




'''simple docstring'''

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
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 transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase="shi-labs/oneformer_demo"   )->			Union[str, Any]:
      with open(hf_hub_download(__UpperCamelCase		,						__UpperCamelCase		,						repo_type="""dataset"""   )		,						"""r"""   ) as f:
            UpperCamelCase      = json.load(__UpperCamelCase   )
      UpperCamelCase      = {}
      UpperCamelCase      = []
      UpperCamelCase      = []
      for key, info in class_info.items():
            UpperCamelCase      = info["""name"""]
            class_names.append(info["""name"""]   )
            if info["isthing"]:
                  thing_ids.append(int(__UpperCamelCase   )   )
      UpperCamelCase      = thing_ids
      UpperCamelCase      = class_names
      return metadata






class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=10      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=255      ,		_SCREAMING_SNAKE_CASE="shi-labs/oneformer_demo"      ,		_SCREAMING_SNAKE_CASE="ade20k_panoptic.json"      ,		_SCREAMING_SNAKE_CASE=10      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = class_info_file
        UpperCamelCase      = prepare_metadata(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = num_text
        UpperCamelCase      = repo_path

        # for the post_process_functions
        UpperCamelCase      = 2
        UpperCamelCase      = 10
        UpperCamelCase      = 10
        UpperCamelCase      = 3
        UpperCamelCase      = 4
        UpperCamelCase      = num_labels
        UpperCamelCase      = do_reduce_labels
        UpperCamelCase      = ignore_index

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "num_labels": self.num_labels,
            "do_reduce_labels": self.do_reduce_labels,
            "ignore_index": self.ignore_index,
            "class_info_file": self.class_info_file,
            "metadata": self.metadata,
            "num_text": self.num_text,
        }

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Union[str, Any]:



        """simple docstring"""






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

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

        return expected_height, expected_width


  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        return OneFormerForUniversalSegmentationOutput(
            # +1 for null class
            class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)				)      ,		masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)				)      ,		)






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  lowercase							=				image_processing_class

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = OneFormerImageProcessorTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processing_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_mean"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""image_std"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_normalize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_resize"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""size"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""ignore_index"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""class_info_file"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""num_text"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""repo_path"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""metadata"""				)				)
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,		"""do_reduce_labels"""				)				)

  def   A__						(				self				)  ->				int:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        UpperCamelCase      = prepare_image_inputs(self.image_processing_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				)

        # Test not batched input
        UpperCamelCase      = image_processor(image_inputs[0]      ,		["""semantic"""]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processor(
            _SCREAMING_SNAKE_CASE      ,		["""semantic"""] * len(_SCREAMING_SNAKE_CASE				)      ,		return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processing_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		numpify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		np.ndarray				)

        # Test not batched input
        UpperCamelCase      = image_processor(image_inputs[0]      ,		["""semantic"""]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processor(
            _SCREAMING_SNAKE_CASE      ,		["""semantic"""] * len(_SCREAMING_SNAKE_CASE				)      ,		return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processing_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE      ,		torchify=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		torch.Tensor				)

        # Test not batched input
        UpperCamelCase      = image_processor(image_inputs[0]      ,		["""semantic"""]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processing_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processor(
            _SCREAMING_SNAKE_CASE      ,		["""semantic"""] * len(_SCREAMING_SNAKE_CASE				)      ,		return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processing_tester.batch_size,
                self.image_processing_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE="np"				)  ->				int:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # prepare image and target
        UpperCamelCase      = self.image_processing_tester.num_labels
        UpperCamelCase      = None
        UpperCamelCase      = None
        UpperCamelCase      = prepare_image_inputs(self.image_processing_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE				)
        if with_segmentation_maps:
              UpperCamelCase      = num_labels
              if is_instance_map:
                    UpperCamelCase      = list(range(_SCREAMING_SNAKE_CASE				)				) * 2
                    UpperCamelCase      = dict(enumerate(_SCREAMING_SNAKE_CASE				)				)
              UpperCamelCase      = [
                  np.random.randint(0      ,		high * 2      ,		(img.size[1], img.size[0])				).astype(np.uinta				) for img in image_inputs
              ]
              if segmentation_type == "pil":
                    UpperCamelCase      = [Image.fromarray(_SCREAMING_SNAKE_CASE				) for annotation in annotations]

        UpperCamelCase      = image_processor(
            _SCREAMING_SNAKE_CASE      ,		["""semantic"""] * len(_SCREAMING_SNAKE_CASE				)      ,		_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""      ,		instance_id_to_semantic_id=_SCREAMING_SNAKE_CASE      ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE      ,		)

        return inputs

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        def common(_SCREAMING_SNAKE_CASE=False      ,		_SCREAMING_SNAKE_CASE=None				):
              UpperCamelCase      = self.comm_get_image_processor_inputs(
                  with_segmentation_maps=_SCREAMING_SNAKE_CASE      ,		is_instance_map=_SCREAMING_SNAKE_CASE      ,		segmentation_type=_SCREAMING_SNAKE_CASE				)

              UpperCamelCase      = inputs["""mask_labels"""]
              UpperCamelCase      = inputs["""class_labels"""]
              UpperCamelCase      = inputs["""pixel_values"""]
              UpperCamelCase      = inputs["""text_inputs"""]

              # check the batch_size
              for mask_label, class_label, text_input in zip(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				):
                    self.assertEqual(mask_label.shape[0]      ,		class_label.shape[0]				)
                    # this ensure padding has happened
                    self.assertEqual(mask_label.shape[1:]      ,		pixel_values.shape[2:]				)
                    self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		self.image_processing_tester.num_text				)

        common()
        common(is_instance_map=_SCREAMING_SNAKE_CASE				)
        common(is_instance_map=_SCREAMING_SNAKE_CASE      ,		segmentation_type="""pil"""				)
        common(is_instance_map=_SCREAMING_SNAKE_CASE      ,		segmentation_type="""pil"""				)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = np.zeros((20, 50)				)
        UpperCamelCase      = 1
        UpperCamelCase      = 1
        UpperCamelCase      = 1

        UpperCamelCase      = binary_mask_to_rle(_SCREAMING_SNAKE_CASE				)
        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		4				)
        self.assertEqual(rle[0]      ,		21				)
        self.assertEqual(rle[1]      ,		45				)

  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes      ,		max_seq_length=77      ,		task_seq_length=77      ,		class_info_file="""ade20k_panoptic.json"""      ,		num_text=self.image_processing_tester.num_text      ,		repo_path="""shi-labs/oneformer_demo"""      ,		)
        UpperCamelCase      = self.image_processing_tester.get_fake_oneformer_outputs()

        UpperCamelCase      = fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE				)

        self.assertEqual(len(_SCREAMING_SNAKE_CASE				)      ,		self.image_processing_tester.batch_size				)
        self.assertEqual(
            segmentation[0].shape      ,		(
                self.image_processing_tester.height,
                self.image_processing_tester.width,
            )      ,		)

        UpperCamelCase      = [(1, 4) for i in range(self.image_processing_tester.batch_size				)]
        UpperCamelCase      = fature_extractor.post_process_semantic_segmentation(_SCREAMING_SNAKE_CASE      ,		target_sizes=_SCREAMING_SNAKE_CASE				)

        self.assertEqual(segmentation[0].shape      ,		target_sizes[0]				)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes      ,		max_seq_length=77      ,		task_seq_length=77      ,		class_info_file="""ade20k_panoptic.json"""      ,		num_text=self.image_processing_tester.num_text      ,		repo_path="""shi-labs/oneformer_demo"""      ,		)
        UpperCamelCase      = self.image_processing_tester.get_fake_oneformer_outputs()
        UpperCamelCase      = image_processor.post_process_instance_segmentation(_SCREAMING_SNAKE_CASE      ,		threshold=0				)

        self.assertTrue(len(_SCREAMING_SNAKE_CASE				) == self.image_processing_tester.batch_size				)
        for el in segmentation:
              self.assertTrue("""segmentation""" in el				)
              self.assertTrue("""segments_info""" in el				)
              self.assertEqual(type(el["""segments_info"""]				)      ,		_SCREAMING_SNAKE_CASE				)
              self.assertEqual(
                  el["""segmentation"""].shape      ,		(self.image_processing_tester.height, self.image_processing_tester.width)				)


  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(
            num_labels=self.image_processing_tester.num_classes      ,		max_seq_length=77      ,		task_seq_length=77      ,		class_info_file="""ade20k_panoptic.json"""      ,		num_text=self.image_processing_tester.num_text      ,		repo_path="""shi-labs/oneformer_demo"""      ,		)
        UpperCamelCase      = self.image_processing_tester.get_fake_oneformer_outputs()
        UpperCamelCase      = image_processor.post_process_panoptic_segmentation(_SCREAMING_SNAKE_CASE      ,		threshold=0				)

        self.assertTrue(len(_SCREAMING_SNAKE_CASE				) == self.image_processing_tester.batch_size				)
        for el in segmentation:
              self.assertTrue("""segmentation""" in el				)
              self.assertTrue("""segments_info""" in el				)
              self.assertEqual(type(el["""segments_info"""]				)      ,		_SCREAMING_SNAKE_CASE				)
              self.assertEqual(
                  el["""segmentation"""].shape      ,		(self.image_processing_tester.height, self.image_processing_tester.width)				)








'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     =       {'vocab_file': 'vocab.txt'}

SCREAMING_SNAKE_CASE__     =       {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

SCREAMING_SNAKE_CASE__     =       {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}







def    lowercase__			(				__UpperCamelCase   )->			Any:
      with open(__UpperCamelCase		,						"""r"""   ) as f:
            UpperCamelCase      = f.read().splitlines()
            return [l.strip() for l in lines]






class     a_						(      lowerCamelCase    ):
  lowercase							=				VOCAB_FILES_NAMES
  lowercase							=				PRETRAINED_VOCAB_FILES_MAP
  lowercase							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowercase							=				["""input_ids""", """attention_mask"""]

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE="<unk>"      ,		_SCREAMING_SNAKE_CASE="<cls>"      ,		_SCREAMING_SNAKE_CASE="<pad>"      ,		_SCREAMING_SNAKE_CASE="<mask>"      ,		_SCREAMING_SNAKE_CASE="<eos>"      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Any:



        """simple docstring"""






        super().__init__(**_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = load_vocab_file(_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = dict(enumerate(self.all_tokens				)				)
        UpperCamelCase      = {tok: ind for ind, tok in enumerate(self.all_tokens				)}
        UpperCamelCase      = unk_token
        UpperCamelCase      = cls_token
        UpperCamelCase      = pad_token
        UpperCamelCase      = mask_token
        UpperCamelCase      = eos_token
        UpperCamelCase      = self.all_tokens
        self._create_trie(self.unique_no_split_tokens				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        return text.split()

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Dict:



        """simple docstring"""






        return len(self._id_to_token				)

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return {token: i for i, token in enumerate(self.all_tokens				)}

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				int:



        """simple docstring"""






        return self._token_to_id.get(_SCREAMING_SNAKE_CASE      ,		self._token_to_id.get(self.unk_token				)				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self._id_to_token.get(_SCREAMING_SNAKE_CASE      ,		self.unk_token				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None				)  ->				List[int]:



        """simple docstring"""






        UpperCamelCase      = [self.cls_token_id]
        UpperCamelCase      = [self.eos_token_id]  # No sep token in ESM vocabulary
        if token_ids_a is None:
              if self.eos_token_id is None:
                    return cls + token_ids_a
              else:
                    return cls + token_ids_a + sep
        elif self.eos_token_id is None:
              raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""				)
        return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = False				)  ->				List[int]:



        """simple docstring"""






        if already_has_special_tokens:
              if token_ids_a is not None:
                    raise ValueError(
                        """You should not supply a second sequence if the provided sequence of """
                        """ids is already formatted with special tokens for the model."""				)

              return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
        UpperCamelCase      = [1] + ([0] * len(_SCREAMING_SNAKE_CASE				)) + [1]
        if token_ids_a is not None:
              mask += [0] * len(_SCREAMING_SNAKE_CASE				) + [1]
        return mask

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = os.path.join(_SCREAMING_SNAKE_CASE      ,		(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""				)
        with open(_SCREAMING_SNAKE_CASE      ,		"""w"""				) as f:
              f.write("""\n""".join(self.all_tokens				)				)
        return (vocab_file,)

  @property
  def   A__						(				self				)  ->				int:



        """simple docstring"""






        return self.get_vocab_size(with_added_tokens=_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False				)  ->				int:



        """simple docstring"""






        return super()._add_tokens(_SCREAMING_SNAKE_CASE      ,		special_tokens=_SCREAMING_SNAKE_CASE				)
