




import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import YolosForObjectDetection, YolosModel
     from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	List[str]=13  ,      SCREAMING_SNAKE_CASE__	:	Any=[30, 30]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=2  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]=3  ,      SCREAMING_SNAKE_CASE__	:	int=True  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=True  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=32  ,      SCREAMING_SNAKE_CASE__	:	Optional[int]=5  ,      SCREAMING_SNAKE_CASE__	:	Tuple=4  ,      SCREAMING_SNAKE_CASE__	:	Tuple=37  ,      SCREAMING_SNAKE_CASE__	:	List[Any]="gelu"  ,      SCREAMING_SNAKE_CASE__	:	Optional[Any]=0.1  ,      SCREAMING_SNAKE_CASE__	:	str=0.1  ,      SCREAMING_SNAKE_CASE__	:	List[str]=10  ,      SCREAMING_SNAKE_CASE__	:	Tuple=0.02  ,      SCREAMING_SNAKE_CASE__	:	Dict=3  ,      SCREAMING_SNAKE_CASE__	:	str=None  ,      SCREAMING_SNAKE_CASE__	:	List[Any]=8  ,      SCREAMING_SNAKE_CASE__	:	Any=10  ,      ) ->			Tuple:
         lowerCAmelCase__            =					parent
         lowerCAmelCase__            =					batch_size
         lowerCAmelCase__            =					image_size
         lowerCAmelCase__            =					patch_size
         lowerCAmelCase__            =					num_channels
         lowerCAmelCase__            =					is_training
         lowerCAmelCase__            =					use_labels
         lowerCAmelCase__            =					hidden_size
         lowerCAmelCase__            =					num_hidden_layers
         lowerCAmelCase__            =					num_attention_heads
         lowerCAmelCase__            =					intermediate_size
         lowerCAmelCase__            =					hidden_act
         lowerCAmelCase__            =					hidden_dropout_prob
         lowerCAmelCase__            =					attention_probs_dropout_prob
         lowerCAmelCase__            =					type_sequence_label_size
         lowerCAmelCase__            =					initializer_range
         lowerCAmelCase__            =					num_labels
         lowerCAmelCase__            =					scope
         lowerCAmelCase__            =					n_targets
         lowerCAmelCase__            =					num_detection_tokens
         # we set the expected sequence length (which is used in several tests)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
         lowerCAmelCase__            =					(image_size[1] // patch_size) * (image_size[0] // patch_size)
         lowerCAmelCase__            =					num_patches + 1 + self.num_detection_tokens


    def   a (			self	:	Dict  ) ->			Tuple:
         lowerCAmelCase__            =					floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]  )

         lowerCAmelCase__            =					None
         if self.use_labels:
              # labels is a list of Dict (each Dict being the labels for a given example in the batch)
              lowerCAmelCase__            =					[]
              for i in range(self.batch_size  ):
                   lowerCAmelCase__            =					{}
                   lowerCAmelCase__            =					torch.randint(
                       high=self.num_labels  ,      size=(self.n_targets,)  ,      device=SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					torch.rand(self.n_targets  ,      4  ,      device=SCREAMING_SNAKE_CASE__  )
                   labels.append(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.get_config()

         return config, pixel_values, labels


    def   a (			self	:	List[str]  ) ->			List[str]:
         return YolosConfig(
             image_size=self.image_size  ,      patch_size=self.patch_size  ,      num_channels=self.num_channels  ,      hidden_size=self.hidden_size  ,      num_hidden_layers=self.num_hidden_layers  ,      num_attention_heads=self.num_attention_heads  ,      intermediate_size=self.intermediate_size  ,      hidden_act=self.hidden_act  ,      hidden_dropout_prob=self.hidden_dropout_prob  ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,      is_decoder=SCREAMING_SNAKE_CASE__  ,      initializer_range=self.initializer_range  ,      num_detection_tokens=self.num_detection_tokens  ,      num_labels=self.num_labels  ,      )


    def   a (			self	:	int  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	Dict  ) ->			Optional[int]:
         lowerCAmelCase__            =					YolosModel(config=SCREAMING_SNAKE_CASE__  )
         model.to(SCREAMING_SNAKE_CASE__  )
         model.eval()
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )
         self.parent.assertEqual(
             result.last_hidden_state.shape  ,      (self.batch_size, self.expected_seq_len, self.hidden_size)  )


    def   a (			self	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Tuple  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	str  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					YolosForObjectDetection(SCREAMING_SNAKE_CASE__  )
         model.to(SCREAMING_SNAKE_CASE__  )
         model.eval()

         lowerCAmelCase__            =					model(pixel_values=SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					model(SCREAMING_SNAKE_CASE__  )

         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
         self.parent.assertEqual(result.pred_boxes.shape  ,      (self.batch_size, self.num_detection_tokens, 4)  )

         lowerCAmelCase__            =					model(pixel_values=SCREAMING_SNAKE_CASE__  ,      labels=SCREAMING_SNAKE_CASE__  )

         self.parent.assertEqual(result.loss.shape  ,      ()  )
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_detection_tokens, self.num_labels + 1)  )
         self.parent.assertEqual(result.pred_boxes.shape  ,      (self.batch_size, self.num_detection_tokens, 4)  )


    def   a (			self	:	Optional[Any]  ) ->			Any:
         lowerCAmelCase__            =					self.prepare_config_and_inputs()
         lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__            =					config_and_inputs
         lowerCAmelCase__            =					{"pixel_values": pixel_values}
         return config, inputs_dict






@require_torch
class 					__lowerCamelCase			(       UpperCamelCase__      ,							UpperCamelCase__      ,							unittest.TestCase       ):




    """simple docstring"""

    snake_case__   = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
    snake_case__   = (
        {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
    )

    snake_case__   = False
    snake_case__   = False
    snake_case__   = False
    snake_case__   = False


    def   a (			self	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	int  ,      SCREAMING_SNAKE_CASE__	:	List[str]  ,      SCREAMING_SNAKE_CASE__	:	Dict=False  ) ->			Dict:
         lowerCAmelCase__            =					super()._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      return_labels=SCREAMING_SNAKE_CASE__  )

         if return_labels:
              if model_class.__name__ == "YolosForObjectDetection":
                   lowerCAmelCase__            =					[]
                   for i in range(self.model_tester.batch_size  ):
                        lowerCAmelCase__            =					{}
                        lowerCAmelCase__            =					torch.ones(
                            size=(self.model_tester.n_targets,)  ,      device=SCREAMING_SNAKE_CASE__  ,      dtype=torch.long  )
                        lowerCAmelCase__            =					torch.ones(
                            self.model_tester.n_targets  ,      4  ,      device=SCREAMING_SNAKE_CASE__  ,      dtype=torch.float  )
                        labels.append(SCREAMING_SNAKE_CASE__  )
                   lowerCAmelCase__            =					labels

         return inputs_dict


    def   a (			self	:	Tuple  ) ->			Dict:
         lowerCAmelCase__            =					YolosModelTester(self  )
         lowerCAmelCase__            =					ConfigTester(self  ,      config_class=SCREAMING_SNAKE_CASE__  ,      has_text_modality=SCREAMING_SNAKE_CASE__  ,      hidden_size=37  )


    def   a (			self	:	List[str]  ) ->			Tuple:
         self.config_tester.run_common_tests()


    def   a (			self	:	int  ) ->			List[str]:
         # YOLOS does not use inputs_embeds
         pass


    def   a (			self	:	Optional[Any]  ) ->			Any:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              self.assertIsInstance(model.get_input_embeddings()  ,      (nn.Module)  )
              lowerCAmelCase__            =					model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE__  ,      nn.Linear  )  )


    def   a (			self	:	List[str]  ) ->			Optional[Any]:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              lowerCAmelCase__            =					inspect.signature(model.forward  )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowerCAmelCase__            =					[*signature.parameters.keys()]

              lowerCAmelCase__            =					["pixel_values"]
              self.assertListEqual(arg_names[:1]  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	Union[str, Any]  ) ->			int:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			Dict:
         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()
         lowerCAmelCase__            =					True

         # in YOLOS, the seq_len is different
         lowerCAmelCase__            =					self.model_tester.expected_seq_len
         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					False
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              model.to(SCREAMING_SNAKE_CASE__  )
              model.eval()
              with torch.no_grad():
                   lowerCAmelCase__            =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )  )
              lowerCAmelCase__            =					outputs.attentions
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      self.model_tester.num_hidden_layers  )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              model.to(SCREAMING_SNAKE_CASE__  )
              model.eval()
              with torch.no_grad():
                   lowerCAmelCase__            =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )  )
              lowerCAmelCase__            =					outputs.attentions
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      self.model_tester.num_hidden_layers  )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]  )  ,      [self.model_tester.num_attention_heads, seq_len, seq_len]  ,      )
              lowerCAmelCase__            =					len(SCREAMING_SNAKE_CASE__  )

              # Check attention is always last and order is fine
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					True
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              model.to(SCREAMING_SNAKE_CASE__  )
              model.eval()
              with torch.no_grad():
                   lowerCAmelCase__            =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )  )

              lowerCAmelCase__            =					1
              self.assertEqual(out_len + added_hidden_states  ,      len(SCREAMING_SNAKE_CASE__  )  )

              lowerCAmelCase__            =					outputs.attentions

              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      self.model_tester.num_hidden_layers  )
              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]  )  ,      [self.model_tester.num_attention_heads, seq_len, seq_len]  ,      )


    def   a (			self	:	Optional[Any]  ) ->			Any:
         def check_hidden_states_output(SCREAMING_SNAKE_CASE__	:	List[Any]  ,      SCREAMING_SNAKE_CASE__	:	Union[str, Any]  ,      SCREAMING_SNAKE_CASE__	:	int  ):
              lowerCAmelCase__            =					model_class(SCREAMING_SNAKE_CASE__  )
              model.to(SCREAMING_SNAKE_CASE__  )
              model.eval()

              with torch.no_grad():
                   lowerCAmelCase__            =					model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )  )

              lowerCAmelCase__            =					outputs.hidden_states

              lowerCAmelCase__            =					getattr(
                  self.model_tester  ,      "expected_num_hidden_layers"  ,      self.model_tester.num_hidden_layers + 1  )
              self.assertEqual(len(SCREAMING_SNAKE_CASE__  )  ,      SCREAMING_SNAKE_CASE__  )

              # YOLOS has a different seq_length
              lowerCAmelCase__            =					self.model_tester.expected_seq_len

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]  )  ,      [seq_length, self.model_tester.hidden_size]  ,      )

         lowerCAmelCase__      ,						lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowerCAmelCase__            =					True
              check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              lowerCAmelCase__            =					True

              check_hidden_states_output(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  )


    def   a (			self	:	str  ) ->			Union[str, Any]:
         lowerCAmelCase__            =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE__  )


    @slow
    def   a (			self	:	Union[str, Any]  ) ->			Optional[Any]:
         for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowerCAmelCase__            =					YolosModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE__  )






def 				_A  (		):
     """simple docstring"""

     lowerCAmelCase__            =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
     return image






@require_torch
@require_vision
class 					__lowerCamelCase			(       unittest.TestCase       ):




    """simple docstring"""

    @cached_property
    def   a (			self	:	Optional[Any]  ) ->			List[str]:
         return AutoImageProcessor.from_pretrained("hustvl/yolos-small"  ) if is_vision_available() else None


    @slow
    def   a (			self	:	Dict  ) ->			int:
         lowerCAmelCase__            =					YolosForObjectDetection.from_pretrained("hustvl/yolos-small"  ).to(SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					self.default_image_processor
         lowerCAmelCase__            =					prepare_img()
         lowerCAmelCase__            =					image_processor(images=SCREAMING_SNAKE_CASE__  ,      return_tensors="pt"  ).to(SCREAMING_SNAKE_CASE__  )

         # forward pass
         with torch.no_grad():
              lowerCAmelCase__            =					model(inputs.pixel_values  )

         # verify outputs
         lowerCAmelCase__            =					torch.Size((1, 100, 92)  )
         self.assertEqual(outputs.logits.shape  ,      SCREAMING_SNAKE_CASE__  )

         lowerCAmelCase__            =					torch.tensor(
             [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]]  ,      device=SCREAMING_SNAKE_CASE__  ,      )
         lowerCAmelCase__            =					torch.tensor(
             [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]]  ,      device=SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )
         self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )

         # verify postprocessing
         lowerCAmelCase__            =					image_processor.post_process_object_detection(
             SCREAMING_SNAKE_CASE__  ,      threshold=0.3  ,      target_sizes=[image.size[::-1]]  )[0]
         lowerCAmelCase__            =					torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]  ).to(SCREAMING_SNAKE_CASE__  )
         lowerCAmelCase__            =					[75, 75, 17, 63, 17]
         lowerCAmelCase__            =					torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]  ).to(SCREAMING_SNAKE_CASE__  )

         self.assertEqual(len(results["scores"]  )  ,      5  )
         self.assertTrue(torch.allclose(results["scores"]  ,      SCREAMING_SNAKE_CASE__  ,      atol=1e-4  )  )
         self.assertSequenceEqual(results["labels"].tolist()  ,      SCREAMING_SNAKE_CASE__  )
         self.assertTrue(torch.allclose(results["boxes"][0, :]  ,      SCREAMING_SNAKE_CASE__  )  )




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


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

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

snake_case							:       Optional[int]										=	{
    '''roberta-base''': 5_12,
    '''roberta-large''': 5_12,
    '''roberta-large-mnli''': 5_12,
    '''distilroberta-base''': 5_12,
    '''roberta-base-openai-detector''': 5_12,
    '''roberta-large-openai-detector''': 5_12,
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE__ =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE__ =			['input_ids', 'attention_mask']
 SCREAMING_SNAKE_CASE__ =			RobertaTokenizer




 def __init__(						self   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase=None   ,    _lowerCamelCase="replace"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="</s>"   ,    _lowerCamelCase="<s>"   ,    _lowerCamelCase="<unk>"   ,    _lowerCamelCase="<pad>"   ,    _lowerCamelCase="<mask>"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    **_lowerCamelCase   ,    ):
       super().__init__(
           _lowerCamelCase   ,    _lowerCamelCase   ,    tokenizer_file=_lowerCamelCase   ,    errors=_lowerCamelCase   ,    bos_token=_lowerCamelCase   ,    eos_token=_lowerCamelCase   ,    sep_token=_lowerCamelCase   ,    cls_token=_lowerCamelCase   ,    unk_token=_lowerCamelCase   ,    pad_token=_lowerCamelCase   ,    mask_token=_lowerCamelCase   ,    add_prefix_space=_lowerCamelCase   ,    trim_offsets=_lowerCamelCase   ,    **_lowerCamelCase   ,    )

       a		:List[Any]									=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
       if pre_tok_state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
             a		:Optional[int]									=			getattr(_lowerCamelCase   ,    pre_tok_state.pop('''type'''       )       )
             a		:Union[str, Any]									=			add_prefix_space
             a		:Dict									=			pre_tok_class(**_lowerCamelCase       )

       a		:Union[str, Any]									=			add_prefix_space

       a		:List[Any]									=			'''post_processor'''
       a		:List[str]									=			getattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )
       if tokenizer_component_instance:
             a		:Optional[Any]									=			json.loads(tokenizer_component_instance.__getstate__()       )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                   a		:Optional[int]									=			tuple(state['''sep''']       )
             if "cls" in state:
                   a		:Union[str, Any]									=			tuple(state['''cls''']       )

             a		:Optional[int]									=			False

             if state.get('''add_prefix_space'''   ,    _lowerCamelCase       ) != add_prefix_space:
                   a		:int									=			add_prefix_space
                   a		:Union[str, Any]									=			True

             if state.get('''trim_offsets'''   ,    _lowerCamelCase       ) != trim_offsets:
                   a		:int									=			trim_offsets
                   a		:List[str]									=			True

             if changes_to_apply:
                   a		:Union[str, Any]									=			getattr(_lowerCamelCase   ,    state.pop('''type'''       )       )
                   a		:str									=			component_class(**_lowerCamelCase       )
                   setattr(self.backend_tokenizer   ,    _lowerCamelCase   ,    _lowerCamelCase       )




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:Union[str, Any]									=			AddedToken(_lowerCamelCase   ,    lstrip=_lowerCamelCase   ,    rstrip=_lowerCamelCase       ) if isinstance(_lowerCamelCase   ,    _lowerCamelCase       ) else value
       a		:int									=			value




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Union[str, Any]									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )
       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(*_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    *_lowerCamelCase   ,    **_lowerCamelCase       ):
       a		:Any									=			kwargs.get('''is_split_into_words'''   ,    _lowerCamelCase       )

       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(*_lowerCamelCase   ,    **_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Any									=			self._tokenizer.model.save(_lowerCamelCase   ,    name=_lowerCamelCase       )
       return tuple(_lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=None       ):
       a		:Union[str, Any]									=			[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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase   ,    _lowerCamelCase = None       ):
       a		:Dict									=			[self.sep_token_id]
       a		:int									=			[self.cls_token_id]

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





