



"""simple docstring"""







import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

							from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image
class        __A       :







		'''simple docstring'''





		def __init__(	self						:  Union[str, Any]			,_snake_case						:  int			,_snake_case						:  Any=3			,_snake_case						:  List[Any]=32			,_snake_case						:  str=3			,_snake_case						:  List[Any]=10			,_snake_case						:  Any=[8, 16, 32, 64]			,_snake_case						:  Optional[int]=[1, 1, 2, 1]			,_snake_case						:  Dict=True			,_snake_case						:  Dict=True			,_snake_case						:  List[Any]="relu"			,_snake_case						:  int=3			,_snake_case						:  Dict=None			,_snake_case						:  List[Any]=["stage2", "stage3", "stage4"]			,_snake_case						:  List[Any]=[2, 3, 4]			,_snake_case						:  int=1			,)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Any				=			parent
				lowercase__   :    Dict				=			batch_size
				lowercase__   :    Any				=			image_size
				lowercase__   :    Dict				=			num_channels
				lowercase__   :    int				=			embeddings_size
				lowercase__   :    str				=			hidden_sizes
				lowercase__   :    Tuple				=			depths
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    str				=			use_labels
				lowercase__   :    int				=			hidden_act
				lowercase__   :    List[Any]				=			num_labels
				lowercase__   :    Dict				=			scope
				lowercase__   :    Dict				=			len(_snake_case						)
				lowercase__   :    Optional[int]				=			out_features
				lowercase__   :    List[str]				=			out_indices
				lowercase__   :    Any				=			num_groups







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






				"""simple docstring"""



				lowercase__   :    int				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

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

				return config, pixel_values, labels







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






				"""simple docstring"""



				return BitConfig(
				    num_channels=self.num_channels			,embeddings_size=self.embeddings_size			,hidden_sizes=self.hidden_sizes			,depths=self.depths			,hidden_act=self.hidden_act			,num_labels=self.num_labels			,out_features=self.out_features			,out_indices=self.out_indices			,num_groups=self.num_groups			,)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Any]			,_snake_case						:  Any			,_snake_case						:  str						)					->		int:






				"""simple docstring"""



				lowercase__   :    Tuple				=			BitModel(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    Union[str, Any]				=			model(_snake_case						)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  List[str]			,_snake_case						:  int			,_snake_case						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.num_labels
				lowercase__   :    List[str]				=			BitForImageClassification(_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    int				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  List[Any]			,_snake_case						:  Tuple			,_snake_case						:  str						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    int				=			BitBackbone(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    str				=			model(_snake_case						)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps						)			,len(config.out_features						)						)
				self.parent.assertListEqual(list(result.feature_maps[0].shape						)			,[self.batch_size, self.hidden_sizes[1], 4, 4]						)

				# verify channels
				self.parent.assertEqual(len(model.channels						)			,len(config.out_features						)						)
				self.parent.assertListEqual(model.channels			,config.hidden_sizes[1:]						)

				# verify backbone works with out_features=None
				lowercase__   :    Tuple				=			None
				lowercase__   :    Optional[int]				=			BitBackbone(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    int				=			model(_snake_case						)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps						)			,1						)
				self.parent.assertListEqual(list(result.feature_maps[0].shape						)			,[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

				# verify channels
				self.parent.assertEqual(len(model.channels						)			,1						)
				self.parent.assertListEqual(model.channels			,[config.hidden_sizes[-1]]						)







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






				"""simple docstring"""



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





@require_torch
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		lowerCAmelCase			:					List[str]   	=				(
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase			:					Any   	=				False
		lowerCAmelCase			:					str   	=				False
		lowerCAmelCase			:					str   	=				False
		lowerCAmelCase			:					Optional[int]   	=				False
		lowerCAmelCase			:					List[str]   	=				False







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






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			BitModelTester(self						)
				lowercase__   :    Tuple				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Optional[int]:






				"""simple docstring"""



				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()







		def 					UpperCAmelCase    (	self						:  int						)					->		Tuple:






				"""simple docstring"""



				return







		@unittest.skip(reason='''Bit does not output attentions'''						)
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''Bit does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''Bit does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Any:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Dict				=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowercase__   :    List[str]				=			model_class(_snake_case						)
						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[Any]				=			['''pixel_values''']
						self.assertListEqual(arg_names[:1]			,_snake_case						)







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






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    str				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*_snake_case						)







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






				"""simple docstring"""



				lowercase__		,			lowercase__   :    Any				=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowercase__   :    List[Any]				=			model_class(config=_snake_case						)
						for name, module in model.named_modules():
								if isinstance(_snake_case			,(nn.BatchNormad, nn.GroupNorm)						):
										self.assertTrue(
										    torch.all(module.weight == 1						)			,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,)
										self.assertTrue(
										    torch.all(module.bias == 0						)			,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Union[str, Any]			,_snake_case						:  int			,_snake_case						:  Optional[Any]						):
						lowercase__   :    Optional[int]				=			model_class(_snake_case						)
						model.to(_snake_case						)
						model.eval()

						with torch.no_grad():
								lowercase__   :    Tuple				=			model(**self._prepare_for_class(_snake_case			,_snake_case						)						)

						lowercase__   :    Optional[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    int				=			self.model_tester.num_stages
						self.assertEqual(len(_snake_case						)			,expected_num_stages + 1						)

						# Bit's feature maps are of shape (batch_size, num_channels, height, width)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]						)			,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,)

				lowercase__		,			lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    str				=			['''preactivation''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    Dict				=			layer_type
								lowercase__   :    Tuple				=			True
								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowercase__   :    List[Any]				=			True

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		@unittest.skip(reason='''Bit does not use feedforward chunking'''						)
		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[int]:






				"""simple docstring"""



				pass







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







		@slow
		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Dict				=			BitModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)





def      __UpperCAmelCase      (    )							->      List[str]:
		lowercase__   :    int				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image
@require_torch
@require_vision
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						) if is_vision_available() else None
				)







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






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_snake_case						)

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

				# forward pass
				with torch.no_grad():
						lowercase__   :    Optional[int]				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    Tuple				=			torch.Size((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    List[str]				=			torch.tensor([[-0.6526, -0.5263, -1.4398]]						).to(_snake_case						)

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





@require_torch
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[int]   	=				(BitBackbone,) if is_torch_available() else ()
		lowerCAmelCase			:					Any   	=				BitConfig

		lowerCAmelCase			:					Optional[int]   	=				False







		def 					UpperCAmelCase    (	self						:  Any						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Tuple				=			BitModelTester(self						)

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>"]  )



