




'''simple docstring'''


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   :  int						,						lowercase__   :  str						,						lowercase__   :  List[str]=7						,						lowercase__   :  Tuple=3						,						lowercase__   :  Union[str, Any]=30						,						lowercase__   :  Tuple=400						,						lowercase__   :  Dict=True						,						lowercase__   :  str=None						,						lowercase__   :  Dict=True						,						lowercase__   :  Any=1 / 255						,						lowercase__   :  Any=True						,						lowercase__   :  int=[0.5, 0.5, 0.5]						,						lowercase__   :  Union[str, Any]=[0.5, 0.5, 0.5]						,						lowercase__   :  Optional[Any]=True						,						):


           '''simple docstring'''

           a_     :							List[Any]     						=  size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
           a_     :							Optional[int]     						=  parent
           a_     :							Union[str, Any]     						=  batch_size
           a_     :							Any     						=  num_channels
           a_     :							List[str]     						=  min_resolution
           a_     :							Union[str, Any]     						=  max_resolution
           a_     :							Any     						=  do_resize
           a_     :							str     						=  size
           a_     :							Any     						=  do_rescale
           a_     :							Optional[int]     						=  rescale_factor
           a_     :							Any     						=  do_normalize
           a_     :							Tuple     						=  image_mean
           a_     :							Optional[Any]     						=  image_std
           a_     :							Any     						=  do_pad






      def 							lowercase_						( self   :  Any       ):


           '''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 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  List[Any]						,						lowercase__   :  List[str]=False       ):


           '''simple docstring'''

           if not batched:
                a_     :							Any     						=  image_inputs[0]
                if isinstance(lowercase__						,						Image.Image       ):
                     a_						,     a_     :							Optional[Any]     						=  image.size
                else:
                     a_						,     a_     :							int     						=  image.shape[1], image.shape[2]
                if w < h:
                     a_     :							Optional[int]     						=  int(self.size["""shortest_edge"""] * h / w       )
                     a_     :							Tuple     						=  self.size["""shortest_edge"""]
                elif w > h:
                     a_     :							Dict     						=  self.size["""shortest_edge"""]
                     a_     :							Dict     						=  int(self.size["""shortest_edge"""] * w / h       )
                else:
                     a_     :							List[Any]     						=  self.size["""shortest_edge"""]
                     a_     :							Optional[Any]     						=  self.size["""shortest_edge"""]

           else:
                a_     :							Optional[Any]     						=  []
                for image in image_inputs:
                     a_						,     a_     :							Optional[Any]     						=  self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
                a_     :							Union[str, Any]     						=  max(lowercase__						,						key=lambda lowercase__       : item[0]       )[0]
                a_     :							Dict     						=  max(lowercase__						,						key=lambda lowercase__       : item[1]       )[1]

           return expected_height, expected_width







@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Any   							=					DetrImageProcessor if is_vision_available() else None






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  DetrImageProcessingTester(self       )






      @property
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							int     						=  self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(lowercase__						,						"""image_mean"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""image_std"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""do_normalize"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""do_rescale"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""rescale_factor"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""do_resize"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""size"""       )       )
           self.assertTrue(hasattr(lowercase__						,						"""do_pad"""       )       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size						,						{"""shortest_edge""": 18, """longest_edge""": 1333}       )
           self.assertEqual(image_processor.do_pad						,						lowercase__       )

           a_     :							int     						=  self.image_processing_class.from_dict(
               self.image_processor_dict						,						size=42						,						max_size=84						,						pad_and_return_pixel_mask=lowercase__       )
           self.assertEqual(image_processor.size						,						{"""shortest_edge""": 42, """longest_edge""": 84}       )
           self.assertEqual(image_processor.do_pad						,						lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							str     						=  self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           a_     :							Dict     						=  prepare_image_inputs(self.image_processor_tester						,						equal_resolution=lowercase__       )
           for image in image_inputs:
                self.assertIsInstance(lowercase__						,						Image.Image       )

           # Test not batched input
           a_     :							Optional[Any]     						=  image_processing(image_inputs[0]						,						return_tensors="""pt"""       ).pixel_values

           a_						,     a_     :							int     						=  self.image_processor_tester.get_expected_values(lowercase__       )

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

           # Test batched
           a_						,     a_     :							int     						=  self.image_processor_tester.get_expected_values(lowercase__						,						batched=lowercase__       )

           a_     :							Dict     						=  image_processing(lowercase__						,						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 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           a_     :							Tuple     						=  prepare_image_inputs(self.image_processor_tester						,						equal_resolution=lowercase__						,						numpify=lowercase__       )
           for image in image_inputs:
                self.assertIsInstance(lowercase__						,						np.ndarray       )

           # Test not batched input
           a_     :							List[Any]     						=  image_processing(image_inputs[0]						,						return_tensors="""pt"""       ).pixel_values

           a_						,     a_     :							Union[str, Any]     						=  self.image_processor_tester.get_expected_values(lowercase__       )

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

           # Test batched
           a_     :							List[str]     						=  image_processing(lowercase__						,						return_tensors="""pt"""       ).pixel_values

           a_						,     a_     :							List[Any]     						=  self.image_processor_tester.get_expected_values(lowercase__						,						batched=lowercase__       )

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






      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           a_     :							List[str]     						=  prepare_image_inputs(self.image_processor_tester						,						equal_resolution=lowercase__						,						torchify=lowercase__       )
           for image in image_inputs:
                self.assertIsInstance(lowercase__						,						torch.Tensor       )

           # Test not batched input
           a_     :							Any     						=  image_processing(image_inputs[0]						,						return_tensors="""pt"""       ).pixel_values

           a_						,     a_     :							int     						=  self.image_processor_tester.get_expected_values(lowercase__       )

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

           # Test batched
           a_     :							str     						=  image_processing(lowercase__						,						return_tensors="""pt"""       ).pixel_values

           a_						,     a_     :							Union[str, Any]     						=  self.image_processor_tester.get_expected_values(lowercase__						,						batched=lowercase__       )

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






      @slow
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Any     						=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
           with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""						,						"""r"""       ) as f:
                a_     :							str     						=  json.loads(f.read()       )

           a_     :							str     						=  {"""image_id""": 3_9769, """annotations""": target}

           # encode them
           a_     :							List[Any]     						=  DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50"""       )
           a_     :							List[Any]     						=  image_processing(images=lowercase__						,						annotations=lowercase__						,						return_tensors="""pt"""       )

           # verify pixel values
           a_     :							List[str]     						=  torch.Size([1, 3, 800, 1066]       )
           self.assertEqual(encoding["""pixel_values"""].shape						,						lowercase__       )

           a_     :							str     						=  torch.tensor([0.2796, 0.3138, 0.3481]       )
           self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,						lowercase__						,						atol=1e-4       )       )

           # verify area
           a_     :							str     						=  torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,						lowercase__       )       )
           # verify boxes
           a_     :							Union[str, Any]     						=  torch.Size([6, 4]       )
           self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,						lowercase__       )
           a_     :							List[Any]     						=  torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,						lowercase__						,						atol=1e-3       )       )
           # verify image_id
           a_     :							Dict     						=  torch.tensor([3_9769]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,						lowercase__       )       )
           # verify is_crowd
           a_     :							Any     						=  torch.tensor([0, 0, 0, 0, 0, 0]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,						lowercase__       )       )
           # verify class_labels
           a_     :							str     						=  torch.tensor([75, 75, 63, 65, 17, 17]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,						lowercase__       )       )
           # verify orig_size
           a_     :							Dict     						=  torch.tensor([480, 640]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,						lowercase__       )       )
           # verify size
           a_     :							Dict     						=  torch.tensor([800, 1066]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,						lowercase__       )       )






      @slow
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							Dict     						=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
           with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""						,						"""r"""       ) as f:
                a_     :							Union[str, Any]     						=  json.loads(f.read()       )

           a_     :							Optional[Any]     						=  {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

           a_     :							List[str]     						=  pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""       )

           # encode them
           a_     :							Optional[Any]     						=  DetrImageProcessor.from_pretrained("""facebook/detr-resnet-50-panoptic"""       )
           a_     :							str     						=  image_processing(images=lowercase__						,						annotations=lowercase__						,						masks_path=lowercase__						,						return_tensors="""pt"""       )

           # verify pixel values
           a_     :							List[Any]     						=  torch.Size([1, 3, 800, 1066]       )
           self.assertEqual(encoding["""pixel_values"""].shape						,						lowercase__       )

           a_     :							int     						=  torch.tensor([0.2796, 0.3138, 0.3481]       )
           self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]						,						lowercase__						,						atol=1e-4       )       )

           # verify area
           a_     :							Tuple     						=  torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]						,						lowercase__       )       )
           # verify boxes
           a_     :							Optional[Any]     						=  torch.Size([6, 4]       )
           self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape						,						lowercase__       )
           a_     :							Dict     						=  torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]						,						lowercase__						,						atol=1e-3       )       )
           # verify image_id
           a_     :							Optional[Any]     						=  torch.tensor([3_9769]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]						,						lowercase__       )       )
           # verify is_crowd
           a_     :							Optional[int]     						=  torch.tensor([0, 0, 0, 0, 0, 0]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]						,						lowercase__       )       )
           # verify class_labels
           a_     :							Union[str, Any]     						=  torch.tensor([17, 17, 63, 75, 75, 93]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]						,						lowercase__       )       )
           # verify masks
           a_     :							int     						=  82_2873
           self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()						,						lowercase__       )
           # verify orig_size
           a_     :							Any     						=  torch.tensor([480, 640]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]						,						lowercase__       )       )
           # verify size
           a_     :							List[Any]     						=  torch.tensor([800, 1066]       )
           self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]						,						lowercase__       )       )






'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


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

lowerCAmelCase_      :  int				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       int   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[str]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       str   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']
      __magic_name__  :       Any   							=					RobertaTokenizer






      def __init__( self   :  Any						,						lowercase__   :  int=None						,						lowercase__   :  Union[str, Any]=None						,						lowercase__   :  List[Any]=None						,						lowercase__   :  int="replace"						,						lowercase__   :  Dict="<s>"						,						lowercase__   :  int="</s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  List[Any]="<s>"						,						lowercase__   :  Optional[int]="<unk>"						,						lowercase__   :  Tuple="<pad>"						,						lowercase__   :  Optional[Any]="<mask>"						,						lowercase__   :  List[Any]=False						,						lowercase__   :  Optional[Any]=True						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           super().__init__(
               lowercase__						,						lowercase__						,						tokenizer_file=lowercase__						,						errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						trim_offsets=lowercase__						,						**lowercase__						,						)

           a_     :							Any     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							List[Any]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							Optional[Any]     						=  add_prefix_space
                a_     :							List[str]     						=  pre_tok_class(**lowercase__       )

           a_     :							List[Any]     						=  add_prefix_space

           a_     :							Any     						=  """post_processor"""
           a_     :							Dict     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[Any]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							Dict     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							List[str]     						=  tuple(state["""cls"""]       )

                a_     :							str     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Any     						=  add_prefix_space
                     a_     :							List[str]     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							Optional[int]     						=  trim_offsets
                     a_     :							Tuple     						=  True

                if changes_to_apply:
                     a_     :							str     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Union[str, Any]     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Union[str, Any]     						=  value






      def 							lowercase_						( self   :  Union[str, Any]						,						*lowercase__   :  Union[str, Any]						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )
           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						*lowercase__   :  int						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							str     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[Any]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							int     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  [self.sep_token_id]
           a_     :							Optional[int]     						=  [self.cls_token_id]

           if token_ids_a is None:
                return len(cls + token_ids_a + sep       ) * [0]
           return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
