


'''simple docstring'''
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   ,   __lowercase   ,   __lowercase=13   ,   __lowercase=[30, 30]   ,   __lowercase=2   ,   __lowercase=3   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=32   ,   __lowercase=5   ,   __lowercase=4   ,   __lowercase=37   ,   __lowercase="gelu"   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=10   ,   __lowercase=0.0_2   ,   __lowercase=3   ,   __lowercase=None   ,   __lowercase=8   ,   __lowercase=10   ,   ):

  """simple docstring"""







  __A	:		List[str]	  =      parent
  __A	:		int	  =      batch_size
  __A	:		int	  =      image_size
  __A	:		List[Any]	  =      patch_size
  __A	:		Tuple	  =      num_channels
  __A	:		Tuple	  =      is_training
  __A	:		List[Any]	  =      use_labels
  __A	:		Dict	  =      hidden_size
  __A	:		Union[str, Any]	  =      num_hidden_layers
  __A	:		List[str]	  =      num_attention_heads
  __A	:		str	  =      intermediate_size
  __A	:		Union[str, Any]	  =      hidden_act
  __A	:		List[str]	  =      hidden_dropout_prob
  __A	:		Optional[int]	  =      attention_probs_dropout_prob
  __A	:		Dict	  =      type_sequence_label_size
  __A	:		List[Any]	  =      initializer_range
  __A	:		Union[str, Any]	  =      num_labels
  __A	:		Union[str, Any]	  =      scope
  __A	:		Any	  =      n_targets
  __A	:		Any	  =      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
  __A	:		List[Any]	  =      (image_size[1] // patch_size) * (image_size[0] // patch_size)
  __A	:		Optional[int]	  =      num_patches + 1 + self.num_detection_tokens


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]    )

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

  __A	:		Optional[int]	  =      self.get_config()

  return config, pixel_values, labels


 def 			snake_case__ (   self    ):

  """simple docstring"""







  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=__lowercase   ,   initializer_range=self.initializer_range   ,   num_detection_tokens=self.num_detection_tokens   ,   num_labels=self.num_labels   ,   )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Tuple	  =      YolosModel(config=__lowercase    )
  model.to(__lowercase    )
  model.eval()
  __A	:		Tuple	  =      model(__lowercase    )
  self.parent.assertEqual(
      result.last_hidden_state.shape   ,   (self.batch_size, self.expected_seq_len, self.hidden_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[str]	  =      YolosForObjectDetection(__lowercase    )
  model.to(__lowercase    )
  model.eval()

  __A	:		List[str]	  =      model(pixel_values=__lowercase    )
  __A	:		Optional[int]	  =      model(__lowercase    )

  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)    )

  __A	:		int	  =      model(pixel_values=__lowercase   ,   labels=__lowercase    )

  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 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		int	  =      self.prepare_config_and_inputs()
  __A  ,__A  ,__A	:		List[str]	  =      config_and_inputs
  __A	:		Dict	  =      {'pixel_values': pixel_values}
  return config, inputs_dict


@require_torch
class     _lowerCamelCase       (     snake_case_     ,		snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[int]     				=    (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
 __lowercase					:		List[Any]     				=    (
     {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
 )

 __lowercase					:		int     				=    False
 __lowercase					:		Union[str, Any]     				=    False
 __lowercase					:		int     				=    False
 __lowercase					:		Union[str, Any]     				=    False


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase=False    ):

  """simple docstring"""







  __A	:		List[Any]	  =      super()._prepare_for_class(__lowercase   ,   __lowercase   ,   return_labels=__lowercase    )

  if return_labels:
   if model_class.__name__ == "YolosForObjectDetection":
    __A	:		int	  =      []
    for i in range(self.model_tester.batch_size    ):
     __A	:		List[Any]	  =      {}
     __A	:		Union[str, Any]	  =      torch.ones(
         size=(self.model_tester.n_targets,)   ,   device=__lowercase   ,   dtype=torch.long    )
     __A	:		str	  =      torch.ones(
         self.model_tester.n_targets   ,   4   ,   device=__lowercase   ,   dtype=torch.float    )
     labels.append(__lowercase    )
    __A	:		Any	  =      labels

  return inputs_dict


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      YolosModelTester(self    )
  __A	:		str	  =      ConfigTester(self   ,   config_class=__lowercase   ,   has_text_modality=__lowercase   ,   hidden_size=37    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  pass


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		Optional[Any]	  =      self.model_tester.prepare_config_and_inputs_for_common()

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


 def 			snake_case__ (   self    ):

  """simple docstring"""







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

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

   __A	:		Union[str, Any]	  =      ['pixel_values']
   self.assertListEqual(arg_names[:1]   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		str	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A  ,__A	:		Any	  =      self.model_tester.prepare_config_and_inputs_for_common()
  __A	:		Dict	  =      True

  # in YOLOS, the seq_len is different
  __A	:		Optional[int]	  =      self.model_tester.expected_seq_len
  for model_class in self.all_model_classes:
   __A	:		int	  =      True
   __A	:		str	  =      False
   __A	:		Optional[Any]	  =      True
   __A	:		Optional[int]	  =      model_class(__lowercase    )
   model.to(__lowercase    )
   model.eval()
   with torch.no_grad():
    __A	:		int	  =      model(**self._prepare_for_class(__lowercase   ,   __lowercase    )    )
   __A	:		List[Any]	  =      outputs.attentions
   self.assertEqual(len(__lowercase    )   ,   self.model_tester.num_hidden_layers    )

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

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

   # Check attention is always last and order is fine
   __A	:		Optional[int]	  =      True
   __A	:		List[str]	  =      True
   __A	:		Any	  =      model_class(__lowercase    )
   model.to(__lowercase    )
   model.eval()
   with torch.no_grad():
    __A	:		str	  =      model(**self._prepare_for_class(__lowercase   ,   __lowercase    )    )

   __A	:		List[str]	  =      1
   self.assertEqual(out_len + added_hidden_states   ,   len(__lowercase    )    )

   __A	:		List[Any]	  =      outputs.attentions

   self.assertEqual(len(__lowercase    )   ,   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 			snake_case__ (   self    ):

  """simple docstring"""







  def check_hidden_states_output(__lowercase   ,   __lowercase   ,   __lowercase    ):
   __A	:		List[Any]	  =      model_class(__lowercase    )
   model.to(__lowercase    )
   model.eval()

   with torch.no_grad():
    __A	:		Optional[int]	  =      model(**self._prepare_for_class(__lowercase   ,   __lowercase    )    )

   __A	:		List[Any]	  =      outputs.hidden_states

   __A	:		List[Any]	  =      getattr(
       self.model_tester   ,   'expected_num_hidden_layers'   ,   self.model_tester.num_hidden_layers + 1    )
   self.assertEqual(len(__lowercase    )   ,   __lowercase    )

   # YOLOS has a different seq_length
   __A	:		Dict	  =      self.model_tester.expected_seq_len

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

  __A  ,__A	:		Optional[int]	  =      self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __A	:		Tuple	  =      True
   check_hidden_states_output(__lowercase   ,   __lowercase   ,   __lowercase    )

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   __A	:		str	  =      True

   check_hidden_states_output(__lowercase   ,   __lowercase   ,   __lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_object_detection(*__lowercase    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __A	:		List[str]	  =      YolosModel.from_pretrained(__lowercase    )
   self.assertIsNotNone(__lowercase    )


def       _lowercase							(					):
 __A	:		Tuple	  =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
 return image


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




 '''simple docstring'''


 @cached_property
 def 			snake_case__ (   self    ):

  """simple docstring"""







  return AutoImageProcessor.from_pretrained('hustvl/yolos-small'    ) if is_vision_available() else None







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      YolosForObjectDetection.from_pretrained('hustvl/yolos-small'    ).to(__lowercase    )

  __A	:		Dict	  =      self.default_image_processor
  __A	:		List[str]	  =      prepare_img()
  __A	:		List[Any]	  =      image_processor(images=__lowercase   ,   return_tensors='pt'    ).to(__lowercase    )

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

  # verify outputs
  __A	:		Dict	  =      torch.Size((1, 100, 92)    )
  self.assertEqual(outputs.logits.shape   ,   __lowercase    )

  __A	:		Optional[Any]	  =      torch.tensor(
      [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]   ,   device=__lowercase   ,   )
  __A	:		List[str]	  =      torch.tensor(
      [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]   ,   device=__lowercase    )
  self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]   ,   __lowercase   ,   atol=1E-4    )    )
  self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]   ,   __lowercase   ,   atol=1E-4    )    )

  # verify postprocessing
  __A	:		int	  =      image_processor.post_process_object_detection(
      __lowercase   ,   threshold=0.3   ,   target_sizes=[image.size[::-1]]    )[0]
  __A	:		List[Any]	  =      torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]    ).to(__lowercase    )
  __A	:		Union[str, Any]	  =      [75, 75, 17, 63, 17]
  __A	:		Any	  =      torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]    ).to(__lowercase    )

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










'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


UpperCAmelCase_			:  int										=   logging.get_logger(__name__)

UpperCAmelCase_			:  int										=   {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase_			:  List[str]										=   {
    'vocab_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
        ),
        'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
        'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
        'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
    },
    'tokenizer_file': {
        'google/realm-cc-news-pretrained-embedder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
        ),
        'google/realm-cc-news-pretrained-encoder': (
            'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-scorer': (
            'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
        ),
        'google/realm-cc-news-pretrained-openqa': (
            'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-openqa': (
            'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-nq-reader': (
            'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-openqa': (
            'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
        ),
        'google/realm-orqa-wq-reader': (
            'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_			:  str										=   {
    'google/realm-cc-news-pretrained-embedder': 5_1_2,
    'google/realm-cc-news-pretrained-encoder': 5_1_2,
    'google/realm-cc-news-pretrained-scorer': 5_1_2,
    'google/realm-cc-news-pretrained-openqa': 5_1_2,
    'google/realm-orqa-nq-openqa': 5_1_2,
    'google/realm-orqa-nq-reader': 5_1_2,
    'google/realm-orqa-wq-openqa': 5_1_2,
    'google/realm-orqa-wq-reader': 5_1_2,
}

UpperCAmelCase_			:  str										=   {
    'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
    'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-openqa': {'do_lower_case': True},
    'google/realm-orqa-nq-reader': {'do_lower_case': True},
    'google/realm-orqa-wq-openqa': {'do_lower_case': True},
    'google/realm-orqa-wq-reader': {'do_lower_case': True},
}

class     _lowerCamelCase       (     snake_case_					):




 '''simple docstring'''
 __lowercase					:		Union[str, Any]     				=    VOCAB_FILES_NAMES
 __lowercase					:		int     				=    PRETRAINED_VOCAB_FILES_MAP
 __lowercase					:		Union[str, Any]     				=    PRETRAINED_INIT_CONFIGURATION
 __lowercase					:		Union[str, Any]     				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowercase					:		List[str]     				=    RealmTokenizer


 def __init__(   self   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=True   ,   __lowercase="[UNK]"   ,   __lowercase="[SEP]"   ,   __lowercase="[PAD]"   ,   __lowercase="[CLS]"   ,   __lowercase="[MASK]"   ,   __lowercase=True   ,   __lowercase=None   ,   **__lowercase   ,   ):

  """simple docstring"""







  super().__init__(
      __lowercase   ,   tokenizer_file=__lowercase   ,   do_lower_case=__lowercase   ,   unk_token=__lowercase   ,   sep_token=__lowercase   ,   pad_token=__lowercase   ,   cls_token=__lowercase   ,   mask_token=__lowercase   ,   tokenize_chinese_chars=__lowercase   ,   strip_accents=__lowercase   ,   **__lowercase   ,   )

  __A	:		Optional[int]	  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
  if (
      normalizer_state.get('lowercase'   ,   __lowercase    ) != do_lower_case
      or normalizer_state.get('strip_accents'   ,   __lowercase    ) != strip_accents
      or normalizer_state.get('handle_chinese_chars'   ,   __lowercase    ) != tokenize_chinese_chars
  ):
   __A	:		Tuple	  =      getattr(__lowercase   ,   normalizer_state.pop('type'    )    )
   __A	:		Optional[int]	  =      do_lower_case
   __A	:		List[str]	  =      strip_accents
   __A	:		Dict	  =      tokenize_chinese_chars
   __A	:		List[Any]	  =      normalizer_class(**__lowercase    )

  __A	:		int	  =      do_lower_case


 def 			snake_case__ (   self   ,   __lowercase   ,   **__lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      PaddingStrategy.MAX_LENGTH

  __A	:		Optional[int]	  =      text
  __A	:		Union[str, Any]	  =      kwargs.pop('text_pair'   ,   __lowercase    )
  __A	:		List[str]	  =      kwargs.pop('return_tensors'   ,   __lowercase    )

  __A	:		int	  =      {
      'input_ids': [],
      'attention_mask': [],
      'token_type_ids': [],
  }

  for idx, candidate_text in enumerate(__lowercase    ):
   if batch_text_pair is not None:
    __A	:		Tuple	  =      batch_text_pair[idx]
   else:
    __A	:		Union[str, Any]	  =      None

   __A	:		Optional[int]	  =      super().__call__(__lowercase   ,   __lowercase   ,   return_tensors=__lowercase   ,   **__lowercase    )

   __A	:		str	  =      encoded_candidates.get('input_ids'    )
   __A	:		Union[str, Any]	  =      encoded_candidates.get('attention_mask'    )
   __A	:		Optional[int]	  =      encoded_candidates.get('token_type_ids'    )

   if encoded_input_ids is not None:
    output_data["input_ids"].append(__lowercase    )
   if encoded_attention_mask is not None:
    output_data["attention_mask"].append(__lowercase    )
   if encoded_token_type_ids is not None:
    output_data["token_type_ids"].append(__lowercase    )

  __A	:		List[str]	  =      {key: item for key, item in output_data.items() if len(__lowercase    ) != 0}

  return BatchEncoding(__lowercase   ,   tensor_type=__lowercase    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase=None    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

  if token_ids_a:
   output += token_ids_a + [self.sep_token_id]

  return output


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      [self.sep_token_id]
  __A	:		Tuple	  =      [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 			snake_case__ (   self   ,   __lowercase   ,   __lowercase = None    ):

  """simple docstring"""







  __A	:		int	  =      self._tokenizer.model.save(__lowercase   ,   name=__lowercase    )
  return tuple(__lowercase    )






