





'''simple docstring'''




from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFConvBertForMaskedLM,
     TFConvBertForMultipleChoice,
     TFConvBertForQuestionAnswering,
     TFConvBertForSequenceClassification,
     TFConvBertForTokenClassification,
     TFConvBertModel,
 )
class 			_lowerCAmelCase		:






     """simple docstring"""






     def __init__(   self							:			Optional[int]   ,       __snake_case							:			List[str]   ,       __snake_case							:			List[Any]=13   ,       __snake_case							:			Any=7   ,       __snake_case							:			int=True   ,       __snake_case							:			Dict=True   ,       __snake_case							:			Any=True   ,       __snake_case							:			str=True   ,       __snake_case							:			List[str]=99   ,       __snake_case							:			Union[str, Any]=32   ,       __snake_case							:			Optional[int]=2   ,       __snake_case							:			List[str]=4   ,       __snake_case							:			Optional[Any]=37   ,       __snake_case							:			Dict="gelu"   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			List[str]=5_12   ,       __snake_case							:			Optional[Any]=16   ,       __snake_case							:			Optional[Any]=2   ,       __snake_case							:			int=0.02   ,       __snake_case							:			List[str]=3   ,       __snake_case							:			Optional[Any]=4   ,       __snake_case							:			Any=None   ,       )->	Tuple:
        snake_case 						=   parent
        snake_case 						=   13
        snake_case 						=   7
        snake_case 						=   True
        snake_case 						=   True
        snake_case 						=   True
        snake_case 						=   True
        snake_case 						=   99
        snake_case 						=   3_84
        snake_case 						=   2
        snake_case 						=   4
        snake_case 						=   37
        snake_case 						=   """gelu"""
        snake_case 						=   0.1
        snake_case 						=   0.1
        snake_case 						=   5_12
        snake_case 						=   16
        snake_case 						=   2
        snake_case 						=   0.02
        snake_case 						=   3
        snake_case 						=   4
        snake_case 						=   1_28
        snake_case 						=   2
        snake_case 						=   9
        snake_case 						=   1
        snake_case 						=   None



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	int:
        snake_case 						=   ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size						)

        snake_case 						=   None
        if self.use_input_mask:
           snake_case 						=   random_attention_mask([self.batch_size, self.seq_length]						)

        snake_case 						=   None
        if self.use_token_type_ids:
           snake_case 						=   ids_tensor([self.batch_size, self.seq_length]   ,       self.type_vocab_size						)

        snake_case 						=   None
        snake_case 						=   None
        snake_case 						=   None
        if self.use_labels:
           snake_case 						=   ids_tensor([self.batch_size]   ,       self.type_sequence_label_size						)
           snake_case 						=   ids_tensor([self.batch_size, self.seq_length]   ,       self.num_labels						)
           snake_case 						=   ids_tensor([self.batch_size]   ,       self.num_choices						)

        snake_case 						=   ConvBertConfig(
            vocab_size=self.vocab_size   ,       hidden_size=self.hidden_size   ,       num_hidden_layers=self.num_hidden_layers   ,       num_attention_heads=self.num_attention_heads   ,       intermediate_size=self.intermediate_size   ,       hidden_act=self.hidden_act   ,       hidden_dropout_prob=self.hidden_dropout_prob   ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,       max_position_embeddings=self.max_position_embeddings   ,       type_vocab_size=self.type_vocab_size   ,       initializer_range=self.initializer_range   ,       return_dict=__snake_case   ,       )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Any   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			int   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Any   ,       __snake_case							:			int						)->	Dict:
        snake_case 						=   TFConvBertModel(config=__snake_case						)
        snake_case 						=   {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

        snake_case 						=   [input_ids, input_mask]
        snake_case 						=   model(__snake_case						)

        snake_case 						=   model(__snake_case						)

        self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			int   ,       __snake_case							:			int   ,       __snake_case							:			List[str]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Any   ,       __snake_case							:			Any   ,       __snake_case							:			List[Any]						)->	Optional[int]:
        snake_case 						=   TFConvBertForMaskedLM(config=__snake_case						)
        snake_case 						=   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			str   ,       __snake_case							:			Tuple   ,       __snake_case							:			Dict   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Dict						)->	Union[str, Any]:
        snake_case 						=   self.num_labels
        snake_case 						=   TFConvBertForSequenceClassification(config=__snake_case						)
        snake_case 						=   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)						)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Any   ,       __snake_case							:			int   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Optional[Any]						)->	Optional[int]:
        snake_case 						=   self.num_choices
        snake_case 						=   TFConvBertForMultipleChoice(config=__snake_case						)
        snake_case 						=   tf.tile(tf.expand_dims(__snake_case   ,       1						)   ,       (1, self.num_choices, 1)						)
        snake_case 						=   tf.tile(tf.expand_dims(__snake_case   ,       1						)   ,       (1, self.num_choices, 1)						)
        snake_case 						=   tf.tile(tf.expand_dims(__snake_case   ,       1						)   ,       (1, self.num_choices, 1)						)
        snake_case 						=   {
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
            """token_type_ids""": multiple_choice_token_type_ids,
        }
        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_choices)						)



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Dict   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Tuple						)->	Union[str, Any]:
        snake_case 						=   self.num_labels
        snake_case 						=   TFConvBertForTokenClassification(config=__snake_case						)
        snake_case 						=   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }
        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)						)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			List[str]   ,       __snake_case							:			str   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Any						)->	Tuple:
        snake_case 						=   TFConvBertForQuestionAnswering(config=__snake_case						)
        snake_case 						=   {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
            """token_type_ids""": token_type_ids,
        }

        snake_case 						=   model(__snake_case						)
        self.parent.assertEqual(result.start_logits.shape   ,       (self.batch_size, self.seq_length)						)
        self.parent.assertEqual(result.end_logits.shape   ,       (self.batch_size, self.seq_length)						)



     def      lowerCAmelCase			(   self							:			str						)->	Dict:
        snake_case 						=   self.prepare_config_and_inputs()
        (
            (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    
        ) 						=   config_and_inputs
        snake_case 						=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_tf
class 			_lowerCAmelCase		(			A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   (
         (
             TFConvBertModel,
             TFConvBertForMaskedLM,
             TFConvBertForQuestionAnswering,
             TFConvBertForSequenceClassification,
             TFConvBertForTokenClassification,
             TFConvBertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     snake_case_						      =   (
         {
             "feature-extraction": TFConvBertModel,
             "fill-mask": TFConvBertForMaskedLM,
             "question-answering": TFConvBertForQuestionAnswering,
             "text-classification": TFConvBertForSequenceClassification,
             "token-classification": TFConvBertForTokenClassification,
             "zero-shot": TFConvBertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     snake_case_						      =   False
     snake_case_						      =   False
     snake_case_						      =   False



     def      lowerCAmelCase			(   self							:			Dict						)->	List[Any]:
        snake_case 						=   TFConvBertModelTester(self						)
        snake_case 						=   ConfigTester(self   ,       config_class=__snake_case   ,       hidden_size=37						)



     def      lowerCAmelCase			(   self							:			str						)->	Dict:
        self.config_tester.run_common_tests()



     def      lowerCAmelCase			(   self							:			List[str]						)->	Optional[int]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Tuple						)->	str:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	List[str]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	Tuple:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[Any]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	str:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			int						)->	Optional[Any]:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()
        snake_case 						=   True
        snake_case 						=   True

        if hasattr(__snake_case   ,       """use_cache"""						):
           snake_case 						=   True

        snake_case 						=   getattr(self.model_tester   ,       """encoder_seq_length"""   ,       self.model_tester.seq_length						)
        snake_case 						=   getattr(self.model_tester   ,       """key_length"""   ,       __snake_case						)

        for model_class in self.all_model_classes:
           snake_case 						=   self._prepare_for_class(__snake_case   ,       __snake_case						)
           snake_case 						=   model_class(__snake_case						)
           snake_case 						=   len(model(__snake_case						)						)

           with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(__snake_case   ,       saved_model=__snake_case						)
              snake_case 						=   os.path.join(__snake_case   ,       """saved_model"""   ,       """1"""						)
              snake_case 						=   tf.keras.models.load_model(__snake_case						)
              snake_case 						=   model(__snake_case						)

              if self.is_encoder_decoder:
                 snake_case 						=   outputs["""encoder_hidden_states"""]
                 snake_case 						=   outputs["""encoder_attentions"""]
              else:
                 snake_case 						=   outputs["""hidden_states"""]
                 snake_case 						=   outputs["""attentions"""]

              self.assertEqual(len(__snake_case						)   ,       __snake_case						)

              snake_case 						=   getattr(
                  self.model_tester   ,       """expected_num_hidden_layers"""   ,       self.model_tester.num_hidden_layers + 1						)

              self.assertEqual(len(__snake_case						)   ,       __snake_case						)
              self.assertListEqual(
                  list(output_hidden_states[0].shape[-2:]						)   ,       [self.model_tester.seq_length, self.model_tester.hidden_size]   ,       )

              self.assertEqual(len(__snake_case						)   ,       self.model_tester.num_hidden_layers						)
              self.assertListEqual(
                  list(output_attentions[0].shape[-3:]						)   ,       [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]   ,       )



     @slow
     def      lowerCAmelCase			(   self							:			List[Any]						)->	Union[str, Any]:
        snake_case 						=   TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""						)
        self.assertIsNotNone(__snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	List[Any]:
        snake_case						,    snake_case 						=   self.model_tester.prepare_config_and_inputs_for_common()
        snake_case 						=   True
        snake_case 						=   getattr(self.model_tester   ,       """decoder_seq_length"""   ,       self.model_tester.seq_length						)
        snake_case 						=   getattr(self.model_tester   ,       """encoder_seq_length"""   ,       self.model_tester.seq_length						)
        snake_case 						=   getattr(self.model_tester   ,       """key_length"""   ,       __snake_case						)
        snake_case 						=   getattr(self.model_tester   ,       """key_length"""   ,       __snake_case						)

        def check_decoder_attentions_output(__snake_case							:			Tuple						):
           snake_case 						=   len(__snake_case						)
           self.assertEqual(out_len % 2   ,       0						)
           snake_case 						=   outputs.decoder_attentions
           self.assertEqual(len(__snake_case						)   ,       self.model_tester.num_hidden_layers						)
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]						)   ,       [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]   ,       )

        def check_encoder_attentions_output(__snake_case							:			Tuple						):
           snake_case 						=   [
               t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
           ]
           self.assertEqual(len(__snake_case						)   ,       self.model_tester.num_hidden_layers						)
           self.assertListEqual(
               list(attentions[0].shape[-3:]						)   ,       [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]   ,       )

        for model_class in self.all_model_classes:
           snake_case 						=   True
           snake_case 						=   False
           snake_case 						=   model_class(__snake_case						)
           snake_case 						=   model(self._prepare_for_class(__snake_case   ,       __snake_case						)						)
           snake_case 						=   len(__snake_case						)
           self.assertEqual(config.output_hidden_states   ,       __snake_case						)
           check_encoder_attentions_output(__snake_case						)

           if self.is_encoder_decoder:
              snake_case 						=   model_class(__snake_case						)
              snake_case 						=   model(self._prepare_for_class(__snake_case   ,       __snake_case						)						)
              self.assertEqual(config.output_hidden_states   ,       __snake_case						)
              check_decoder_attentions_output(__snake_case						)

           # Check that output attentions can also be changed via the config
           del inputs_dict["output_attentions"]
           snake_case 						=   True
           snake_case 						=   model_class(__snake_case						)
           snake_case 						=   model(self._prepare_for_class(__snake_case   ,       __snake_case						)						)
           self.assertEqual(config.output_hidden_states   ,       __snake_case						)
           check_encoder_attentions_output(__snake_case						)

           # Check attention is always last and order is fine
           snake_case 						=   True
           snake_case 						=   True
           snake_case 						=   model_class(__snake_case						)
           snake_case 						=   model(self._prepare_for_class(__snake_case   ,       __snake_case						)						)

           self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   ,       len(__snake_case						)						)
           self.assertEqual(model.config.output_hidden_states   ,       __snake_case						)
           check_encoder_attentions_output(__snake_case						)




@require_tf
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @slow
     def      lowerCAmelCase			(   self							:			Optional[Any]						)->	Optional[Any]:
        snake_case 						=   TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""						)
        snake_case 						=   tf.constant([[0, 1, 2, 3, 4, 5]]						)
        snake_case 						=   model(__snake_case						)[0]

        snake_case 						=   [1, 6, 7_68]
        self.assertEqual(output.shape   ,       __snake_case						)

        snake_case 						=   tf.constant(
            [
                [
                    [-0.03_47_54_93, -0.4_68_60_34, -0.30_63_88_32],
                    [0.22_63_72_48, -0.26_98_86_46, -0.7_42_34_24],
                    [0.10_32_48_68, -0.45_01_35_08, -0.58_28_07_84],
                ]
            ]						)
        tf.debugging.assert_near(output[:, :3, :3]   ,       __snake_case   ,       atol=1e-4						)








'''simple docstring'''




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 			_lowerCAmelCase		(			A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   DebertaTokenizer
     snake_case_						      =   True
     snake_case_						      =   DebertaTokenizerFast



     def      lowerCAmelCase			(   self							:			Any						)->	Optional[int]:
        super().setUp()

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

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



     def      lowerCAmelCase			(   self							:			Optional[Any]   ,       **__snake_case							:			Union[str, Any]						)->	int:
        kwargs.update(self.special_tokens_map						)
        return self.tokenizer_class.from_pretrained(self.tmpdirname   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Any   ,       __snake_case							:			int						)->	Any:
        snake_case 						=   """lower newer"""
        snake_case 						=   """lower newer"""
        return input_text, output_text



     def      lowerCAmelCase			(   self							:			str						)->	Any:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   """lower newer"""
        snake_case 						=   ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
        snake_case 						=   tokenizer.tokenize(__snake_case						)
        self.assertListEqual(__snake_case   ,       __snake_case						)

        snake_case 						=   tokens + [tokenizer.unk_token]
        snake_case 						=   [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case						)   ,       __snake_case						)



     def      lowerCAmelCase			(   self							:			Any						)->	List[str]:
        snake_case 						=   self.get_tokenizer()
        snake_case 						=   tokenizer("""Hello"""   ,       """World"""						)
        snake_case 						=   [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd["""token_type_ids"""]   ,       __snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Union[str, Any]:
        snake_case 						=   self.tokenizer_class.from_pretrained("""microsoft/deberta-base"""						)

        snake_case 						=   tokenizer.encode("""sequence builders"""   ,       add_special_tokens=__snake_case						)
        snake_case 						=   tokenizer.encode("""multi-sequence build"""   ,       add_special_tokens=__snake_case						)

        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)
        snake_case 						=   tokenizer.encode(
            """sequence builders"""   ,       """multi-sequence build"""   ,       add_special_tokens=__snake_case   ,       add_prefix_space=__snake_case						)

        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case						)
        snake_case 						=   tokenizer.build_inputs_with_special_tokens(__snake_case   ,       __snake_case						)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode



     @slow
     def      lowerCAmelCase			(   self							:			Any						)->	Union[str, Any]:
        snake_case 						=   [self.tokenizer_class]
        if self.test_rust_tokenizer:
           tokenizer_classes.append(self.rust_tokenizer_class						)

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

           snake_case 						=   [
               """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.""",
           ]

           snake_case 						=   tokenizer(__snake_case   ,       padding=__snake_case						)
           snake_case 						=   [tokenizer.decode(__snake_case   ,       skip_special_tokens=__snake_case						) for seq in encoding["""input_ids"""]]

           # fmt: off
           snake_case 						=   {
               """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

           snake_case 						=   [
               """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   ,       __snake_case						)

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

