		List[str]=4_00       ,						__lowerCamelCase	:		int=True       ,						__lowerCamelCase	:		Dict=None       ,						__lowerCamelCase	:		Tuple=True       ,						__lowerCamelCase	:		Dict=1 / 2_55       ,						__lowerCamelCase	:		Any=True       ,						__lowerCamelCase	:		Union[str, Any]=[0.5, 0.5, 0.5]       ,						__lowerCamelCase	:		Union[str, Any]=[0.5, 0.5, 0.5]       ,						__lowerCamelCase	:		Any=True       ,						):



       """simple docstring"""
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       lowerCAmelCase__										=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
       lowerCAmelCase__										=      parent
       lowerCAmelCase__										=      batch_size
       lowerCAmelCase__										=      num_channels
       lowerCAmelCase__										=      min_resolution
       lowerCAmelCase__										=      max_resolution
       lowerCAmelCase__										=      do_resize
       lowerCAmelCase__										=      size
       lowerCAmelCase__										=      do_rescale
       lowerCAmelCase__										=      rescale_factor
       lowerCAmelCase__										=      do_normalize
       lowerCAmelCase__										=      image_mean
       lowerCAmelCase__										=      image_std
       lowerCAmelCase__										=      do_pad
     def        A__      (						self	:		str						):



       """simple docstring"""
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_pad": self.do_pad,
       }



     def        A__      (						self	:		Any       ,						__lowerCamelCase	:		Optional[int]       ,						__lowerCamelCase	:		Optional[Any]=False						):



       """simple docstring"""
       if not batched:
         lowerCAmelCase__										=      image_inputs[0]
         if isinstance(__lowerCamelCase       ,						Image.Image						):
           lowerCAmelCase__				,      lowerCAmelCase__										=      image.size
         else:
           lowerCAmelCase__				,      lowerCAmelCase__										=      image.shape[1], image.shape[2]
         if w < h:
           lowerCAmelCase__										=      int(self.size['''shortest_edge'''] * h / w						)
           lowerCAmelCase__										=      self.size['''shortest_edge''']
         elif w > h:
           lowerCAmelCase__										=      self.size['''shortest_edge''']
           lowerCAmelCase__										=      int(self.size['''shortest_edge'''] * w / h						)
         else:
           lowerCAmelCase__										=      self.size['''shortest_edge''']
           lowerCAmelCase__										=      self.size['''shortest_edge''']

       else:
         lowerCAmelCase__										=      []
         for image in image_inputs:
           lowerCAmelCase__				,      lowerCAmelCase__										=      self.get_expected_values([image]						)
           expected_values.append((expected_height, expected_width)						)
         lowerCAmelCase__										=      max(__lowerCamelCase       ,						key=lambda __lowerCamelCase						: item[0]						)[0]
         lowerCAmelCase__										=      max(__lowerCamelCase       ,						key=lambda __lowerCamelCase						: item[1]						)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class     SCREAMING_SNAKE_CASE__		(_a    ,					unittest.TestCase			):
     lowercase_ :      Union[str, Any]				= DetrImageProcessor if is_vision_available() else None
     def        A__      (						self	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      DetrImageProcessingTester(self						)
     @property
     def        A__      (						self	:		Dict						):



       """simple docstring"""
       return self.image_processor_tester.prepare_image_processor_dict()
     def        A__      (						self	:		Any						):



       """simple docstring"""
       lowerCAmelCase__										=      self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''image_mean'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''image_std'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''do_normalize'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''do_rescale'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''rescale_factor'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''do_resize'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''size'''						)						)
       self.assertTrue(hasattr(__lowerCamelCase       ,						'''do_pad'''						)						)
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       lowerCAmelCase__										=      self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size       ,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}						)
       self.assertEqual(image_processor.do_pad       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      self.image_processing_class.from_dict(
           self.image_processor_dict       ,						size=42       ,						max_size=84       ,						pad_and_return_pixel_mask=__lowerCamelCase						)
       self.assertEqual(image_processor.size       ,						{'''shortest_edge''': 42, '''longest_edge''': 84}						)
       self.assertEqual(image_processor.do_pad       ,						__lowerCamelCase						)
     def        A__      (						self	:		List[str]						):



       """simple docstring"""
       pass
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__										=      self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       lowerCAmelCase__										=      prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__lowerCamelCase						)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase       ,						Image.Image						)

       # Test not batched input
       lowerCAmelCase__										=      image_processing(image_inputs[0]       ,						return_tensors='''pt'''						).pixel_values

       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase						)

       self.assertEqual(
           encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

       # Test batched
       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase       ,						batched=__lowerCamelCase						)

       lowerCAmelCase__										=      image_processing(__lowerCamelCase       ,						return_tensors='''pt'''						).pixel_values
       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,						)
     def        A__      (						self	:		str						):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__										=      self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       lowerCAmelCase__										=      prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__lowerCamelCase       ,						numpify=__lowerCamelCase						)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase       ,						np.ndarray						)

       # Test not batched input
       lowerCAmelCase__										=      image_processing(image_inputs[0]       ,						return_tensors='''pt'''						).pixel_values

       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase						)

       self.assertEqual(
           encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

       # Test batched
       lowerCAmelCase__										=      image_processing(__lowerCamelCase       ,						return_tensors='''pt'''						).pixel_values

       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase       ,						batched=__lowerCamelCase						)

       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,						)
     def        A__      (						self	:		int						):



       """simple docstring"""
       # Initialize image_processing
       lowerCAmelCase__										=      self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       lowerCAmelCase__										=      prepare_image_inputs(self.image_processor_tester       ,						equal_resolution=__lowerCamelCase       ,						torchify=__lowerCamelCase						)
       for image in image_inputs:
         self.assertIsInstance(__lowerCamelCase       ,						torch.Tensor						)

       # Test not batched input
       lowerCAmelCase__										=      image_processing(image_inputs[0]       ,						return_tensors='''pt'''						).pixel_values

       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase						)

       self.assertEqual(
           encoded_images.shape       ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,						)

       # Test batched
       lowerCAmelCase__										=      image_processing(__lowerCamelCase       ,						return_tensors='''pt'''						).pixel_values

       lowerCAmelCase__				,      lowerCAmelCase__										=      self.image_processor_tester.get_expected_values(__lowerCamelCase       ,						batched=__lowerCamelCase						)

       self.assertEqual(
           encoded_images.shape       ,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )       ,						)
     @slow
     def        A__      (						self	:		List[Any]						):



       """simple docstring"""
       # prepare image and target
       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,						'''r'''						) as f:
         lowerCAmelCase__										=      json.loads(f.read()						)

       lowerCAmelCase__										=      {'''image_id''': 3_97_69, '''annotations''': target}

       # encode them
       lowerCAmelCase__										=      DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''						)
       lowerCAmelCase__										=      image_processing(images=__lowerCamelCase       ,						annotations=__lowerCamelCase       ,						return_tensors='''pt'''						)

       # verify pixel values
       lowerCAmelCase__										=      torch.Size([1, 3, 8_00, 10_66]						)
       self.assertEqual(encoding['''pixel_values'''].shape       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,						__lowerCamelCase       ,						atol=1e-4						)						)

       # verify area
       lowerCAmelCase__										=      torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,						__lowerCamelCase						)						)
       # verify boxes
       lowerCAmelCase__										=      torch.Size([6, 4]						)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,						__lowerCamelCase       ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase__										=      torch.tensor([3_97_69]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,						__lowerCamelCase						)						)
       # verify is_crowd
       lowerCAmelCase__										=      torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,						__lowerCamelCase						)						)
       # verify class_labels
       lowerCAmelCase__										=      torch.tensor([75, 75, 63, 65, 17, 17]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,						__lowerCamelCase						)						)
       # verify orig_size
       lowerCAmelCase__										=      torch.tensor([4_80, 6_40]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,						__lowerCamelCase						)						)
       # verify size
       lowerCAmelCase__										=      torch.tensor([8_00, 10_66]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,						__lowerCamelCase						)						)



     @slow
     def        A__      (						self	:		Optional[int]						):



       """simple docstring"""
       # prepare image, target and masks_path
       lowerCAmelCase__										=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
       with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,						'''r'''						) as f:
         lowerCAmelCase__										=      json.loads(f.read()						)

       lowerCAmelCase__										=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

       lowerCAmelCase__										=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

       # encode them
       lowerCAmelCase__										=      DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''						)
       lowerCAmelCase__										=      image_processing(images=__lowerCamelCase       ,						annotations=__lowerCamelCase       ,						masks_path=__lowerCamelCase       ,						return_tensors='''pt'''						)

       # verify pixel values
       lowerCAmelCase__										=      torch.Size([1, 3, 8_00, 10_66]						)
       self.assertEqual(encoding['''pixel_values'''].shape       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      torch.tensor([0.2796, 0.3138, 0.3481]						)
       self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,						__lowerCamelCase       ,						atol=1e-4						)						)

       # verify area
       lowerCAmelCase__										=      torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,						__lowerCamelCase						)						)
       # verify boxes
       lowerCAmelCase__										=      torch.Size([6, 4]						)
       self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,						__lowerCamelCase       ,						atol=1e-3						)						)
       # verify image_id
       lowerCAmelCase__										=      torch.tensor([3_97_69]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,						__lowerCamelCase						)						)
       # verify is_crowd
       lowerCAmelCase__										=      torch.tensor([0, 0, 0, 0, 0, 0]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,						__lowerCamelCase						)						)
       # verify class_labels
       lowerCAmelCase__										=      torch.tensor([17, 17, 63, 75, 75, 93]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,						__lowerCamelCase						)						)
       # verify masks
       lowerCAmelCase__										=      82_28_73
       self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,						__lowerCamelCase						)
       # verify orig_size
       lowerCAmelCase__										=      torch.tensor([4_80, 6_40]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,						__lowerCamelCase						)						)
       # verify size
       lowerCAmelCase__										=      torch.tensor([8_00, 10_66]						)
       self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,						__lowerCamelCase						)						)







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_ta import TaTokenizer
else:
      __magic_name__  :  Dict					=       None


__magic_name__  :  Optional[Any]					=       logging.get_logger(__name__)

__magic_name__  :  Optional[Any]					=       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__magic_name__  :  Union[str, Any]					=       {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__magic_name__  :  Tuple					=       {
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      Dict				= VOCAB_FILES_NAMES
     lowercase_ :      Any				= PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      Tuple				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      Tuple				= ["input_ids", "attention_mask"]
     lowercase_ :      Any				= TaTokenizer

     lowercase_ :      List[int]				= []
     def __init__(						self	:		Tuple       ,						__lowerCamelCase	:		Tuple=None       ,						__lowerCamelCase	:		str=None       ,						__lowerCamelCase	:		Optional[int]="</s>"       ,						__lowerCamelCase	:		Optional[Any]="<unk>"       ,						__lowerCamelCase	:		int="<pad>"       ,						__lowerCamelCase	:		int=1_00       ,						__lowerCamelCase	:		List[str]=None       ,						**__lowerCamelCase	:		Tuple       ,						):



       """simple docstring"""
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
         lowerCAmelCase__										=      [F"""<extra_id_{i}>""" for i in range(__lowerCamelCase						)]
       elif extra_ids > 0 and additional_special_tokens is not None:
         # Check that we have the right number of extra special tokens
         lowerCAmelCase__										=      len(set(filter(lambda __lowerCamelCase						: bool('''extra_id_''' in str(__lowerCamelCase						)						)       ,						__lowerCamelCase						)						)						)
         if extra_tokens != extra_ids:
           raise ValueError(
               F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
               ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
               ''' tokens'''						)

       super().__init__(
           __lowerCamelCase       ,						tokenizer_file=__lowerCamelCase       ,						eos_token=__lowerCamelCase       ,						unk_token=__lowerCamelCase       ,						pad_token=__lowerCamelCase       ,						extra_ids=__lowerCamelCase       ,						additional_special_tokens=__lowerCamelCase       ,						**__lowerCamelCase       ,						)

       lowerCAmelCase__										=      vocab_file
       lowerCAmelCase__										=      False if not self.vocab_file else True
       lowerCAmelCase__										=      extra_ids
     @staticmethod
     def        A__      (						__lowerCamelCase	:		Dict       ,						__lowerCamelCase	:		List[str]       ,						__lowerCamelCase	:		Any						):



       """simple docstring"""
       if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
         lowerCAmelCase__										=      TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
         if init_max_model_length is not None and init_max_model_length != max_model_length:
           return init_max_model_length
         elif init_max_model_length is None:
           warnings.warn(
               '''This tokenizer was incorrectly instantiated with a model max length of'''
               F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
               ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
               ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
               F""" {pretrained_model_name_or_path} automatically truncating your input to"""
               F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
               F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
               ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
               ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,						__lowerCamelCase       ,						)

       return max_model_length
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[str] = None						):



       """simple docstring"""
       if not self.can_save_slow_tokenizer:
         raise ValueError(
             '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
             '''tokenizer.'''						)

       if not os.path.isdir(__lowerCamelCase						):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
         return
       lowerCAmelCase__										=      os.path.join(
           __lowerCamelCase       ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(__lowerCamelCase						):
         copyfile(self.vocab_file       ,						__lowerCamelCase						)
         logger.info(F"""Copy vocab file to {out_vocab_file}"""						)

       return (out_vocab_file,)
     def        A__      (						self	:		Optional[Any]       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
         return self.prefix_tokens + token_ids_a
       else:
         lowerCAmelCase__										=      token_ids_a + [self.eos_token_id]
         return self.prefix_tokens + token_ids_a + token_ids_a
     def        A__      (						self	:		int       ,						__lowerCamelCase	:		List[int]       ,						__lowerCamelCase	:		Optional[List[int]] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      [self.eos_token_id]

       if token_ids_a is None:
         return len(token_ids_a + eos						) * [0]
       return len(token_ids_a + eos + token_ids_a + eos						) * [0]
     def        A__      (						self	:		int						):



       """simple docstring"""
       return list(
           set(filter(lambda __lowerCamelCase						: bool(re.search(r'''<extra_id_\d+>'''       ,						__lowerCamelCase						)						) is not None       ,						self.additional_special_tokens						)						)						)



     def        A__      (						self	:		Dict						):



       """simple docstring"""
       return [self.convert_tokens_to_ids(__lowerCamelCase						) for token in self.get_sentinel_tokens()]

