

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor



class SCREAMING_SNAKE_CASE     (  a_			):
		"""simple docstring"""






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






					"""simple docstring"""







					__lowerCAmelCase  :      Any    =						self.config_class(**self.inputs_dict   )
					self.parent.assertTrue(hasattr(__lowerCAmelCase	,							"""tf_padding"""   )   )
					self.parent.assertTrue(hasattr(__lowerCAmelCase	,							"""depth_multiplier"""   )   )






class SCREAMING_SNAKE_CASE     :
		"""simple docstring"""






		def __init__(      self			:   str	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Union[str, Any]=13	,							lowerCAmelCase			:   int=3	,							lowerCAmelCase			:   Optional[int]=32	,							lowerCAmelCase			:   str=0.25	,							lowerCAmelCase			:   Optional[Any]=8	,							lowerCAmelCase			:   Union[str, Any]=8	,							lowerCAmelCase			:   str=6	,							lowerCAmelCase			:   Dict=32	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   Dict=True	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Union[str, Any]="relu6"	,							lowerCAmelCase			:   List[Any]=12_80	,							lowerCAmelCase			:   Any=0.1	,							lowerCAmelCase			:   Union[str, Any]=0.02	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Tuple=True	,							lowerCAmelCase			:   Tuple=10	,							lowerCAmelCase			:   Dict=None	,							)							->     Dict:






					"""simple docstring"""







					__lowerCAmelCase  :      Union[str, Any]    =						parent
					__lowerCAmelCase  :      str    =						batch_size
					__lowerCAmelCase  :      int    =						num_channels
					__lowerCAmelCase  :      Union[str, Any]    =						image_size
					__lowerCAmelCase  :      str    =						depth_multiplier
					__lowerCAmelCase  :      Dict    =						depth_divisible_by
					__lowerCAmelCase  :      List[str]    =						min_depth
					__lowerCAmelCase  :      Optional[int]    =						expand_ratio
					__lowerCAmelCase  :      Any    =						tf_padding
					__lowerCAmelCase  :      List[str]    =						output_stride
					__lowerCAmelCase  :      Tuple    =						first_layer_is_expansion
					__lowerCAmelCase  :      Optional[int]    =						finegrained_output
					__lowerCAmelCase  :      str    =						hidden_act
					__lowerCAmelCase  :      List[Any]    =						last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier   )
					__lowerCAmelCase  :      int    =						classifier_dropout_prob
					__lowerCAmelCase  :      List[str]    =						use_labels
					__lowerCAmelCase  :      Optional[int]    =						is_training
					__lowerCAmelCase  :      int    =						num_labels
					__lowerCAmelCase  :      Any    =						initializer_range
					__lowerCAmelCase  :      List[Any]    =						scope







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






					"""simple docstring"""







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

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

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

					return config, pixel_values, labels, pixel_labels







		def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






					"""simple docstring"""







					return MobileNetVaConfig(
					    num_channels=self.num_channels	,							image_size=self.image_size	,							depth_multiplier=self.depth_multiplier	,							depth_divisible_by=self.depth_divisible_by	,							min_depth=self.min_depth	,							expand_ratio=self.expand_ratio	,							output_stride=self.output_stride	,							first_layer_is_expansion=self.first_layer_is_expansion	,							finegrained_output=self.finegrained_output	,							hidden_act=self.hidden_act	,							tf_padding=self.tf_padding	,							classifier_dropout_prob=self.classifier_dropout_prob	,							initializer_range=self.initializer_range	,							)







		def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Optional[int]	,							lowerCAmelCase			:   List[Any]   )							->     Optional[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Dict    =						MobileNetVaModel(config=__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					__lowerCAmelCase  :      Dict    =						model(__lowerCAmelCase   )
					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,
					    )	,							)
					self.parent.assertEqual(
					    result.pooler_output.shape	,							(self.batch_size, self.last_hidden_size)	,							)







		def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   List[Any]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   Optional[int]   )							->     Union[str, Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      List[str]    =						self.num_labels
					__lowerCAmelCase  :      Dict    =						MobileNetVaForImageClassification(__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					__lowerCAmelCase  :      Optional[Any]    =						model(__lowerCAmelCase	,							labels=__lowerCAmelCase   )
					self.parent.assertEqual(result.logits.shape	,							(self.batch_size, self.num_labels)   )







		def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   int	,							lowerCAmelCase			:   Any	,							lowerCAmelCase			:   Union[str, Any]   )							->     Union[str, Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      List[str]    =						self.num_labels
					__lowerCAmelCase  :      Any    =						MobileNetVaForSemanticSegmentation(__lowerCAmelCase   )
					model.to(__lowerCAmelCase   )
					model.eval()
					__lowerCAmelCase  :      Optional[Any]    =						model(__lowerCAmelCase   )
					self.parent.assertEqual(
					    result.logits.shape	,							(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )	,							)
					__lowerCAmelCase  :      List[Any]    =						model(__lowerCAmelCase	,							labels=__lowerCAmelCase   )
					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 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






					"""simple docstring"""







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






@require_torch
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							unittest.TestCase			):
		"""simple docstring"""






		lowerCamelCase   :      Any										=(
		    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		lowerCamelCase   :      Optional[int]										=(
		    {
		        'feature-extraction': MobileNetVaModel,
		        'image-classification': MobileNetVaForImageClassification,
		        'image-segmentation': MobileNetVaForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		lowerCamelCase   :      int										=False
		lowerCamelCase   :      List[Any]										=False
		lowerCamelCase   :      Optional[int]										=False
		lowerCamelCase   :      Tuple										=False







		def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[int]    =						MobileNetVaModelTester(self   )
					__lowerCAmelCase  :      int    =						MobileNetVaConfigTester(self	,							config_class=__lowerCAmelCase	,							has_text_modality=__lowerCAmelCase   )







		def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






					"""simple docstring"""







					self.config_tester.run_common_tests()







		@unittest.skip(reason="""MobileNetV2 does not use inputs_embeds"""   )
		def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






					"""simple docstring"""







					pass







		@unittest.skip(reason="""MobileNetV2 does not support input and output embeddings"""   )
		def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Tuple:






					"""simple docstring"""







					pass







		@unittest.skip(reason="""MobileNetV2 does not output attentions"""   )
		def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Optional[int]:






					"""simple docstring"""







					pass







		def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Any:






					"""simple docstring"""







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

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

								__lowerCAmelCase  :      Optional[Any]    =						["""pixel_values"""]
								self.assertListEqual(arg_names[:1]	,							__lowerCAmelCase   )







		def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__lowerCAmelCase   )







		def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Any:






					"""simple docstring"""







					def check_hidden_states_output(lowerCAmelCase			:   Union[str, Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   List[Any]   ):
								__lowerCAmelCase  :      Any    =						model_class(__lowerCAmelCase   )
								model.to(__lowerCAmelCase   )
								model.eval()

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

								__lowerCAmelCase  :      str    =						outputs.hidden_states

								__lowerCAmelCase  :      Any    =						16
								self.assertEqual(len(__lowerCAmelCase   )	,							__lowerCAmelCase   )

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

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

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

								check_hidden_states_output(__lowerCAmelCase	,							__lowerCAmelCase	,							__lowerCAmelCase   )







		def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[int]    =						self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase   )







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






					"""simple docstring"""







					__lowerCAmelCase  :      Dict    =						self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCAmelCase   )







		@slow
		def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Optional[Any]:






					"""simple docstring"""







					for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__lowerCAmelCase  :      Optional[int]    =						MobileNetVaModel.from_pretrained(__lowerCAmelCase   )
								self.assertIsNotNone(__lowerCAmelCase   )






def      snake_case_				()      ->     List[Any]:
			__lowerCAmelCase  :      str    =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
			return image



@require_torch
@require_vision
class SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
		"""simple docstring"""






		@cached_property
		def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Optional[int]:






					"""simple docstring"""







					return (
					    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v2_1.0_224"""   ) if is_vision_available() else None
					)







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






					"""simple docstring"""







					__lowerCAmelCase  :      str    =						MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v2_1.0_224"""   ).to(__lowerCAmelCase   )

					__lowerCAmelCase  :      int    =						self.default_image_processor
					__lowerCAmelCase  :      List[Any]    =						prepare_img()
					__lowerCAmelCase  :      str    =						image_processor(images=__lowerCAmelCase	,							return_tensors="""pt"""   ).to(__lowerCAmelCase   )

					# forward pass
					with torch.no_grad():
								__lowerCAmelCase  :      Any    =						model(**__lowerCAmelCase   )

					# verify the logits
					__lowerCAmelCase  :      List[str]    =						torch.Size((1, 10_01)   )
					self.assertEqual(outputs.logits.shape	,							__lowerCAmelCase   )

					__lowerCAmelCase  :      Optional[Any]    =						torch.tensor([0.2445, -1.1993, 0.1905]   ).to(__lowerCAmelCase   )

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







		@slow
		def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Optional[Any]:






					"""simple docstring"""







					__lowerCAmelCase  :      Optional[Any]    =						MobileNetVaForSemanticSegmentation.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""   )
					__lowerCAmelCase  :      Dict    =						model.to(__lowerCAmelCase   )

					__lowerCAmelCase  :      List[str]    =						MobileNetVaImageProcessor.from_pretrained("""google/deeplabv3_mobilenet_v2_1.0_513"""   )

					__lowerCAmelCase  :      Union[str, Any]    =						prepare_img()
					__lowerCAmelCase  :      str    =						image_processor(images=__lowerCAmelCase	,							return_tensors="""pt"""   ).to(__lowerCAmelCase   )

					# forward pass
					with torch.no_grad():
								__lowerCAmelCase  :      List[str]    =						model(**__lowerCAmelCase   )
					__lowerCAmelCase  :      List[str]    =						outputs.logits

					# verify the logits
					__lowerCAmelCase  :      Any    =						torch.Size((1, 21, 65, 65)   )
					self.assertEqual(logits.shape	,							__lowerCAmelCase   )

					__lowerCAmelCase  :      Any    =						torch.tensor(
					    [
					        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
					        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
					        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
					    ]	,							device=__lowerCAmelCase	,							)

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





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      str										=DebertaTokenizer
  lowerCamelCase   :      Dict										=True
  lowerCamelCase   :      List[str]										=DebertaTokenizerFast







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     Union[str, Any]:






     """simple docstring"""







     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __lowerCAmelCase  :      Optional[int]    =						[
         """l""",
         """o""",
         """w""",
         """e""",
         """r""",
         """s""",
         """t""",
         """i""",
         """d""",
         """n""",
         """\u0120""",
         """\u0120l""",
         """\u0120n""",
         """\u0120lo""",
         """\u0120low""",
         """er""",
         """\u0120lowest""",
         """\u0120newer""",
         """\u0120wider""",
         """[UNK]""",
     ]
     __lowerCAmelCase  :      Optional[int]    =						dict(zip(lowerCAmelCase	,							range(len(lowerCAmelCase   )   )   )   )
     __lowerCAmelCase  :      List[str]    =						["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
     __lowerCAmelCase  :      Any    =						{"""unk_token""": """[UNK]"""}

     __lowerCAmelCase  :      Tuple    =						os.path.join(self.tmpdirname	,							VOCAB_FILES_NAMES["""vocab_file"""]   )
     __lowerCAmelCase  :      Optional[int]    =						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(lowerCAmelCase   ) + """\n"""   )
     with open(self.merges_file	,							"""w"""	,							encoding="""utf-8"""   ) as fp:
        fp.write("""\n""".join(lowerCAmelCase   )   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]	,							**lowerCAmelCase			:   Tuple   )							->     Dict:






     """simple docstring"""







     kwargs.update(self.special_tokens_map   )
     return self.tokenizer_class.from_pretrained(self.tmpdirname	,							**lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]	,							lowerCAmelCase			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						"""lower newer"""
     __lowerCAmelCase  :      Union[str, Any]    =						"""lower newer"""
     return input_text, output_text







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






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.get_tokenizer()
     __lowerCAmelCase  :      int    =						"""lower newer"""
     __lowerCAmelCase  :      Any    =						["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.tokenize(lowerCAmelCase   )
     self.assertListEqual(lowerCAmelCase	,							lowerCAmelCase   )

     __lowerCAmelCase  :      List[Any]    =						tokens + [tokenizer.unk_token]
     __lowerCAmelCase  :      Union[str, Any]    =						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase   )	,							lowerCAmelCase   )







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






     """simple docstring"""







     __lowerCAmelCase  :      int    =						self.get_tokenizer()
     __lowerCAmelCase  :      Any    =						tokenizer("""Hello"""	,							"""World"""   )
     __lowerCAmelCase  :      Tuple    =						[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
     self.assertListEqual(tokd["""token_type_ids"""]	,							lowerCAmelCase   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[int]    =						self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

     __lowerCAmelCase  :      Optional[Any]    =						tokenizer.encode("""sequence builders"""	,							add_special_tokens=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						tokenizer.encode("""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase   )

     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						tokenizer.encode(
         """sequence builders"""	,							"""multi-sequence build"""	,							add_special_tokens=lowerCAmelCase	,							add_prefix_space=lowerCAmelCase   )

     __lowerCAmelCase  :      List[str]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   )
     __lowerCAmelCase  :      Union[str, Any]    =						tokenizer.build_inputs_with_special_tokens(lowerCAmelCase	,							lowerCAmelCase   )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						[self.tokenizer_class]
     if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

     for tokenizer_class in tokenizer_classes:
        __lowerCAmelCase  :      int    =						tokenizer_class.from_pretrained("""microsoft/deberta-base"""   )

        __lowerCAmelCase  :      Optional[Any]    =						[
            """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
            """ALBERT incorporates two parameter reduction techniques""",
            """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
            """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
            """ vocabulary embedding.""",
        ]

        __lowerCAmelCase  :      List[str]    =						tokenizer(lowerCAmelCase	,							padding=lowerCAmelCase   )
        __lowerCAmelCase  :      List[str]    =						[tokenizer.decode(lowerCAmelCase	,							skip_special_tokens=lowerCAmelCase   ) for seq in encoding["""input_ids"""]]

        # fmt: off
        __lowerCAmelCase  :      Any    =						{
            """input_ids""": [
                [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 4, 2]
            ],
            """token_type_ids""": [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            """attention_mask""": [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        __lowerCAmelCase  :      int    =						[
            """ALBERT: A Lite BERT for Self-supervised Learning of Language Representations""",
            """ALBERT incorporates two parameter reduction techniques""",
            """The first one is a factorized embedding parameterization. By decomposing the large vocabulary"""
            """ embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"""
            """ vocabulary embedding.""",
        ]

        self.assertDictEqual(encoding.data	,							lowerCAmelCase   )

        for expected, decoded in zip(lowerCAmelCase	,							lowerCAmelCase   ):
           self.assertEqual(lowerCAmelCase	,							lowerCAmelCase   )


