


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      snake_case__	(   _A	):
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   self.config_class(**self.inputs_dict   )
										self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,   'hidden_sizes'   )   )
										self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,   'neck_hidden_sizes'   )   )
										self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,   'num_attention_heads'   )   )






class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase=13  ,   _UpperCAmelCase=32  ,   _UpperCAmelCase=2  ,   _UpperCAmelCase=3  ,   _UpperCAmelCase=640  ,   _UpperCAmelCase=4  ,   _UpperCAmelCase="silu"  ,   _UpperCAmelCase=3  ,   _UpperCAmelCase=32  ,   _UpperCAmelCase=0.1  ,   _UpperCAmelCase=0.1  ,   _UpperCAmelCase=0.1  ,   _UpperCAmelCase=0.02  ,   _UpperCAmelCase=True  ,   _UpperCAmelCase=True  ,   _UpperCAmelCase=10  ,   _UpperCAmelCase=None  ,   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   parent
										__magic_name__					:				Tuple				=   batch_size
										__magic_name__					:				List[str]				=   image_size
										__magic_name__					:				Dict				=   patch_size
										__magic_name__					:				Optional[int]				=   num_channels
										__magic_name__					:				str				=   last_hidden_size
										__magic_name__					:				Union[str, Any]				=   num_attention_heads
										__magic_name__					:				Optional[Any]				=   hidden_act
										__magic_name__					:				List[Any]				=   conv_kernel_size
										__magic_name__					:				Any				=   output_stride
										__magic_name__					:				str				=   hidden_dropout_prob
										__magic_name__					:				str				=   attention_probs_dropout_prob
										__magic_name__					:				Dict				=   classifier_dropout_prob
										__magic_name__					:				Optional[Any]				=   use_labels
										__magic_name__					:				str				=   is_training
										__magic_name__					:				str				=   num_labels
										__magic_name__					:				List[str]				=   initializer_range
										__magic_name__					:				Any				=   scope
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

										__magic_name__					:				List[str]				=   None
										__magic_name__					:				int				=   None
										if self.use_labels:
																__magic_name__					:				str				=   ids_tensor([self.batch_size]  ,   self.num_labels   )
																__magic_name__					:				Optional[int]				=   ids_tensor([self.batch_size, self.image_size, self.image_size]  ,   self.num_labels   )

										__magic_name__					:				Optional[Any]				=   self.get_config()

										return config, pixel_values, labels, pixel_labels
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   TFMobileViTModel(config=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Dict				=   model(__SCREAMING_SNAKE_CASE  ,   training=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				List[str]				=   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 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   self.num_labels
										__magic_name__					:				str				=   TFMobileViTForImageClassification(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Optional[int]				=   model(__SCREAMING_SNAKE_CASE  ,   labels=__SCREAMING_SNAKE_CASE  ,   training=__SCREAMING_SNAKE_CASE   )
										self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.num_labels
										__magic_name__					:				List[Any]				=   TFMobileViTForSemanticSegmentation(__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Optional[int]				=   self.image_size // self.output_stride

										__magic_name__					:				str				=   model(__SCREAMING_SNAKE_CASE  ,   training=__SCREAMING_SNAKE_CASE   )
										self.parent.assertEqual(
										    result.logits.shape  ,   (self.batch_size, self.num_labels, expected_height, expected_width)   )

										__magic_name__					:				Any				=   model(__SCREAMING_SNAKE_CASE  ,   labels=__SCREAMING_SNAKE_CASE  ,   training=__SCREAMING_SNAKE_CASE   )
										self.parent.assertEqual(
										    result.logits.shape  ,   (self.batch_size, self.num_labels, expected_height, expected_width)   )





				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




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






@require_tf
class      snake_case__	(   _A    ,							_A    ,							unittest.TestCase	):
				__lowerCAmelCase		=   (
				    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
				    if is_tf_available()
				    else ()
				)
				__lowerCAmelCase		=   (
				    {'''feature-extraction''': TFMobileViTModel, '''image-classification''': TFMobileViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   False
				__lowerCAmelCase		=   False
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   TFMobileViTModelTester(self   )
										__magic_name__					:				List[str]				=   TFMobileViTConfigTester(self  ,   config_class=__SCREAMING_SNAKE_CASE  ,   has_text_modality=__SCREAMING_SNAKE_CASE   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										self.config_tester.run_common_tests()
				@unittest.skip(reason='MobileViT does not use inputs_embeds'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass
				@unittest.skip(reason='MobileViT does not support input and output embeddings'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass
				@unittest.skip(reason='MobileViT does not output attentions'   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										pass
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__					:				Optional[int]				=   model_class(__SCREAMING_SNAKE_CASE   )
																__magic_name__					:				Optional[int]				=   inspect.signature(model.call   )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__magic_name__					:				int				=   [*signature.parameters.keys()]

																__magic_name__					:				Any				=   ['pixel_values']
																self.assertListEqual(arg_names[:1]  ,   __SCREAMING_SNAKE_CASE   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				int				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										def check_hidden_states_output(_UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase   ):
																__magic_name__					:				Any				=   model_class(__SCREAMING_SNAKE_CASE   )

																__magic_name__					:				Union[str, Any]				=   model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE   )   )

																__magic_name__					:				Dict				=   outputs.hidden_states

																__magic_name__					:				Any				=   5
																self.assertEqual(len(__SCREAMING_SNAKE_CASE   )  ,   __SCREAMING_SNAKE_CASE   )

																# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
																# with the width and height being successively divided by 2.
																__magic_name__					:				str				=   2
																for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
																						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   )

										__magic_name__					:				List[Any]				=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																__magic_name__					:				Optional[int]				=   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"]
																__magic_name__					:				List[str]				=   True

																check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Tuple				=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE   )
				@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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										super().test_dataset_conversion()
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase=2E-1  ,   _UpperCAmelCase=2E-1   ):




										"""simple docstring"""




										self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   atol=__SCREAMING_SNAKE_CASE  ,   rtol=__SCREAMING_SNAKE_CASE   )   )
				@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 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				List[str]				=   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":
																						__magic_name__					:				Any				=   model_class(__SCREAMING_SNAKE_CASE   )
																						if getattr(__SCREAMING_SNAKE_CASE  ,   'hf_compute_loss'  ,   __SCREAMING_SNAKE_CASE   ):
																												super().test_keras_fit()
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, 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":
																						__magic_name__					:				int				=   5

																__magic_name__					:				Any				=   model_class(__SCREAMING_SNAKE_CASE   )
																if getattr(__SCREAMING_SNAKE_CASE  ,   'hf_compute_loss'  ,   __SCREAMING_SNAKE_CASE   ):
																						# The number of elements in the loss should be the same as the number of elements in the label
																						__magic_name__					:				List[Any]				=   self._prepare_for_class(inputs_dict.copy()  ,   __SCREAMING_SNAKE_CASE  ,   return_labels=__SCREAMING_SNAKE_CASE   )
																						__magic_name__					:				Union[str, Any]				=   prepared_for_class[
																						    sorted(prepared_for_class.keys() - inputs_dict.keys()  ,   reverse=__SCREAMING_SNAKE_CASE   )[0]
																						]
																						__magic_name__					:				Optional[Any]				=   added_label.shape.as_list()[:1]

																						# Test that model correctly compute the loss with kwargs
																						__magic_name__					:				Tuple				=   self._prepare_for_class(inputs_dict.copy()  ,   __SCREAMING_SNAKE_CASE  ,   return_labels=__SCREAMING_SNAKE_CASE   )
																						__magic_name__					:				Union[str, Any]				=   {'input_ids', 'pixel_values', 'input_features'}
																						__magic_name__					:				Optional[Any]				=   possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE   )   ).pop()
																						__magic_name__					:				str				=   prepared_for_class.pop(__SCREAMING_SNAKE_CASE   )

																						__magic_name__					:				Union[str, Any]				=   model(__SCREAMING_SNAKE_CASE  ,   **__SCREAMING_SNAKE_CASE   )[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
																						__magic_name__					:				List[str]				=   self._prepare_for_class(inputs_dict.copy()  ,   __SCREAMING_SNAKE_CASE  ,   return_labels=__SCREAMING_SNAKE_CASE   )
																						__magic_name__					:				str				=   {'input_ids', 'pixel_values', 'input_features'}
																						__magic_name__					:				Optional[int]				=   possible_input_names.intersection(set(__SCREAMING_SNAKE_CASE   )   ).pop()
																						__magic_name__					:				Union[str, Any]				=   prepared_for_class.pop(__SCREAMING_SNAKE_CASE   )
																						if "labels" in prepared_for_class:
																												__magic_name__					:				Union[str, Any]				=   prepared_for_class['labels'].numpy()
																												if len(labels.shape   ) > 1 and labels.shape[1] != 1:
																																		# labels[0] = -100
																																		__magic_name__					:				List[Any]				=   tf.convert_to_tensor(__SCREAMING_SNAKE_CASE   )
																																		__magic_name__					:				List[str]				=   model(__SCREAMING_SNAKE_CASE  ,   **__SCREAMING_SNAKE_CASE   )[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
																						__magic_name__					:				Any				=   self._prepare_for_class(inputs_dict.copy()  ,   __SCREAMING_SNAKE_CASE  ,   return_labels=__SCREAMING_SNAKE_CASE   )
																						__magic_name__					:				Dict				=   model(__SCREAMING_SNAKE_CASE   )[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
																						__magic_name__					:				Union[str, Any]				=   self._prepare_for_class(inputs_dict.copy()  ,   __SCREAMING_SNAKE_CASE  ,   return_labels=__SCREAMING_SNAKE_CASE   )

																						# Get keys that were added with the _prepare_for_class function
																						__magic_name__					:				List[str]				=   prepared_for_class.keys() - inputs_dict.keys()
																						__magic_name__					:				List[Any]				=   inspect.signature(model.call   ).parameters
																						__magic_name__					:				Tuple				=   list(signature.keys()   )

																						# Create a dictionary holding the location of the tensors in the tuple
																						__magic_name__					:				int				=   {0: input_name}
																						for label_key in label_keys:
																												__magic_name__					:				Optional[Any]				=   signature_names.index(__SCREAMING_SNAKE_CASE   )
																												__magic_name__					:				List[Any]				=   label_key
																						__magic_name__					:				Dict				=   sorted(tuple_index_mapping.items()   )
																						# Initialize a list with their default values, update the values and convert to a tuple
																						__magic_name__					:				str				=   []

																						for name in signature_names:
																												if name != "kwargs":
																																		list_input.append(signature[name].default   )

																						for index, value in sorted_tuple_index_mapping:
																												__magic_name__					:				List[Any]				=   prepared_for_class[value]

																						__magic_name__					:				Optional[Any]				=   tuple(__SCREAMING_SNAKE_CASE   )

																						# Send to model
																						__magic_name__					:				Optional[Any]				=   model(tuple_input[:-1]   )[0]

																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]   )





				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__					:				int				=   TFMobileViTModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
																self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )






def 			__SCREAMING_SNAKE_CASE							(					)							->	Any:
						__magic_name__					:				Any				=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
						return image



@require_tf
class      snake_case__	(   unittest.TestCase	):
				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   TFMobileViTForImageClassification.from_pretrained('apple/mobilevit-xx-small'   )

										__magic_name__					:				int				=   MobileViTImageProcessor.from_pretrained('apple/mobilevit-xx-small'   )
										__magic_name__					:				Union[str, Any]				=   prepare_img()
										__magic_name__					:				Dict				=   image_processor(images=__SCREAMING_SNAKE_CASE  ,   return_tensors='tf'   )

										# forward pass
										__magic_name__					:				Tuple				=   model(**__SCREAMING_SNAKE_CASE  ,   training=__SCREAMING_SNAKE_CASE   )

										# verify the logits
										__magic_name__					:				Union[str, Any]				=   tf.TensorShape((1, 1000)   )
										self.assertEqual(outputs.logits.shape  ,   __SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Dict				=   tf.constant([-1.9364, -1.2327, -0.4653]   )

										tf.debugging.assert_near(outputs.logits[0, :3]  ,   __SCREAMING_SNAKE_CASE  ,   atol=1E-4  ,   rtol=1E-0_4   )





				@slow
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   TFMobileViTForSemanticSegmentation.from_pretrained('apple/deeplabv3-mobilevit-xx-small'   )

										__magic_name__					:				Dict				=   MobileViTImageProcessor.from_pretrained('apple/deeplabv3-mobilevit-xx-small'   )

										__magic_name__					:				Any				=   prepare_img()
										__magic_name__					:				Any				=   image_processor(images=__SCREAMING_SNAKE_CASE  ,   return_tensors='tf'   )

										# forward pass
										__magic_name__					:				List[str]				=   model(inputs.pixel_values  ,   training=__SCREAMING_SNAKE_CASE   )
										__magic_name__					:				Union[str, Any]				=   outputs.logits

										# verify the logits
										__magic_name__					:				Optional[Any]				=   tf.TensorShape((1, 21, 32, 32)   )
										self.assertEqual(logits.shape  ,   __SCREAMING_SNAKE_CASE   )

										__magic_name__					:				Optional[int]				=   tf.constant(
										    [
										        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
										        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
										        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
										    ]   )

										tf.debugging.assert_near(logits[0, :3, :3, :3]  ,   __SCREAMING_SNAKE_CASE  ,   rtol=1E-4  ,   atol=1E-4   )




'''simple docstring'''

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= MPNetTokenizer
   A		= MPNetTokenizerFast
   A		= True
   A		= True






   def 					__a			(       self      ):
    super().setUp()

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        'want',
        '##want',
        '##ed',
        'wa',
        'un',
        'runn',
        '##ing',
        ',',
        'low',
        'lowest',
    ]
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['vocab_file']      )
    with open(self.vocab_file    ,'w'    ,encoding='utf-8'      ) as vocab_writer:
     vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		'UNwant\u00E9d,running'
    SCREAMING_SNAKE_CASE  :  Any								=		'unwanted, running'
    return input_text, output_text






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_class(self.vocab_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize('UNwant\u00E9d,running'      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,['un', '##want', '##ed', ',', 'runn', '##ing']      )
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,[9, 6, 7, 12, 10, 11]      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == [0] + text + [2]
    assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]


