






"""simple docstring"""



import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
						from transformers.models.mobilevitva.modeling_mobilevitva import (
						    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    make_divisible,
						)


if is_vision_available():
						from PIL import Image

						from transformers import MobileViTImageProcessor





class 					_UpperCAmelCase       ( __A							):




					def   A  (				self     :						Optional[Any]   )  ->  List[str]:
							lowercase_ : str							       =   self.config_class(**self.inputs_dict   )
							self.parent.assertTrue(hasattr(lowercase_    ,						'''width_multiplier'''   )   )





class 					_UpperCAmelCase       :




					def __init__(				self     :						Optional[int]    ,						A     :						Union[str, Any]    ,						A     :						List[str]=13    ,						A     :						List[str]=64    ,						A     :						Any=2    ,						A     :						Dict=3    ,						A     :						Tuple="swish"    ,						A     :						List[Any]=3    ,						A     :						List[Any]=32    ,						A     :						str=0.1    ,						A     :						Any=0.02    ,						A     :						Optional[int]=True    ,						A     :						List[str]=True    ,						A     :						Optional[Any]=10    ,						A     :						List[str]=None    ,						A     :						int=0.25    ,						A     :						List[str]=0.0    ,						A     :						str=0.0    ,						)  ->  Dict:
							lowercase_ : Optional[int]							       =   parent
							lowercase_ : Optional[Any]							       =   batch_size
							lowercase_ : str							       =   image_size
							lowercase_ : Union[str, Any]							       =   patch_size
							lowercase_ : Tuple							       =   num_channels
							lowercase_ : Optional[int]							       =   make_divisible(5_12 * width_multiplier    ,						divisor=8   )
							lowercase_ : Optional[Any]							       =   hidden_act
							lowercase_ : Union[str, Any]							       =   conv_kernel_size
							lowercase_ : Tuple							       =   output_stride
							lowercase_ : Dict							       =   classifier_dropout_prob
							lowercase_ : Optional[int]							       =   use_labels
							lowercase_ : Dict							       =   is_training
							lowercase_ : Optional[int]							       =   num_labels
							lowercase_ : Tuple							       =   initializer_range
							lowercase_ : List[Any]							       =   scope
							lowercase_ : List[Any]							       =   width_multiplier
							lowercase_ : Optional[Any]							       =   ffn_dropout
							lowercase_ : List[Any]							       =   attn_dropout




					def   A  (				self     :						str   )  ->  str:
							lowercase_ : Optional[Any]							       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

							lowercase_ : Dict							       =   self.get_config()

							return config, pixel_values, labels, pixel_labels




					def   A  (				self     :						Any   )  ->  List[str]:
							return MobileViTVaConfig(
							    image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						hidden_act=self.hidden_act    ,						conv_kernel_size=self.conv_kernel_size    ,						output_stride=self.output_stride    ,						classifier_dropout_prob=self.classifier_dropout_prob    ,						initializer_range=self.initializer_range    ,						width_multiplier=self.width_multiplier    ,						ffn_dropout=self.ffn_dropout_prob    ,						attn_dropout=self.attn_dropout_prob    ,						)




					def   A  (				self     :						List[Any]    ,						A     :						str    ,						A     :						List[Any]    ,						A     :						List[Any]    ,						A     :						Dict   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   MobileViTVaModel(config=lowercase_   )
							model.to(lowercase_   )
							model.eval()
							lowercase_ : List[str]							       =   model(lowercase_   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,						(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,						)




					def   A  (				self     :						List[str]    ,						A     :						Optional[int]    ,						A     :						Tuple    ,						A     :						Union[str, Any]    ,						A     :						Optional[Any]   )  ->  Tuple:
							lowercase_ : Optional[Any]							       =   self.num_labels
							lowercase_ : List[Any]							       =   MobileViTVaForImageClassification(lowercase_   )
							model.to(lowercase_   )
							model.eval()
							lowercase_ : Any							       =   model(lowercase_    ,						labels=lowercase_   )
							self.parent.assertEqual(result.logits.shape    ,						(self.batch_size, self.num_labels)   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						Optional[int]    ,						A     :						Optional[int]    ,						A     :						Tuple   )  ->  Union[str, Any]:
							lowercase_ : Any							       =   self.num_labels
							lowercase_ : Dict							       =   MobileViTVaForSemanticSegmentation(lowercase_   )
							model.to(lowercase_   )
							model.eval()
							lowercase_ : List[Any]							       =   model(lowercase_   )
							self.parent.assertEqual(
							    result.logits.shape    ,						(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,						)
							lowercase_ : Tuple							       =   model(lowercase_    ,						labels=lowercase_   )
							self.parent.assertEqual(
							    result.logits.shape    ,						(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )    ,						)






					def   A  (				self     :						Tuple   )  ->  Optional[Any]:
							lowercase_ : Tuple							       =   self.prepare_config_and_inputs()
							lowercase_ : Dict							       =   config_and_inputs
							lowercase_ : List[Any]							       =   {"pixel_values": pixel_values}
							return config, inputs_dict





@require_torch
class 					_UpperCAmelCase       ( __A   ,						__A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   (
					    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)

					SCREAMING_SNAKE_CASE_		:				Tuple							    =   (
					    {
					        "feature-extraction": MobileViTVaModel,
					        "image-classification": MobileViTVaForImageClassification,
					        "image-segmentation": MobileViTVaForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   False
					SCREAMING_SNAKE_CASE_		:				List[Any]							    =   False




					def   A  (				self     :						str   )  ->  List[Any]:
							lowercase_ : int							       =   MobileViTVaModelTester(self   )
							lowercase_ : Optional[int]							       =   MobileViTVaConfigTester(self    ,						config_class=lowercase_    ,						has_text_modality=lowercase_   )




					def   A  (				self     :						Any   )  ->  Union[str, Any]:
							self.config_tester.run_common_tests()




					@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds'''   )
					def   A  (				self     :						str   )  ->  List[Any]:
							pass




					@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings'''   )
					def   A  (				self     :						Any   )  ->  Optional[int]:
							pass




					@unittest.skip(reason='''MobileViTV2 does not output attentions'''   )
					def   A  (				self     :						str   )  ->  Optional[int]:
							pass




					@require_torch_multi_gpu
					@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.'''   )
					def   A  (				self     :						Any   )  ->  int:
							pass




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
					def   A  (				self     :						str   )  ->  Optional[int]:
							pass




					def   A  (				self     :						Optional[int]   )  ->  Union[str, Any]:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase_ : List[str]							       =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]    ,						lowercase_   )




					def   A  (				self     :						Union[str, Any]   )  ->  Any:
							lowercase_ : List[Any]							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase_   )




					def   A  (				self     :						Union[str, Any]   )  ->  Dict:
							def check_hidden_states_output(A     :						int    ,						A     :						Dict    ,						A     :						Dict   ):
									lowercase_ : Any							       =   model_class(lowercase_   )
									model.to(lowercase_   )
									model.eval()

									with torch.no_grad():
											lowercase_ : Optional[Any]							       =   model(**self._prepare_for_class(lowercase_    ,						lowercase_   )   )

									lowercase_ : Optional[int]							       =   outputs.hidden_states

									lowercase_ : Any							       =   5
									self.assertEqual(len(lowercase_   )    ,						lowercase_   )

									# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									lowercase_ : Optional[int]							       =   2
									for i in range(len(lowercase_   )   ):
											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   )

							lowercase_ : Any							       =   self.model_tester.prepare_config_and_inputs_for_common()

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

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowercase_ : Optional[int]							       =   True

									check_hidden_states_output(lowercase_    ,						lowercase_    ,						lowercase_   )




					def   A  (				self     :						List[Any]   )  ->  str:
							lowercase_ : str							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowercase_   )




					def   A  (				self     :						Any   )  ->  int:
							lowercase_ : Dict							       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase_   )






					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  List[str]:
							for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Tuple							       =   MobileViTVaModel.from_pretrained(lowercase_   )
									self.assertIsNotNone(lowercase_   )



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






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




					@cached_property
					def   A  (				self     :						List[Any]   )  ->  Optional[Any]:
							return (
							    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''   )
							    if is_vision_available()
							    else None
							)




					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  List[str]:
							lowercase_ : List[str]							       =   MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256'''   ).to(
							    lowercase_   )

							lowercase_ : int							       =   self.default_image_processor
							lowercase_ : int							       =   prepare_img()
							lowercase_ : Optional[int]							       =   image_processor(images=lowercase_    ,						return_tensors='''pt'''   ).to(lowercase_   )

							# forward pass
							with torch.no_grad():
									lowercase_ : str							       =   model(**lowercase_   )

							# verify the logits
							lowercase_ : Optional[int]							       =   torch.Size((1, 10_00)   )
							self.assertEqual(outputs.logits.shape    ,						lowercase_   )

							lowercase_ : Tuple							       =   torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]   ).to(lowercase_   )

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




					@slow
					def   A  (				self     :						Tuple   )  ->  Optional[int]:
							lowercase_ : Dict							       =   MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''   )
							lowercase_ : int							       =   model.to(lowercase_   )

							lowercase_ : Any							       =   MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''   )

							lowercase_ : List[str]							       =   prepare_img()
							lowercase_ : int							       =   image_processor(images=lowercase_    ,						return_tensors='''pt'''   ).to(lowercase_   )

							# forward pass
							with torch.no_grad():
									lowercase_ : str							       =   model(**lowercase_   )
							lowercase_ : Tuple							       =   outputs.logits

							# verify the logits
							lowercase_ : Union[str, Any]							       =   torch.Size((1, 21, 32, 32)   )
							self.assertEqual(logits.shape    ,						lowercase_   )

							lowercase_ : Optional[Any]							       =   torch.tensor(
							    [
							        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
							        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
							        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
							    ]    ,						device=lowercase_    ,						)

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






					@slow
					def   A  (				self     :						Any   )  ->  List[str]:
							lowercase_ : Dict							       =   MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''   )
							lowercase_ : int							       =   model.to(lowercase_   )

							lowercase_ : Optional[int]							       =   MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3'''   )

							lowercase_ : List[str]							       =   prepare_img()
							lowercase_ : Tuple							       =   image_processor(images=lowercase_    ,						return_tensors='''pt'''   ).to(lowercase_   )

							# forward pass
							with torch.no_grad():
									lowercase_ : Optional[Any]							       =   model(**lowercase_   )

							lowercase_ : Tuple							       =   outputs.logits.detach().cpu()

							lowercase_ : List[str]							       =   image_processor.post_process_semantic_segmentation(outputs=lowercase_    ,						target_sizes=[(50, 60)]   )
							lowercase_ : List[str]							       =   torch.Size((50, 60)   )
							self.assertEqual(segmentation[0].shape    ,						lowercase_   )

							lowercase_ : int							       =   image_processor.post_process_semantic_segmentation(outputs=lowercase_   )
							lowercase_ : Dict							       =   torch.Size((32, 32)   )
							self.assertEqual(segmentation[0].shape    ,						lowercase_   )




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		Union[str, Any]         =				LEDTokenizer
   __A	:		Union[str, Any]         =				LEDTokenizerFast
   __A	:		Optional[Any]         =				True






   def       __UpperCamelCase     (      self				:     List[str]  )					->						Union[str, Any]:
       super().setUp()
       lowercase__ :   List[str]    					=				[
           "l",
           "o",
           "w",
           "e",
           "r",
           "s",
           "t",
           "i",
           "d",
           "n",
           "\u0120",
           "\u0120l",
           "\u0120n",
           "\u0120lo",
           "\u0120low",
           "er",
           "\u0120lowest",
           "\u0120newer",
           "\u0120wider",
           "<unk>",
       ]
       lowercase__ :   Optional[int]    					=				dict(zip(lowercase_	,						range(len(lowercase_  )  )  )  )
       lowercase__ :   str    					=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
       lowercase__ :   Tuple    					=				{"unk_token": "<unk>"}

       lowercase__ :   Optional[Any]    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["vocab_file"]  )
       lowercase__ :   Dict    					=				os.path.join(self.tmpdirname	,						VOCAB_FILES_NAMES["merges_file"]  )
       with open(self.vocab_file	,						"w"	,						encoding="utf-8"  ) as fp:
           fp.write(json.dumps(lowercase_  ) + "\n"  )
       with open(self.merges_file	,						"w"	,						encoding="utf-8"  ) as fp:
           fp.write("\n".join(lowercase_  )  )






   def       __UpperCamelCase     (      self				:     int	,						**lowercase_				:     str  )					->						List[Any]:
       kwargs.update(self.special_tokens_map  )
       return self.tokenizer_class.from_pretrained(self.tmpdirname	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     List[Any]	,						**lowercase_				:     Any  )					->						List[Any]:
       kwargs.update(self.special_tokens_map  )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     Any  )					->						Tuple:
       return "lower newer", "lower newer"






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						Optional[Any]:
       return LEDTokenizer.from_pretrained("allenai/led-base-16384"  )






   @cached_property
   def       __UpperCamelCase     (      self				:     Tuple  )					->						int:
       return LEDTokenizerFast.from_pretrained("allenai/led-base-16384"  )






   @require_torch
   def       __UpperCamelCase     (      self				:     int  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       lowercase__ :   str    					=				[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Dict    					=				tokenizer(lowercase_	,						max_length=len(lowercase_  )	,						padding=lowercase_	,						return_tensors="pt"  )
           self.assertIsInstance(lowercase_	,						lowercase_  )

           self.assertEqual((2, 9)	,						batch.input_ids.shape  )
           self.assertEqual((2, 9)	,						batch.attention_mask.shape  )
           lowercase__ :   Union[str, Any]    					=				batch.input_ids.tolist()[0]
           self.assertListEqual(lowercase_	,						lowercase_  )






   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Tuple:
       lowercase__ :   Dict    					=				["A long paragraph for summarization.", "Another paragraph for summarization."]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Optional[int]    					=				tokenizer(lowercase_	,						padding=lowercase_	,						return_tensors="pt"  )
           self.assertIn("input_ids"	,						lowercase_  )
           self.assertIn("attention_mask"	,						lowercase_  )
           self.assertNotIn("labels"	,						lowercase_  )
           self.assertNotIn("decoder_attention_mask"	,						lowercase_  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Any:
       lowercase__ :   Dict    					=				[
           "Summary of the text.",
           "Another summary.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   Dict    					=				tokenizer(text_target=lowercase_	,						max_length=32	,						padding="max_length"	,						return_tensors="pt"  )
           self.assertEqual(32	,						targets["input_ids"].shape[1]  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   int    					=				tokenizer(
               ["I am a small frog" * 10_24, "I am a small frog"]	,						padding=lowercase_	,						truncation=lowercase_	,						return_tensors="pt"  )
           self.assertIsInstance(lowercase_	,						lowercase_  )
           self.assertEqual(batch.input_ids.shape	,						(2, 51_22)  )






   @require_torch
   def       __UpperCamelCase     (      self				:     List[str]  )					->						Any:
       lowercase__ :   Union[str, Any]    					=				["A long paragraph for summarization."]
       lowercase__ :   List[Any]    					=				[
           "Summary of the text.",
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   List[Any]    					=				tokenizer(lowercase_	,						return_tensors="pt"  )
           lowercase__ :   Dict    					=				tokenizer(text_target=lowercase_	,						return_tensors="pt"  )
           lowercase__ :   Optional[int]    					=				inputs["input_ids"]
           lowercase__ :   str    					=				targets["input_ids"]
           self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
           self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
           self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
           self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )






   @require_torch
   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						Dict:
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           lowercase__ :   int    					=				["Summary of the text.", "Another summary."]
           lowercase__ :   List[str]    					=				[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

           lowercase__ :   Tuple    					=				tokenizer(lowercase_	,						padding=lowercase_  )
           lowercase__ :   int    					=				[[0] * len(lowercase_  ) for x in encoded_output["input_ids"]]
           lowercase__ :   Any    					=				tokenizer.pad(lowercase_  )
           self.assertSequenceEqual(outputs["global_attention_mask"]	,						lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						Union[str, Any]:
       pass







   def       __UpperCamelCase     (      self				:     int  )					->						Optional[Any]:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
               lowercase__ :   List[Any]    					=				self.rust_tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   List[str]    					=				self.tokenizer_class.from_pretrained(lowercase_	,						**lowercase_  )
               lowercase__ :   List[Any]    					=				"A, <mask> AllenNLP sentence."
               lowercase__ :   Tuple    					=				tokenizer_r.encode_plus(lowercase_	,						add_special_tokens=lowercase_	,						return_token_type_ids=lowercase_  )
               lowercase__ :   List[str]    					=				tokenizer_p.encode_plus(lowercase_	,						add_special_tokens=lowercase_	,						return_token_type_ids=lowercase_  )
               self.assertEqual(sum(tokens_r["token_type_ids"]  )	,						sum(tokens_p["token_type_ids"]  )  )
               self.assertEqual(
                   sum(tokens_r["attention_mask"]  ) / len(tokens_r["attention_mask"]  )	,						sum(tokens_p["attention_mask"]  ) / len(tokens_p["attention_mask"]  )	,						)

               lowercase__ :   Any    					=				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]  )
               lowercase__ :   Optional[Any]    					=				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]  )
               self.assertSequenceEqual(tokens_p["input_ids"]	,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )
               self.assertSequenceEqual(tokens_r["input_ids"]	,						[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]  )

               self.assertSequenceEqual(
                   lowercase_	,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )
               self.assertSequenceEqual(
                   lowercase_	,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]  )



