






import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
   from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor





class 	_lowercase		:

 def __init__(				self			:							Tuple     ,   snake_case			:							List[str]     ,   snake_case			:							Optional[Any]=2     ,   snake_case			:							Dict=3_2     ,   snake_case			:							Optional[Any]=1_6     ,   snake_case			:							Tuple=3     ,   snake_case			:							Optional[int]=True     ,   snake_case			:							Any=True     ,   snake_case			:							Union[str, Any]=3_2     ,   snake_case			:							List[Any]=4     ,   snake_case			:							Dict=[0, 1, 2, 3]     ,   snake_case			:							Optional[Any]=4     ,   snake_case			:							Any=3_7     ,   snake_case			:							Dict="gelu"     ,   snake_case			:							Dict=0.1     ,   snake_case			:							List[Any]=0.1     ,   snake_case			:							int=0.02     ,   snake_case			:							Any=3     ,   snake_case			:							Optional[int]=False     ,   snake_case			:							Optional[Any]=None     ,   )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      parent
  UpperCamelCase_      :		List[Any]				=      batch_size
  UpperCamelCase_      :		Optional[Any]				=      image_size
  UpperCamelCase_      :		Dict				=      patch_size
  UpperCamelCase_      :		str				=      num_channels
  UpperCamelCase_      :		Union[str, Any]				=      is_training
  UpperCamelCase_      :		Dict				=      use_labels
  UpperCamelCase_      :		str				=      hidden_size
  UpperCamelCase_      :		Dict				=      num_hidden_layers
  UpperCamelCase_      :		Dict				=      backbone_out_indices
  UpperCamelCase_      :		Optional[Any]				=      num_attention_heads
  UpperCamelCase_      :		List[str]				=      intermediate_size
  UpperCamelCase_      :		Dict				=      hidden_act
  UpperCamelCase_      :		Any				=      hidden_dropout_prob
  UpperCamelCase_      :		Any				=      attention_probs_dropout_prob
  UpperCamelCase_      :		Optional[int]				=      initializer_range
  UpperCamelCase_      :		List[str]				=      num_labels
  UpperCamelCase_      :		Tuple				=      scope
  UpperCamelCase_      :		Tuple				=      is_hybrid
  # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
  UpperCamelCase_      :		Tuple				=      (image_size // patch_size) ** 2
  UpperCamelCase_      :		List[str]				=      num_patches + 1

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

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

  UpperCamelCase_      :		Union[str, Any]				=      None
  if self.use_labels:
   UpperCamelCase_      :		Any				=      ids_tensor([self.batch_size, self.image_size, self.image_size]     ,   self.num_labels    )

  UpperCamelCase_      :		Union[str, Any]				=      self.get_config()

  return config, pixel_values, labels

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

  """simple docstring"""





  return DPTConfig(
      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     ,   backbone_out_indices=self.backbone_out_indices     ,   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=snake_case     ,   initializer_range=self.initializer_range     ,   is_hybrid=self.is_hybrid     ,   )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   snake_case			:							List[Any]     ,   snake_case			:							Tuple     ,   snake_case			:							int    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		int				=      DPTModel(config=snake_case    )
  model.to(snake_case    )
  model.eval()
  UpperCamelCase_      :		List[str]				=      model(snake_case    )
  self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, self.seq_length, self.hidden_size)    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]     ,   snake_case			:							List[str]     ,   snake_case			:							Dict     ,   snake_case			:							Tuple    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      self.num_labels
  UpperCamelCase_      :		Union[str, Any]				=      DPTForDepthEstimation(snake_case    )
  model.to(snake_case    )
  model.eval()
  UpperCamelCase_      :		List[Any]				=      model(snake_case    )
  self.parent.assertEqual(result.predicted_depth.shape     ,   (self.batch_size, self.image_size, self.image_size)    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							str     ,   snake_case			:							Optional[int]     ,   snake_case			:							List[str]    )     ->		Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      self.num_labels
  UpperCamelCase_      :		List[str]				=      DPTForSemanticSegmentation(snake_case    )
  model.to(snake_case    )
  model.eval()
  UpperCamelCase_      :		Optional[int]				=      model(snake_case     ,   labels=snake_case    )
  self.parent.assertEqual(
      result.logits.shape     ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.prepare_config_and_inputs()
  UpperCamelCase_      :		Union[str, Any]				=      config_and_inputs
  UpperCamelCase_      :		Optional[int]				=      {'pixel_values': pixel_values}
  return config, inputs_dict





@require_torch
class 	_lowercase		(      snake_case_		,   snake_case_		,   unittest.TestCase ):
 lowercase         =     (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 lowercase         =     (
     {
         'depth-estimation': DPTForDepthEstimation,
         'feature-extraction': DPTModel,
         'image-segmentation': DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 lowercase         =     False
 lowercase         =     False
 lowercase         =     False

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      DPTModelTester(self    )
  UpperCamelCase_      :		str				=      ConfigTester(self     ,   config_class=snake_case     ,   has_text_modality=snake_case     ,   hidden_size=3_7    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		List[Any]:

  """simple docstring"""





  self.config_tester.run_common_tests()

 @unittest.skip(reason='DPT does not use inputs_embeds'    )
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		int:

  """simple docstring"""





  pass

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs_for_common()

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

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

  """simple docstring"""





  UpperCamelCase_      :		int				=      self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   UpperCamelCase_      :		List[Any]				=      model_class(snake_case    )
   UpperCamelCase_      :		List[str]				=      inspect.signature(model.forward    )
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   UpperCamelCase_      :		Tuple				=      [*signature.parameters.keys()]

   UpperCamelCase_      :		Optional[Any]				=      ['pixel_values']
   self.assertListEqual(arg_names[:1]     ,   snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		Optional[Any]				=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		str				=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_depth_estimation(*snake_case    )

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

  """simple docstring"""





  UpperCamelCase_      :		int				=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_semantic_segmentation(*snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		int:

  """simple docstring"""





  for model_class in self.all_model_classes:
   if model_class.__name__ == "DPTForDepthEstimation":
    continue

   UpperCamelCase_      :		Any				=      self.model_tester.prepare_config_and_inputs_for_common()
   UpperCamelCase_      :		Any				=      True

   if model_class in get_values(snake_case    ):
    continue

   UpperCamelCase_      :		Union[str, Any]				=      model_class(snake_case    )
   model.to(snake_case    )
   model.train()
   UpperCamelCase_      :		Union[str, Any]				=      self._prepare_for_class(snake_case     ,   snake_case     ,   return_labels=snake_case    )
   UpperCamelCase_      :		Tuple				=      model(**snake_case    ).loss
   loss.backward()

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

  """simple docstring"""





  for model_class in self.all_model_classes:
   if model_class.__name__ == "DPTForDepthEstimation":
    continue

   UpperCamelCase_      :		int				=      self.model_tester.prepare_config_and_inputs_for_common()
   UpperCamelCase_      :		Union[str, Any]				=      False
   UpperCamelCase_      :		List[str]				=      True

   if model_class in get_values(snake_case    ) or not model_class.supports_gradient_checkpointing:
    continue
   UpperCamelCase_      :		int				=      model_class(snake_case    )
   model.to(snake_case    )
   model.gradient_checkpointing_enable()
   model.train()
   UpperCamelCase_      :		str				=      self._prepare_for_class(snake_case     ,   snake_case     ,   return_labels=snake_case    )
   UpperCamelCase_      :		int				=      model(**snake_case    ).loss
   loss.backward()

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

  """simple docstring"""





  UpperCamelCase_      :		Any				=      self.model_tester.prepare_config_and_inputs_for_common()

  UpperCamelCase_      :		Any				=      _config_zero_init(snake_case    )
  for model_class in self.all_model_classes:
   UpperCamelCase_      :		Tuple				=      model_class(config=snake_case    )
   # Skip the check for the backbone
   UpperCamelCase_      :		int				=      []
   for name, module in model.named_modules():
    if module.__class__.__name__ == "DPTViTHybridEmbeddings":
     UpperCamelCase_      :		str				=      [f"{name}.{key}" for key in module.state_dict().keys()]
     break

   for name, param in model.named_parameters():
    if param.requires_grad:
     if name in backbone_params:
      continue
     self.assertIn(
         ((param.data.mean() * 1e9).round() / 1e9).item()     ,   [0.0, 1.0]     ,   msg=f"Parameter {name} of model {model_class} seems not properly initialized"     ,   )

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  pass


 @slow
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Union[str, Any]    )     ->		Union[str, Any]:

  """simple docstring"""





  for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   UpperCamelCase_      :		Tuple				=      DPTModel.from_pretrained(snake_case    )
   self.assertIsNotNone(snake_case    )





def      __lowercase  (		):
 UpperCamelCase_      :		Optional[int]				=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
 return image






@require_torch
@require_vision
@slow
class 	_lowercase		(      unittest.TestCase ):

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

  """simple docstring"""





  UpperCamelCase_      :		int				=      DPTImageProcessor.from_pretrained('Intel/dpt-large'    )
  UpperCamelCase_      :		Union[str, Any]				=      DPTForDepthEstimation.from_pretrained('Intel/dpt-large'    ).to(snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      prepare_img()
  UpperCamelCase_      :		Optional[Any]				=      image_processor(images=snake_case     ,   return_tensors='pt'    ).to(snake_case    )

  # forward pass
  with torch.no_grad():
   UpperCamelCase_      :		List[str]				=      model(**snake_case    )
   UpperCamelCase_      :		List[str]				=      outputs.predicted_depth

  # verify the predicted depth
  UpperCamelCase_      :		Union[str, Any]				=      torch.Size((1, 3_8_4, 3_8_4)    )
  self.assertEqual(predicted_depth.shape     ,   snake_case    )

  UpperCamelCase_      :		Optional[Any]				=      torch.tensor(
      [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]    ).to(snake_case    )

  self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]     ,   snake_case     ,   atol=1e-4    )    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
  UpperCamelCase_      :		List[Any]				=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(snake_case    )

  UpperCamelCase_      :		List[str]				=      prepare_img()
  UpperCamelCase_      :		Any				=      image_processor(images=snake_case     ,   return_tensors='pt'    ).to(snake_case    )

  # forward pass
  with torch.no_grad():
   UpperCamelCase_      :		int				=      model(**snake_case    )

  # verify the logits
  UpperCamelCase_      :		Optional[Any]				=      torch.Size((1, 1_5_0, 4_8_0, 4_8_0)    )
  self.assertEqual(outputs.logits.shape     ,   snake_case    )

  UpperCamelCase_      :		List[Any]				=      torch.tensor(
      [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]    ).to(snake_case    )

  self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]     ,   snake_case     ,   atol=1e-4    )    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'    )
  UpperCamelCase_      :		Dict				=      DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'    ).to(snake_case    )

  UpperCamelCase_      :		Dict				=      prepare_img()
  UpperCamelCase_      :		Optional[Any]				=      image_processor(images=snake_case     ,   return_tensors='pt'    ).to(snake_case    )

  # forward pass
  with torch.no_grad():
   UpperCamelCase_      :		int				=      model(**snake_case    )

  UpperCamelCase_      :		str				=      outputs.logits.detach().cpu()

  UpperCamelCase_      :		Optional[Any]				=      image_processor.post_process_semantic_segmentation(outputs=snake_case     ,   target_sizes=[(5_0_0, 3_0_0)]    )
  UpperCamelCase_      :		Optional[Any]				=      torch.Size((5_0_0, 3_0_0)    )
  self.assertEqual(segmentation[0].shape     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      image_processor.post_process_semantic_segmentation(outputs=snake_case    )
  UpperCamelCase_      :		Any				=      torch.Size((4_8_0, 4_8_0)    )
  self.assertEqual(segmentation[0].shape     ,   snake_case    )




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


a_					      = logging.get_logger(__name__)

a_					      = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_					      = {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 lowercase         =     VOCAB_FILES_NAMES
 lowercase         =     PRETRAINED_VOCAB_FILES_MAP
 lowercase         =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase         =     ['input_ids', 'attention_mask', 'token_type_ids']
 lowercase         =     DebertaTokenizer

 def __init__(				self			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [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    ) * [0] + len(token_ids_a + sep    ) * [1]

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  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(*snake_case     ,   **snake_case    )

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

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      []
  for is_user, text in conversation.iter_texts():
   input_ids.extend(self.encode(snake_case     ,   add_special_tokens=snake_case    ) + [self.eos_token_id]    )

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



