


"""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 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[str]=7    , UpperCamelCase_     :    Optional[Any]=3    , UpperCamelCase_     :    List[Any]=3_0    , UpperCamelCase_     :    int=4_0_0    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    Dict=None    , UpperCamelCase_     :    Optional[Any]=True    , UpperCamelCase_     :    Any=[0.5, 0.5, 0.5]    , UpperCamelCase_     :    Dict=[0.5, 0.5, 0.5]    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Optional[int]=1 / 2_5_5    , UpperCamelCase_     :    List[Any]=True    , ):
           lowerCAmelCase    :		List[Any]				=      size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
           lowerCAmelCase    :		int				=      parent
           lowerCAmelCase    :		Any				=      batch_size
           lowerCAmelCase    :		Tuple				=      num_channels
           lowerCAmelCase    :		int				=      min_resolution
           lowerCAmelCase    :		int				=      max_resolution
           lowerCAmelCase    :		Dict				=      do_resize
           lowerCAmelCase    :		int				=      size
           lowerCAmelCase    :		Dict				=      do_normalize
           lowerCAmelCase    :		int				=      image_mean
           lowerCAmelCase    :		str				=      image_std
           lowerCAmelCase    :		Dict				=      do_rescale
           lowerCAmelCase    :		Optional[int]				=      rescale_factor
           lowerCAmelCase    :		Any				=      do_pad






     def    lowerCamelCase__   (		self     :    Tuple ):
           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     :    int    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    str=False ):


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

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

           return expected_height, expected_width



@require_torch
@require_vision
class 						snake_case_(				__snake_case							,    unittest.TestCase	):
     __UpperCamelCase     =    YolosImageProcessor if is_vision_available() else None






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      YolosImageProcessingTester(self )






     @property
     def    lowerCamelCase__   (		self     :    str ):
           return self.image_processor_tester.prepare_image_processor_dict()






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           lowerCAmelCase    :		str				=      self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase__    , '''image_mean''' ) )
           self.assertTrue(hasattr(UpperCamelCase__    , '''image_std''' ) )
           self.assertTrue(hasattr(UpperCamelCase__    , '''do_normalize''' ) )
           self.assertTrue(hasattr(UpperCamelCase__    , '''do_resize''' ) )
           self.assertTrue(hasattr(UpperCamelCase__    , '''size''' ) )






     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Optional[int]				=      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    , UpperCamelCase__ )

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






     def    lowerCamelCase__   (		self     :    str ):
           pass






     def    lowerCamelCase__   (		self     :    int ):
           lowerCAmelCase    :		int				=      self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           lowerCAmelCase    :		List[str]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase__    , Image.Image )

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

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

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

           # Test batched
           lowerCAmelCase    :		str				=      self.image_processor_tester.get_expected_values(UpperCamelCase__    , batched=UpperCamelCase__ )

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

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

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

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

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

           lowerCAmelCase    :		Optional[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase__    , batched=UpperCamelCase__ )

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






     def    lowerCamelCase__   (		self     :    str ):
           lowerCAmelCase    :		Any				=      self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           lowerCAmelCase    :		int				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase__    , torchify=UpperCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase__    , torch.Tensor )

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

           lowerCAmelCase    :		Union[str, Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase__ )

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

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

           lowerCAmelCase    :		Optional[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase__    , batched=UpperCamelCase__ )

           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] ):
           lowerCAmelCase    :		str				=      self.image_processing_class(**self.image_processor_dict )
           lowerCAmelCase    :		Dict				=      self.image_processing_class(do_resize=UpperCamelCase__    , do_normalize=UpperCamelCase__    , do_rescale=UpperCamelCase__ )
           # create random PyTorch tensors
           lowerCAmelCase    :		Dict				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase__    , torchify=UpperCamelCase__ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase__    , torch.Tensor )

           # Test whether the method "pad" and calling the image processor return the same tensors
           lowerCAmelCase    :		Optional[Any]				=      image_processing_a.pad(UpperCamelCase__    , return_tensors='''pt''' )
           lowerCAmelCase    :		Optional[int]				=      image_processing_a(UpperCamelCase__    , return_tensors='''pt''' )

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






     @slow
     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Union[str, Any]				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''    , '''r''' ) as f:
                 lowerCAmelCase    :		Any				=      json.loads(f.read() )

           lowerCAmelCase    :		Any				=      {"image_id": 3_9_7_6_9, "annotations": target}

           # encode them
           lowerCAmelCase    :		Tuple				=      YolosImageProcessor.from_pretrained('''hustvl/yolos-small''' )
           lowerCAmelCase    :		Dict				=      image_processing(images=UpperCamelCase__    , annotations=UpperCamelCase__    , return_tensors='''pt''' )

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

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

           # verify area
           lowerCAmelCase    :		List[str]				=      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''']    , UpperCamelCase__ ) )
           # verify boxes
           lowerCAmelCase    :		Optional[int]				=      torch.Size([6, 4] )
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    , UpperCamelCase__ )
           lowerCAmelCase    :		Dict				=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    , UpperCamelCase__    , atol=1E-3 ) )
           # verify image_id
           lowerCAmelCase    :		Tuple				=      torch.tensor([3_9_7_6_9] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    , UpperCamelCase__ ) )
           # verify is_crowd
           lowerCAmelCase    :		Any				=      torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    , UpperCamelCase__ ) )
           # verify class_labels
           lowerCAmelCase    :		Tuple				=      torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    , UpperCamelCase__ ) )
           # verify orig_size
           lowerCAmelCase    :		Optional[Any]				=      torch.tensor([4_8_0, 6_4_0] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    , UpperCamelCase__ ) )
           # verify size
           lowerCAmelCase    :		int				=      torch.tensor([8_0_0, 1_0_6_6] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    , UpperCamelCase__ ) )






     @slow
     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		List[str]				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
           with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''    , '''r''' ) as f:
                 lowerCAmelCase    :		Any				=      json.loads(f.read() )

           lowerCAmelCase    :		Any				=      {"file_name": "000000039769.png", "image_id": 3_9_7_6_9, "segments_info": target}

           lowerCAmelCase    :		Dict				=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''' )

           # encode them
           lowerCAmelCase    :		Dict				=      YolosImageProcessor(format='''coco_panoptic''' )
           lowerCAmelCase    :		List[Any]				=      image_processing(images=UpperCamelCase__    , annotations=UpperCamelCase__    , masks_path=UpperCamelCase__    , return_tensors='''pt''' )

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

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

           # verify area
           lowerCAmelCase    :		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''']    , UpperCamelCase__ ) )
           # verify boxes
           lowerCAmelCase    :		int				=      torch.Size([6, 4] )
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape    , UpperCamelCase__ )
           lowerCAmelCase    :		List[Any]				=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]    , UpperCamelCase__    , atol=1E-3 ) )
           # verify image_id
           lowerCAmelCase    :		Any				=      torch.tensor([3_9_7_6_9] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']    , UpperCamelCase__ ) )
           # verify is_crowd
           lowerCAmelCase    :		Tuple				=      torch.tensor([0, 0, 0, 0, 0, 0] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']    , UpperCamelCase__ ) )
           # verify class_labels
           lowerCAmelCase    :		List[Any]				=      torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']    , UpperCamelCase__ ) )
           # verify masks
           lowerCAmelCase    :		List[Any]				=      8_2_2_8_7_3
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()    , UpperCamelCase__ )
           # verify orig_size
           lowerCAmelCase    :		Dict				=      torch.tensor([4_8_0, 6_4_0] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']    , UpperCamelCase__ ) )
           # verify size
           lowerCAmelCase    :		Union[str, Any]				=      torch.tensor([8_0_0, 1_0_6_6] )
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']    , UpperCamelCase__ ) )





"""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


__A						   =			logging.get_logger(__name__)

__A						   =			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "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"
        ),
    },
}

__A						   =			{
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      str         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      Any         =  RobertaTokenizer

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



						'''simple docstring'''


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

						__lowerCAmelCase:       Tuple          =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
								__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  pre_tok_state.pop("type"))
								__lowerCAmelCase:       Optional[int]          =			add_prefix_space
								__lowerCAmelCase:       Dict          =			pre_tok_class(**UpperCamelCase__)

						__lowerCAmelCase:       Any          =			add_prefix_space

						__lowerCAmelCase:       int          =			"post_processor"
						__lowerCAmelCase:       Optional[Any]          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       Dict          =			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:
										__lowerCAmelCase:       List[Any]          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       str          =			tuple(state["cls"])

								__lowerCAmelCase:       str          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Optional[Any]          =			add_prefix_space
										__lowerCAmelCase:       List[str]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Any          =			trim_offsets
										__lowerCAmelCase:       List[str]          =			True

								if changes_to_apply:
										__lowerCAmelCase:       str          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       List[str]          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				def 							lowercase_     (	self							:				List[str])->   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							:				Tuple     ,  UpperCamelCase__							:				Optional[int])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       int          =			value

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				str     ,  **UpperCamelCase__							:				List[Any])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				int     ,  *UpperCamelCase__							:				Dict     ,  **UpperCamelCase__							:				List[str])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				Any     ,  UpperCamelCase__							:				Union[str, Any]=None)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[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							:				int     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[Any]          =			[self.sep_token_id]
						__lowerCAmelCase:       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]


