



"""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 YolosImageProcessor
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A=7       ,	A=3       ,	A=3_0       ,	A=4_0_0       ,	A=True       ,	A=None       ,	A=True       ,	A=[0.5, 0.5, 0.5]       ,	A=[0.5, 0.5, 0.5]       ,	A=True       ,	A=1 / 2_5_5       ,	A=True       ,	)							->					List[str]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         _UpperCAmelCase							:  List[str]											=    size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
         _UpperCAmelCase							:  List[str]											=    parent
         _UpperCAmelCase							:  Dict											=    batch_size
         _UpperCAmelCase							:  Optional[Any]											=    num_channels
         _UpperCAmelCase							:  str											=    min_resolution
         _UpperCAmelCase							:  Any											=    max_resolution
         _UpperCAmelCase							:  List[Any]											=    do_resize
         _UpperCAmelCase							:  Optional[int]											=    size
         _UpperCAmelCase							:  List[str]											=    do_normalize
         _UpperCAmelCase							:  Any											=    image_mean
         _UpperCAmelCase							:  Union[str, Any]											=    image_std
         _UpperCAmelCase							:  int											=    do_rescale
         _UpperCAmelCase							:  str											=    rescale_factor
         _UpperCAmelCase							:  str											=    do_pad





     def  __lowerCAmelCase	(					self		)							->					Dict:
         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       ,	A       ,	A=False		)							->					List[str]:
         if not batched:
             _UpperCAmelCase							:  List[Any]											=    image_inputs[0]
             if isinstance(A       ,	Image.Image		):
                 _UpperCAmelCase							,				_UpperCAmelCase							:  str											=    image.size
             else:
                 _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    image.shape[1], image.shape[2]
             if w < h:
                 _UpperCAmelCase							:  List[str]											=    int(self.size['''shortest_edge'''] * h / w		)
                 _UpperCAmelCase							:  Optional[int]											=    self.size['''shortest_edge''']
             elif w > h:
                 _UpperCAmelCase							:  Dict											=    self.size['''shortest_edge''']
                 _UpperCAmelCase							:  List[Any]											=    int(self.size['''shortest_edge'''] * w / h		)
             else:
                 _UpperCAmelCase							:  Union[str, Any]											=    self.size['''shortest_edge''']
                 _UpperCAmelCase							:  Optional[int]											=    self.size['''shortest_edge''']

         else:
             _UpperCAmelCase							:  Any											=    []
             for image in image_inputs:
                 _UpperCAmelCase							,				_UpperCAmelCase							:  List[Any]											=    self.get_expected_values([image]		)
                 expected_values.append((expected_height, expected_width)		)
             _UpperCAmelCase							:  List[str]											=    max(A       ,	key=lambda A		: item[0]		)[0]
             _UpperCAmelCase							:  Optional[int]											=    max(A       ,	key=lambda A		: item[1]		)[1]

         return expected_height, expected_width



@require_torch
@require_vision
class 							_UpperCAmelCase							( a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=YolosImageProcessor if is_vision_available() else None





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Optional[Any]											=    YolosImageProcessingTester(self		)





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         return self.image_processor_tester.prepare_image_processor_dict()





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    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		)							->					List[Any]:
         _UpperCAmelCase							:  Optional[Any]											=    self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}		)
         self.assertEqual(image_processor.do_pad       ,	A		)

         _UpperCAmelCase							:  Any											=    self.image_processing_class.from_dict(
             self.image_processor_dict       ,	size=4_2       ,	max_size=8_4       ,	pad_and_return_pixel_mask=A		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}		)
         self.assertEqual(image_processor.do_pad       ,	A		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         pass





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         # Initialize image_processing
         _UpperCAmelCase							:  Union[str, 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							:  str											=    image_processing(image_inputs[0]       ,	return_tensors='''pt'''		).pixel_values

         _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, 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							,				_UpperCAmelCase							:  Tuple											=    self.image_processor_tester.get_expected_values(A       ,	batched=A		)

         _UpperCAmelCase							:  Tuple											=    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		)							->					Any:
         # Initialize image_processing
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         _UpperCAmelCase							:  Dict											=    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							:  Tuple											=    image_processing(image_inputs[0]       ,	return_tensors='''pt'''		).pixel_values

         _UpperCAmelCase							,				_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							:  Optional[int]											=    image_processing(A       ,	return_tensors='''pt'''		).pixel_values

         _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    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		)							->					List[Any]:
         # Initialize image_processing
         _UpperCAmelCase							:  Any											=    self.image_processing_class(**self.image_processor_dict		)
         # create random PyTorch tensors
         _UpperCAmelCase							:  int											=    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							,				_UpperCAmelCase							:  Tuple											=    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							,				_UpperCAmelCase							:  Any											=    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		)							->					List[str]:
         # Initialize image_processings
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(**self.image_processor_dict		)
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(do_resize=A       ,	do_normalize=A       ,	do_rescale=A		)
         # 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 whether the method "pad" and calling the image processor return the same tensors
         _UpperCAmelCase							:  int											=    image_processing_a.pad(A       ,	return_tensors='''pt'''		)
         _UpperCAmelCase							:  Optional[int]											=    image_processing_a(A       ,	return_tensors='''pt'''		)

         self.assertTrue(
             torch.allclose(encoded_images_with_method['''pixel_values''']       ,	encoded_images['''pixel_values''']       ,	atol=1E-4		)		)





     @slow
     def  __lowerCAmelCase	(					self		)							->					Tuple:
         # prepare image and target
         _UpperCAmelCase							:  Any											=    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''': 3_9_7_6_9, '''annotations''': target}

         # encode them
         _UpperCAmelCase							:  Tuple											=    YolosImageProcessor.from_pretrained('''hustvl/yolos-small'''		)
         _UpperCAmelCase							:  Optional[Any]											=    image_processing(images=A       ,	annotations=A       ,	return_tensors='''pt'''		)

         # verify pixel values
         _UpperCAmelCase							:  Optional[Any]											=    torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
         self.assertEqual(encoding['''pixel_values'''].shape       ,	A		)

         _UpperCAmelCase							:  List[str]											=    torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,	A       ,	atol=1E-4		)		)

         # verify area
         _UpperCAmelCase							:  Dict											=    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]		)
         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							:  Dict											=    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,	A       ,	atol=1E-3		)		)
         # verify image_id
         _UpperCAmelCase							:  str											=    torch.tensor([3_9_7_6_9]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,	A		)		)
         # verify is_crowd
         _UpperCAmelCase							:  Optional[Any]											=    torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,	A		)		)
         # verify class_labels
         _UpperCAmelCase							:  Any											=    torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,	A		)		)
         # verify orig_size
         _UpperCAmelCase							:  Tuple											=    torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,	A		)		)
         # verify size
         _UpperCAmelCase							:  Optional[int]											=    torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,	A		)		)





     @slow
     def  __lowerCAmelCase	(					self		)							->					Optional[int]:
         # prepare image, target and masks_path
         _UpperCAmelCase							:  Tuple											=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''       ,	'''r'''		) as f:
             _UpperCAmelCase							:  Any											=    json.loads(f.read()		)

         _UpperCAmelCase							:  Optional[Any]											=    {'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

         _UpperCAmelCase							:  Optional[Any]											=    pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''		)

         # encode them
         _UpperCAmelCase							:  Dict											=    YolosImageProcessor(format='''coco_panoptic'''		)
         _UpperCAmelCase							:  Any											=    image_processing(images=A       ,	annotations=A       ,	masks_path=A       ,	return_tensors='''pt'''		)

         # verify pixel values
         _UpperCAmelCase							:  Tuple											=    torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
         self.assertEqual(encoding['''pixel_values'''].shape       ,	A		)

         _UpperCAmelCase							:  List[Any]											=    torch.tensor([0.2_796, 0.3_138, 0.3_481]		)
         self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]       ,	A       ,	atol=1E-4		)		)

         # verify area
         _UpperCAmelCase							:  List[Any]											=    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']       ,	A		)		)
         # verify boxes
         _UpperCAmelCase							:  Optional[Any]											=    torch.Size([6, 4]		)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape       ,	A		)
         _UpperCAmelCase							:  Dict											=    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]       ,	A       ,	atol=1E-3		)		)
         # verify image_id
         _UpperCAmelCase							:  List[str]											=    torch.tensor([3_9_7_6_9]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']       ,	A		)		)
         # verify is_crowd
         _UpperCAmelCase							:  str											=    torch.tensor([0, 0, 0, 0, 0, 0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']       ,	A		)		)
         # verify class_labels
         _UpperCAmelCase							:  List[str]											=    torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']       ,	A		)		)
         # verify masks
         _UpperCAmelCase							:  Optional[Any]											=    8_2_2_8_7_3
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()       ,	A		)
         # verify orig_size
         _UpperCAmelCase							:  Dict											=    torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']       ,	A		)		)
         # verify size
         _UpperCAmelCase							:  Optional[Any]											=    torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']       ,	A		)		)







"""simple docstring"""
import os
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


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)

_lowerCAmelCase    :List[str]     =					'▁'

_lowerCAmelCase    :Tuple     =					{'vocab_file': 'sentencepiece.bpe.model'}

_lowerCAmelCase    :List[Any]     =					{
    'vocab_file': {
        'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model',
        'xlm-roberta-large-finetuned-conll02-dutch': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll02-spanish': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-english': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'
        ),
        'xlm-roberta-large-finetuned-conll03-german': (
            'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

_lowerCAmelCase    :Tuple     =					{
    'xlm-roberta-base': 512,
    'xlm-roberta-large': 512,
    'xlm-roberta-large-finetuned-conll02-dutch': 512,
    'xlm-roberta-large-finetuned-conll02-spanish': 512,
    'xlm-roberta-large-finetuned-conll03-english': 512,
    'xlm-roberta-large-finetuned-conll03-german': 512,
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=VOCAB_FILES_NAMES
     a__									=PRETRAINED_VOCAB_FILES_MAP
     a__									=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a__									=['''input_ids''', '''attention_mask''']





     def __init__(					self       ,	A       ,	A="<s>"       ,	A="</s>"       ,	A="</s>"       ,	A="<s>"       ,	A="<unk>"       ,	A="<pad>"       ,	A="<mask>"       ,	A = None       ,	**A       ,	)							->					None:
         # Mask token behave like a normal word, i.e. include the space before it
         _UpperCAmelCase							:  Tuple											=    AddedToken(A       ,	lstrip=A       ,	rstrip=A		) if isinstance(A       ,	A		) else mask_token

         _UpperCAmelCase							:  List[Any]											=    {} if sp_model_kwargs is None else sp_model_kwargs

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

         _UpperCAmelCase							:  Any											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.Load(str(A		)		)
         _UpperCAmelCase							:  List[Any]											=    vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # Mimic fairseq token-to-id alignment for the first 4 token
         _UpperCAmelCase							:  List[str]											=    {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

         # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
         _UpperCAmelCase							:  Any											=    1

         _UpperCAmelCase							:  Optional[Any]											=    len(self.sp_model		) + self.fairseq_offset
         _UpperCAmelCase							:  int											=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  Tuple											=    self.__dict__.copy()
         _UpperCAmelCase							:  List[str]											=    None
         _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
         return state





     def __setstate__(					self       ,	A		)							->					Optional[int]:
         _UpperCAmelCase							:  Optional[int]											=    d

         # for backward compatibility
         if not hasattr(self       ,	'''sp_model_kwargs'''		):
             _UpperCAmelCase							:  Optional[Any]											=    {}

         _UpperCAmelCase							:  Optional[int]											=    spm.SentencePieceProcessor(**self.sp_model_kwargs		)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:

         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         _UpperCAmelCase							:  Any											=    [self.cls_token_id]
         _UpperCAmelCase							:  Any											=    [self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None       ,	A = False		)							->					List[int]:

         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, 1] + ([0] * len(A		)) + [1]





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					List[int]:
         _UpperCAmelCase							:  Dict											=    [self.sep_token_id]
         _UpperCAmelCase							:  List[str]											=    [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]





     @property
     def  __lowerCAmelCase	(					self		)							->					Dict:
         return len(self.sp_model		) + self.fairseq_offset + 1  # Add the <mask> token





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    {self.convert_ids_to_tokens(A		): i for i in range(self.vocab_size		)}
         vocab.update(self.added_tokens_encoder		)
         return vocab





     def  __lowerCAmelCase	(					self       ,	A		)							->					List[str]:
         return self.sp_model.encode(A       ,	out_type=A		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					Any:
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         _UpperCAmelCase							:  Any											=    self.sp_model.PieceToId(A		)

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset		)





     def  __lowerCAmelCase	(					self       ,	A		)							->					int:
         _UpperCAmelCase							:  str											=    ''''''.join(A		).replace(A       ,	''' '''		).strip()
         return out_string





     def  __lowerCAmelCase	(					self       ,	A       ,	A = None		)							->					Tuple[str]:
         if not os.path.isdir(A		):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
             return
         _UpperCAmelCase							:  List[Any]											=    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:
                 _UpperCAmelCase							:  str											=    self.sp_model.serialized_model_proto()
                 fi.write(A		)

         return (out_vocab_file,)


