


'''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 ConditionalDetrImageProcessor
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		def __init__(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=7       ,  UpperCamelCase_=3       ,  UpperCamelCase_=30       ,  UpperCamelCase_=400       ,  UpperCamelCase_=True       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_=[0.5, 0.5, 0.5]       ,  UpperCamelCase_=[0.5, 0.5, 0.5]       ,  UpperCamelCase_=True       ,  UpperCamelCase_=1 / 255       ,  UpperCamelCase_=True       ,  ):

							'''simple docstring'''


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




		def        lowerCAmelCase__							(						self				):

							'''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,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_=False				):

							'''simple docstring'''


							if not batched:
												UpperCamelCase__  :List[str]				   =					image_inputs[0]
												if isinstance(A__       ,  Image.Image				):
																	UpperCamelCase__  :Dict				   =					image.size
												else:
																	UpperCamelCase__  :str				   =					image.shape[1], image.shape[2]
												if w < h:
																	UpperCamelCase__  :Union[str, Any]				   =					int(self.size['''shortest_edge'''] * h / w				)
																	UpperCamelCase__  :int				   =					self.size["""shortest_edge"""]
												elif w > h:
																	UpperCamelCase__  :List[str]				   =					self.size["""shortest_edge"""]
																	UpperCamelCase__  :int				   =					int(self.size['''shortest_edge'''] * w / h				)
												else:
																	UpperCamelCase__  :List[Any]				   =					self.size["""shortest_edge"""]
																	UpperCamelCase__  :Dict				   =					self.size["""shortest_edge"""]

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

							return expected_height, expected_width



@require_torch
@require_vision
class   lowercase      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							ConditionalDetrImageProcessor if is_vision_available() else None




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					ConditionalDetrImageProcessingTester(self				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							return self.image_processor_tester.prepare_image_processor_dict()




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.image_processing_class(**self.image_processor_dict				)
							self.assertTrue(hasattr(A__       ,  '''image_mean'''				)				)
							self.assertTrue(hasattr(A__       ,  '''image_std'''				)				)
							self.assertTrue(hasattr(A__       ,  '''do_normalize'''				)				)
							self.assertTrue(hasattr(A__       ,  '''do_resize'''				)				)
							self.assertTrue(hasattr(A__       ,  '''size'''				)				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Union[str, 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       ,  A__				)

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							pass




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							UpperCamelCase__  :Union[str, Any]				   =					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=A__				)
							for image in image_inputs:
												self.assertIsInstance(A__       ,  Image.Image				)

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

							UpperCamelCase__  :str				   =					self.image_processor_tester.get_expected_values(A__				)

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

							# Test batched
							UpperCamelCase__  :Optional[Any]				   =					self.image_processor_tester.get_expected_values(A__       ,  batched=A__				)

							UpperCamelCase__  :Optional[int]				   =					image_processing(A__       ,  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        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							UpperCamelCase__  :List[str]				   =					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=A__       ,  numpify=A__				)
							for image in image_inputs:
												self.assertIsInstance(A__       ,  np.ndarray				)

							# Test not batched input
							UpperCamelCase__  :List[str]				   =					image_processing(image_inputs[0]       ,  return_tensors='''pt'''				).pixel_values

							UpperCamelCase__  :int				   =					self.image_processor_tester.get_expected_values(A__				)

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

							# Test batched
							UpperCamelCase__  :int				   =					image_processing(A__       ,  return_tensors='''pt'''				).pixel_values

							UpperCamelCase__  :Dict				   =					self.image_processor_tester.get_expected_values(A__       ,  batched=A__				)

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




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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

							# Test not batched input
							UpperCamelCase__  :List[str]				   =					image_processing(image_inputs[0]       ,  return_tensors='''pt'''				).pixel_values

							UpperCamelCase__  :Any				   =					self.image_processor_tester.get_expected_values(A__				)

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

							# Test batched
							UpperCamelCase__  :Tuple				   =					image_processing(A__       ,  return_tensors='''pt'''				).pixel_values

							UpperCamelCase__  :Optional[int]				   =					self.image_processor_tester.get_expected_values(A__       ,  batched=A__				)

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




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''       ,  '''r'''				) as f:
												UpperCamelCase__  :Optional[int]				   =					json.loads(f.read()				)

							UpperCamelCase__  :List[str]				   =					{"""image_id""": 39769, """annotations""": target}

							# encode them
							UpperCamelCase__  :Optional[Any]				   =					ConditionalDetrImageProcessor.from_pretrained('''microsoft/conditional-detr-resnet-50'''				)
							UpperCamelCase__  :Optional[Any]				   =					image_processing(images=A__       ,  annotations=A__       ,  return_tensors='''pt'''				)

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

							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor([0.2796, 0.3138, 0.3481]				)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,  A__       ,  atol=1e-4				)				)

							# verify area
							UpperCamelCase__  :str				   =					torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,  A__				)				)
							# verify boxes
							UpperCamelCase__  :Tuple				   =					torch.Size([6, 4]				)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,  A__				)
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,  A__       ,  atol=1e-3				)				)
							# verify image_id
							UpperCamelCase__  :Optional[int]				   =					torch.tensor([39769]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,  A__				)				)
							# verify is_crowd
							UpperCamelCase__  :Dict				   =					torch.tensor([0, 0, 0, 0, 0, 0]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,  A__				)				)
							# verify class_labels
							UpperCamelCase__  :List[Any]				   =					torch.tensor([75, 75, 63, 65, 17, 17]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,  A__				)				)
							# verify orig_size
							UpperCamelCase__  :Dict				   =					torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,  A__				)				)
							# verify size
							UpperCamelCase__  :Dict				   =					torch.tensor([800, 1066]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,  A__				)				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[int]				   =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,  '''r'''				) as f:
												UpperCamelCase__  :str				   =					json.loads(f.read()				)

							UpperCamelCase__  :List[Any]				   =					{"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

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

							# encode them
							UpperCamelCase__  :List[str]				   =					ConditionalDetrImageProcessor(format='''coco_panoptic'''				)
							UpperCamelCase__  :Tuple				   =					image_processing(images=A__       ,  annotations=A__       ,  masks_path=A__       ,  return_tensors='''pt'''				)

							# verify pixel values
							UpperCamelCase__  :Optional[int]				   =					torch.Size([1, 3, 800, 1066]				)
							self.assertEqual(encoding['''pixel_values'''].shape       ,  A__				)

							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor([0.2796, 0.3138, 0.3481]				)
							self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,  A__       ,  atol=1e-4				)				)

							# verify area
							UpperCamelCase__  :List[Any]				   =					torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,  A__				)				)
							# verify boxes
							UpperCamelCase__  :List[str]				   =					torch.Size([6, 4]				)
							self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,  A__				)
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,  A__       ,  atol=1e-3				)				)
							# verify image_id
							UpperCamelCase__  :Dict				   =					torch.tensor([39769]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,  A__				)				)
							# verify is_crowd
							UpperCamelCase__  :Tuple				   =					torch.tensor([0, 0, 0, 0, 0, 0]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,  A__				)				)
							# verify class_labels
							UpperCamelCase__  :str				   =					torch.tensor([17, 17, 63, 75, 75, 93]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,  A__				)				)
							# verify masks
							UpperCamelCase__  :Dict				   =					822873
							self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,  A__				)
							# verify orig_size
							UpperCamelCase__  :Optional[int]				   =					torch.tensor([480, 640]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,  A__				)				)
							# verify size
							UpperCamelCase__  :Tuple				   =					torch.tensor([800, 1066]				)
							self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,  A__				)				)


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model'}

A_			:					Optional[Any]    			=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A_			:					Tuple    			=     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[int]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  str    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  List[str]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  str    				=			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:  List[int]    				=			[]






					def __init__(					self       ,			A__       ,			A__="<unk>"       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<pad>"       ,			A__="[SEP]"       ,			A__="[MASK]"       ,			A__="[CLS]"       ,			A__ = None       ,			**A__       ,			):
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else bos_token
						A__				:       int	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else eos_token
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else unk_token
						A__				:       str	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else pad_token
						A__				:       Dict	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else cls_token
						A__				:       Any	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=A__       ,			eos_token=A__       ,			unk_token=A__       ,			pad_token=A__       ,			sep_token=A__       ,			mask_token=A__       ,			cls_token=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       Any	  =						vocab_file

						A__				:       Optional[int]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






					def     __A     (					self     ):
						A__				:       List[str]	  =						{self.convert_ids_to_tokens(A__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab






					def __getstate__(					self     ):
						A__				:       Optional[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Dict	  =						d

						# for backward compatibility
						if not hasattr(self       ,			"""sp_model_kwargs"""     ):
							A__				:       str	  =						{}

						A__				:       Tuple	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						[]
						A__				:       Optional[int]	  =						""""""
						A__				:       Any	  =						False
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								if not prev_is_special:
									out_string += " "
								out_string += self.sp_model.decode(A__     ) + token
								A__				:       Union[str, Any]	  =						True
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
								A__				:       Any	  =						False
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[str]	  =						kwargs.pop("""use_source_tokenizer"""       ,			A__     )

						A__				:       Union[str, Any]	  =						self.convert_ids_to_tokens(A__       ,			skip_special_tokens=A__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A__				:       Dict	  =						[]
						A__				:       List[Any]	  =						[]
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A__     )     )
									A__				:       Optional[Any]	  =						[]
								sub_texts.append(A__     )
							else:
								current_sub_text.append(A__     )
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
							A__				:       List[Any]	  =						re.sub(r""" (\[(MASK|SEP)\])"""       ,			r"""\1"""       ,			""" """.join(A__     )     )
						else:
							A__				:       Optional[Any]	  =						"""""".join(A__     )

						A__				:       Union[str, Any]	  =						(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							A__				:       Union[str, Any]	  =						self.clean_up_tokenization(A__     )
							return clean_text
						else:
							return text






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       str	  =						os.path.join(
						    A__       ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

						if os.path.abspath(self.vocab_file     ) != os.path.abspath(A__     ) and os.path.isfile(self.vocab_file     ):
							copyfile(self.vocab_file       ,			A__     )
						elif not os.path.isfile(self.vocab_file     ):
							with open(A__       ,			"""wb"""     ) as fi:
								A__				:       Dict	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						A__				:       List[Any]	  =						[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = False     ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A__       ,			token_ids_a=A__       ,			already_has_special_tokens=A__     )

						if token_ids_a is None:
							return [1] + ([0] * len(A__     )) + [1]
						return [1] + ([0] * len(A__     )) + [1] + ([0] * len(A__     )) + [1]






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Any	  =						[self.sep_token_id]
						A__				:       List[Any]	  =						[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     ) * [0] + len(token_ids_a + sep     ) * [1]



