code: 
  
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  import inspect
  import unittest
  
  from transformers import MobileViTConfig
  from transformers.file_utils import is_tf_available, is_vision_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
   import numpy as np
   import tensorflow as tf
  
   from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
   from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import MobileViTImageProcessor
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             _UpperCAmelCase    :			List[str]              =					self.config_class(**self.inputs_dict		)
             self.parent.assertTrue(hasattr(A  ,			"hidden_sizes"		)		)
             self.parent.assertTrue(hasattr(A  ,			"neck_hidden_sizes"		)		)
             self.parent.assertTrue(hasattr(A  ,			"num_attention_heads"		)		)
  
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
        def __init__(  self  :			Optional[Any]  ,			A  :			Union[str, Any]  ,			A  :			Optional[Any]=1_3  ,			A  :			Dict=3_2  ,			A  :			Union[str, Any]=2  ,			A  :			Tuple=3  ,			A  :			Optional[Any]=6_4_0  ,			A  :			List[str]=4  ,			A  :			int="silu"  ,			A  :			Optional[int]=3  ,			A  :			Tuple=3_2  ,			A  :			Union[str, Any]=0.1  ,			A  :			Tuple=0.1  ,			A  :			str=0.1  ,			A  :			Union[str, Any]=0.02  ,			A  :			Optional[Any]=True  ,			A  :			Optional[int]=True  ,			A  :			List[str]=1_0  ,			A  :			List[str]=None  ,			):
             _UpperCAmelCase    :			Optional[Any]              =					parent
             _UpperCAmelCase    :			Optional[int]              =					batch_size
             _UpperCAmelCase    :			Optional[Any]              =					image_size
             _UpperCAmelCase    :			Optional[Any]              =					patch_size
             _UpperCAmelCase    :			Optional[int]              =					num_channels
             _UpperCAmelCase    :			Any              =					last_hidden_size
             _UpperCAmelCase    :			Dict              =					num_attention_heads
             _UpperCAmelCase    :			str              =					hidden_act
             _UpperCAmelCase    :			Any              =					conv_kernel_size
             _UpperCAmelCase    :			Optional[int]              =					output_stride
             _UpperCAmelCase    :			Dict              =					hidden_dropout_prob
             _UpperCAmelCase    :			List[Any]              =					attention_probs_dropout_prob
             _UpperCAmelCase    :			Union[str, Any]              =					classifier_dropout_prob
             _UpperCAmelCase    :			Optional[int]              =					use_labels
             _UpperCAmelCase    :			Dict              =					is_training
             _UpperCAmelCase    :			List[str]              =					num_labels
             _UpperCAmelCase    :			str              =					initializer_range
             _UpperCAmelCase    :			Tuple              =					scope
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Optional[int]              =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
  
             _UpperCAmelCase    :			int              =					None
             _UpperCAmelCase    :			Tuple              =					None
             if self.use_labels:
                  _UpperCAmelCase    :			Dict              =					ids_tensor([self.batch_size]  ,			self.num_labels		)
                  _UpperCAmelCase    :			Optional[int]              =					ids_tensor([self.batch_size, self.image_size, self.image_size]  ,			self.num_labels		)
  
             _UpperCAmelCase    :			List[str]              =					self.get_config()
  
             return config, pixel_values, labels, pixel_labels
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             return MobileViTConfig(
                 image_size=self.image_size  ,			patch_size=self.patch_size  ,			num_channels=self.num_channels  ,			num_attention_heads=self.num_attention_heads  ,			hidden_act=self.hidden_act  ,			conv_kernel_size=self.conv_kernel_size  ,			output_stride=self.output_stride  ,			hidden_dropout_prob=self.hidden_dropout_prob  ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,			classifier_dropout_prob=self.classifier_dropout_prob  ,			initializer_range=self.initializer_range  ,			)
  
  
  
        def 		snake_case_      (  self  :			Union[str, Any]  ,			A  :			str  ,			A  :			List[str]  ,			A  :			Tuple  ,			A  :			Optional[Any]		):
             _UpperCAmelCase    :			List[Any]              =					TFMobileViTModel(config=A		)
             _UpperCAmelCase    :			Optional[Any]              =					model(A  ,			training=A		)
             _UpperCAmelCase    :			Union[str, Any]              =					self.image_size // self.output_stride
             self.parent.assertEqual(
                 result.last_hidden_state.shape  ,			(self.batch_size, self.last_hidden_size, expected_height, expected_width)		)
  
  
  
        def 		snake_case_      (  self  :			int  ,			A  :			Dict  ,			A  :			int  ,			A  :			Optional[int]  ,			A  :			List[str]		):
             _UpperCAmelCase    :			str              =					self.num_labels
             _UpperCAmelCase    :			Optional[int]              =					TFMobileViTForImageClassification(A		)
             _UpperCAmelCase    :			int              =					model(A  ,			labels=A  ,			training=A		)
             self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)		)
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			Union[str, Any]  ,			A  :			Dict  ,			A  :			str  ,			A  :			Tuple		):
             _UpperCAmelCase    :			Any              =					self.num_labels
             _UpperCAmelCase    :			Dict              =					TFMobileViTForSemanticSegmentation(A		)
             _UpperCAmelCase    :			Optional[Any]              =					self.image_size // self.output_stride
  
             _UpperCAmelCase    :			str              =					model(A  ,			training=A		)
             self.parent.assertEqual(
                 result.logits.shape  ,			(self.batch_size, self.num_labels, expected_height, expected_width)		)
  
             _UpperCAmelCase    :			int              =					model(A  ,			labels=A  ,			training=A		)
             self.parent.assertEqual(
                 result.logits.shape  ,			(self.batch_size, self.num_labels, expected_height, expected_width)		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             _UpperCAmelCase    :			Optional[int]              =					self.prepare_config_and_inputs()
             _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					config_and_inputs
             _UpperCAmelCase    :			List[Any]              =					{"pixel_values": pixel_values}
             return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						(       _UpperCamelCase ,    _UpperCamelCase ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				(
            (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
            if is_tf_available()
            else ()
        )
        __SCREAMING_SNAKE_CASE		:     str				  =				(
            {'feature-extraction': TFMobileViTModel, 'image-classification': TFMobileViTForImageClassification}
            if is_tf_available()
            else {}
        )
  
        __SCREAMING_SNAKE_CASE		:     Any				  =				False
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				False
        __SCREAMING_SNAKE_CASE		:     Optional[Any]				  =				False
        __SCREAMING_SNAKE_CASE		:     Tuple				  =				False
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				False
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             _UpperCAmelCase    :			List[Any]              =					TFMobileViTModelTester(self		)
             _UpperCAmelCase    :			List[Any]              =					TFMobileViTConfigTester(self  ,			config_class=A  ,			has_text_modality=A		)
  
  
  
        def 		snake_case_      (  self  :			List[Any]		):
             self.config_tester.run_common_tests()
  
  
  
        @unittest.skip(reason="MobileViT does not use inputs_embeds"		)
        def 		snake_case_      (  self  :			Tuple		):
             pass
  
  
  
        @unittest.skip(reason="MobileViT does not support input and output embeddings"		)
        def 		snake_case_      (  self  :			Any		):
             pass
  
  
  
        @unittest.skip(reason="MobileViT does not output attentions"		)
        def 		snake_case_      (  self  :			Tuple		):
             pass
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					self.model_tester.prepare_config_and_inputs_for_common()
  
             for model_class in self.all_model_classes:
                  _UpperCAmelCase    :			Union[str, Any]              =					model_class(A		)
                  _UpperCAmelCase    :			Any              =					inspect.signature(model.call		)
                  # signature.parameters is an OrderedDict => so arg_names order is deterministic
                  _UpperCAmelCase    :			int              =					[*signature.parameters.keys()]
  
                  _UpperCAmelCase    :			str              =					["pixel_values"]
                  self.assertListEqual(arg_names[:1]  ,			A		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase    :			Optional[Any]              =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A		)
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             def check_hidden_states_output(A  :			str  ,			A  :			Dict  ,			A  :			Optional[Any]		):
                  _UpperCAmelCase    :			Union[str, Any]              =					model_class(A		)
  
                  _UpperCAmelCase    :			int              =					model(**self._prepare_for_class(A  ,			A		)		)
  
                  _UpperCAmelCase    :			List[str]              =					outputs.hidden_states
  
                  _UpperCAmelCase    :			Optional[Any]              =					5
                  self.assertEqual(len(A		)  ,			A		)
  
                  # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                  # with the width and height being successively divided by 2.
                  _UpperCAmelCase    :			Optional[Any]              =					2
                  for i in range(len(A		)		):
                       self.assertListEqual(
                           list(hidden_states[i].shape[-2:]		)  ,			[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]  ,			)
                       divisor *= 2
  
                  self.assertEqual(self.model_tester.output_stride  ,			divisor // 2		)
  
             _UpperCAmelCase       , _UpperCAmelCase    :			Tuple              =					self.model_tester.prepare_config_and_inputs_for_common()
  
             for model_class in self.all_model_classes:
                  _UpperCAmelCase    :			int              =					True
                  check_hidden_states_output(A  ,			A  ,			A		)
  
                  # check that output_hidden_states also work using config
                  del inputs_dict["output_hidden_states"]
                  _UpperCAmelCase    :			Any              =					True
  
                  check_hidden_states_output(A  ,			A  ,			A		)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[int]              =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A		)
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			int              =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*A		)
  
  
  
        @unittest.skipIf(
            not is_tf_available() or len(tf.config.list_physical_devices("GPU"		)		) == 0  ,			reason="TF does not support backprop for grouped convolutions on CPU."  ,			)
        def 		snake_case_      (  self  :			Tuple		):
             super().test_dataset_conversion()
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			A  :			Dict  ,			A  :			int  ,			A  :			Optional[Any]=2e-1  ,			A  :			List[str]=2e-1		):
             self.assertTrue(np.allclose(A  ,			A  ,			atol=A  ,			rtol=A		)		)
  
  
  
        @unittest.skipIf(
            not is_tf_available() or len(tf.config.list_physical_devices("GPU"		)		) == 0  ,			reason="TF does not support backprop for grouped convolutions on CPU."  ,			)
        @slow
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					self.model_tester.prepare_config_and_inputs_for_common()
  
             for model_class in self.all_model_classes:
                  # Since `TFMobileViTModel` cannot operate with the default `fit()` method.
                  if model_class.__name__ != "TFMobileViTModel":
                       _UpperCAmelCase    :			List[str]              =					model_class(A		)
                       if getattr(A  ,			"hf_compute_loss"  ,			A		):
                            super().test_keras_fit()
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes:
                  # set an ignore index to correctly test the masked loss used in
                  # `TFMobileViTForSemanticSegmentation`.
                  if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
                       _UpperCAmelCase    :			List[Any]              =					5
  
                  _UpperCAmelCase    :			List[Any]              =					model_class(A		)
                  if getattr(A  ,			"hf_compute_loss"  ,			A		):
                       # The number of elements in the loss should be the same as the number of elements in the label
                       _UpperCAmelCase    :			Dict              =					self._prepare_for_class(inputs_dict.copy()  ,			A  ,			return_labels=A		)
                       _UpperCAmelCase    :			str              =					prepared_for_class[
                           sorted(prepared_for_class.keys() - inputs_dict.keys()  ,			reverse=A		)[0]
                       ]
                       _UpperCAmelCase    :			str              =					added_label.shape.as_list()[:1]
  
                       # Test that model correctly compute the loss with kwargs
                       _UpperCAmelCase    :			Tuple              =					self._prepare_for_class(inputs_dict.copy()  ,			A  ,			return_labels=A		)
                       _UpperCAmelCase    :			List[Any]              =					{"input_ids", "pixel_values", "input_features"}
                       _UpperCAmelCase    :			List[Any]              =					possible_input_names.intersection(set(A		)		).pop()
                       _UpperCAmelCase    :			Union[str, Any]              =					prepared_for_class.pop(A		)
  
                       _UpperCAmelCase    :			Tuple              =					model(A  ,			**A		)[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
  
                       # Test that model correctly compute the loss when we mask some positions
                       _UpperCAmelCase    :			Optional[int]              =					self._prepare_for_class(inputs_dict.copy()  ,			A  ,			return_labels=A		)
                       _UpperCAmelCase    :			Optional[int]              =					{"input_ids", "pixel_values", "input_features"}
                       _UpperCAmelCase    :			Dict              =					possible_input_names.intersection(set(A		)		).pop()
                       _UpperCAmelCase    :			Union[str, Any]              =					prepared_for_class.pop(A		)
                       if "labels" in prepared_for_class:
                            _UpperCAmelCase    :			Tuple              =					prepared_for_class["labels"].numpy()
                            if len(labels.shape		) > 1 and labels.shape[1] != 1:
                                 # labels[0] = -100
                                 _UpperCAmelCase    :			Tuple              =					tf.convert_to_tensor(A		)
                                 _UpperCAmelCase    :			str              =					model(A  ,			**A		)[0]
                                 self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
                                 self.assertTrue(not np.any(np.isnan(loss.numpy()		)		)		)
  
                  # Test that model correctly compute the loss with a dict
                       _UpperCAmelCase    :			Any              =					self._prepare_for_class(inputs_dict.copy()  ,			A  ,			return_labels=A		)
                       _UpperCAmelCase    :			str              =					model(A		)[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
  
                       # Test that model correctly compute the loss with a tuple
                       _UpperCAmelCase    :			Any              =					self._prepare_for_class(inputs_dict.copy()  ,			A  ,			return_labels=A		)
  
                       # Get keys that were added with the _prepare_for_class function
                       _UpperCAmelCase    :			List[str]              =					prepared_for_class.keys() - inputs_dict.keys()
                       _UpperCAmelCase    :			List[Any]              =					inspect.signature(model.call		).parameters
                       _UpperCAmelCase    :			str              =					list(signature.keys()		)
  
                       # Create a dictionary holding the location of the tensors in the tuple
                       _UpperCAmelCase    :			Union[str, Any]              =					{0: input_name}
                       for label_key in label_keys:
                            _UpperCAmelCase    :			Union[str, Any]              =					signature_names.index(A		)
                            _UpperCAmelCase    :			str              =					label_key
                       _UpperCAmelCase    :			Dict              =					sorted(tuple_index_mapping.items()		)
                       # Initialize a list with their default values, update the values and convert to a tuple
                       _UpperCAmelCase    :			Optional[int]              =					[]
  
                       for name in signature_names:
                            if name != "kwargs":
                                 list_input.append(signature[name].default		)
  
                       for index, value in sorted_tuple_index_mapping:
                            _UpperCAmelCase    :			Any              =					prepared_for_class[value]
  
                       _UpperCAmelCase    :			Optional[int]              =					tuple(A		)
  
                       # Send to model
                       _UpperCAmelCase    :			int              =					model(tuple_input[:-1]		)[0]
  
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			List[str]		):
             for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _UpperCAmelCase    :			Tuple              =					TFMobileViTModel.from_pretrained(A		)
                  self.assertIsNotNone(A		)
  
  
  
  
  
  
  
  def 		__snake_case		(   )						->     Optional[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Optional[int]              =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
       return image
  
  
  
  
  
  
  @require_tf
  class 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
        @slow
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			Optional[int]              =					TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small"		)
  
             _UpperCAmelCase    :			int              =					MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small"		)
             _UpperCAmelCase    :			Dict              =					prepare_img()
             _UpperCAmelCase    :			Optional[Any]              =					image_processor(images=A  ,			return_tensors="tf"		)
  
             # forward pass
             _UpperCAmelCase    :			str              =					model(**A  ,			training=A		)
  
             # verify the logits
             _UpperCAmelCase    :			List[Any]              =					tf.TensorShape((1, 1_0_0_0)		)
             self.assertEqual(outputs.logits.shape  ,			A		)
  
             _UpperCAmelCase    :			List[str]              =					tf.constant([-1.9_364, -1.2_327, -0.4_653]		)
  
             tf.debugging.assert_near(outputs.logits[0, :3]  ,			A  ,			atol=1e-4  ,			rtol=1e-04		)
  
  
  
        @slow
        def 		snake_case_      (  self  :			Tuple		):
             # `from_pt` will be removed
             _UpperCAmelCase    :			Any              =					TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
  
             _UpperCAmelCase    :			str              =					MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small"		)
  
             _UpperCAmelCase    :			Tuple              =					prepare_img()
             _UpperCAmelCase    :			Union[str, Any]              =					image_processor(images=A  ,			return_tensors="tf"		)
  
             # forward pass
             _UpperCAmelCase    :			Optional[Any]              =					model(inputs.pixel_values  ,			training=A		)
             _UpperCAmelCase    :			str              =					outputs.logits
  
             # verify the logits
             _UpperCAmelCase    :			str              =					tf.TensorShape((1, 2_1, 3_2, 3_2)		)
             self.assertEqual(logits.shape  ,			A		)
  
             _UpperCAmelCase    :			List[str]              =					tf.constant(
                 [
                     [[6.9_713, 6.9_786, 7.2_422], [7.2_893, 7.2_825, 7.4_446], [7.6_580, 7.8_797, 7.9_420]],
                     [[-10.6_869, -10.3_250, -10.3_471], [-10.4_228, -9.9_868, -9.7_132], [-11.0_405, -11.0_221, -10.7_318]],
                     [[-3.3_089, -2.8_539, -2.6_740], [-3.2_706, -2.5_621, -2.5_108], [-3.2_534, -2.6_615, -2.6_651]],
                 ]		)
  
             tf.debugging.assert_near(logits[0, :3, :3, :3]  ,			A  ,			rtol=1e-4  ,			atol=1e-4		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
   from .tokenization_xglm import XGLMTokenizer
  else:
   _lowerCAmelCase       :      Tuple							=							None
  
  
  _lowerCAmelCase       :      List[str]							=							logging.get_logger(__name__)
  
  _lowerCAmelCase       :      str							=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  _lowerCAmelCase       :      Optional[Any]							=							{
      "vocab_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
      },
      "tokenizer_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
      },
  }
  
  _lowerCAmelCase       :      Union[str, Any]							=							{
      "facebook/xglm-564M": 20_48,
  }
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       _UpperCamelCase		):
        __SCREAMING_SNAKE_CASE		:     str				  =				VOCAB_FILES_NAMES
        __SCREAMING_SNAKE_CASE		:     List[str]				  =				PRETRAINED_VOCAB_FILES_MAP
        __SCREAMING_SNAKE_CASE		:     str				  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __SCREAMING_SNAKE_CASE		:     int				  =				['input_ids', 'attention_mask']
        __SCREAMING_SNAKE_CASE		:     str				  =				XGLMTokenizer
  
  
  
        def __init__(  self  :			List[Any]  ,			A  :			Union[str, Any]=None  ,			A  :			Optional[Any]=None  ,			A  :			Optional[Any]="<s>"  ,			A  :			Optional[int]="</s>"  ,			A  :			str="</s>"  ,			A  :			int="<s>"  ,			A  :			int="<unk>"  ,			A  :			List[str]="<pad>"  ,			**A  :			Tuple  ,			):
             # Compatibility with the original tokenizer
             _UpperCAmelCase    :			Union[str, Any]              =					7
             _UpperCAmelCase    :			Optional[int]              =					[f'<madeupword{i}>' for i in range(self.num_madeup_words		)]
  
             _UpperCAmelCase    :			Any              =					kwargs.get("additional_special_tokens"  ,			[]		)
             kwargs["additional_special_tokens"] += [
                 word for word in madeup_words if word not in kwargs["additional_special_tokens"]
             ]
  
             super().__init__(
                 A  ,			tokenizer_file=A  ,			bos_token=A  ,			eos_token=A  ,			sep_token=A  ,			cls_token=A  ,			unk_token=A  ,			pad_token=A  ,			**A  ,			)
  
             _UpperCAmelCase    :			Any              =					vocab_file
             _UpperCAmelCase    :			Union[str, Any]              =					False if not self.vocab_file else True
  
  
  
        def 		snake_case_      (  self  :			List[str]  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
  
             if token_ids_a is None:
                  return [self.sep_token_id] + token_ids_a
             _UpperCAmelCase    :			Tuple              =					[self.sep_token_id]
             return sep + token_ids_a + sep + sep + token_ids_a
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			List[int]  ,			A  :			Optional[List[int]] = None		):
             _UpperCAmelCase    :			Optional[int]              =					[self.sep_token_id]
  
             if token_ids_a is None:
                  return len(sep + token_ids_a		) * [0]
             return len(sep + token_ids_a + sep + sep + token_ids_a		) * [0]
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			A  :			str  ,			A  :			Optional[str] = None		):
             if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                      "tokenizer."		)
  
             if not os.path.isdir(A		):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory.'		)
                  return
             _UpperCAmelCase    :			str              =					os.path.join(
                 A  ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
  
             if os.path.abspath(self.vocab_file		) != os.path.abspath(A		):
                  copyfile(self.vocab_file  ,			A		)
  
             return (out_vocab_file,)
  