




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
    )
    from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST







class __snake_case			:







   def __init__(      self		:						List[str]					,						_snake_case		:						Any					,						_snake_case		:						Tuple=13					,						_snake_case		:						Tuple=7					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						Optional[int]=True					,						_snake_case		:						List[str]=True					,						_snake_case		:						Dict=True					,						_snake_case		:						Any=99					,						_snake_case		:						str=32					,						_snake_case		:						Optional[Any]=5					,						_snake_case		:						Any=4					,						_snake_case		:						Tuple=37					,						_snake_case		:						Optional[int]="gelu"					,						_snake_case		:						int=0.1					,						_snake_case		:						Optional[Any]=0.1					,						_snake_case		:						Any=128					,						_snake_case		:						List[str]=32					,						_snake_case		:						str=16					,						_snake_case		:						str=2					,						_snake_case		:						List[str]=0.0_2					,						_snake_case		:						str=3					,						_snake_case		:						Optional[Any]=4					,						_snake_case		:						Optional[int]=None					,						):

        """simple docstring"""


        UpperCAmelCase_          =							parent
        UpperCAmelCase_          =							batch_size
        UpperCAmelCase_          =							seq_length
        UpperCAmelCase_          =							is_training
        UpperCAmelCase_          =							use_input_mask
        UpperCAmelCase_          =							use_token_type_ids
        UpperCAmelCase_          =							use_labels
        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							hidden_size
        UpperCAmelCase_          =							num_hidden_layers
        UpperCAmelCase_          =							num_attention_heads
        UpperCAmelCase_          =							intermediate_size
        UpperCAmelCase_          =							hidden_act
        UpperCAmelCase_          =							hidden_dropout_prob
        UpperCAmelCase_          =							attention_probs_dropout_prob
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							type_vocab_size
        UpperCAmelCase_          =							type_sequence_label_size
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							num_labels
        UpperCAmelCase_          =							num_choices
        UpperCAmelCase_          =							scope







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						self.vocab_size)

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

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

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

        UpperCAmelCase_          =							self.get_config()

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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        return NezhaConfig(
            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					,						is_decoder=_snake_case					,						initializer_range=self.initializer_range					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.prepare_config_and_inputs()

        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        UpperCAmelCase_          =							ids_tensor([self.batch_size, self.seq_length]					,						vocab_size=2)

        return (
            config,
            input_ids,
            token_type_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
            encoder_hidden_states,
            encoder_attention_mask,
        )







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]					,						_snake_case		:						List[Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModel(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case)
        UpperCAmelCase_          =							model(_snake_case					,						token_type_ids=_snake_case)
        UpperCAmelCase_          =							model(_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape					,						(self.batch_size, self.hidden_size))







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						Any					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Dict					,						_snake_case		:						List[str]					,						_snake_case		:						Optional[Any]					,						):

        """simple docstring"""


        UpperCAmelCase_          =							True
        UpperCAmelCase_          =							NezhaModel(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						encoder_hidden_states=_snake_case					,						encoder_attention_mask=_snake_case					,						)
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						encoder_hidden_states=_snake_case					,						)
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case)
        self.parent.assertEqual(result.last_hidden_state.shape					,						(self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape					,						(self.batch_size, self.hidden_size))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						str					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]					,						_snake_case		:						str					,						_snake_case		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForMaskedLM(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Any					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict					,						_snake_case		:						int					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForNextSentencePrediction(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, 2))







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Tuple					,						_snake_case		:						Dict					,						_snake_case		:						Tuple					,						_snake_case		:						Any					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForPreTraining(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						next_sentence_label=_snake_case					,						)
        self.parent.assertEqual(result.prediction_logits.shape					,						(self.batch_size, self.seq_length, self.vocab_size))
        self.parent.assertEqual(result.seq_relationship_logits.shape					,						(self.batch_size, 2))







   def lowerCamelCase						(      self		:						Dict					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str					,						_snake_case		:						int					,						_snake_case		:						Tuple					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Tuple					,						_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForQuestionAnswering(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						start_positions=_snake_case					,						end_positions=_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		:						Optional[int]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						List[Any]					,						_snake_case		:						Tuple					,						_snake_case		:						str					,						_snake_case		:						List[Any]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							NezhaForSequenceClassification(_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_labels))







   def lowerCamelCase						(      self		:						Optional[int]					,						_snake_case		:						Optional[int]					,						_snake_case		:						Tuple					,						_snake_case		:						Tuple					,						_snake_case		:						Optional[int]					,						_snake_case		:						Dict					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_labels
        UpperCAmelCase_          =							NezhaForTokenClassification(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.seq_length, self.num_labels))







   def lowerCamelCase						(      self		:						List[Any]					,						_snake_case		:						Optional[int]					,						_snake_case		:						int					,						_snake_case		:						Optional[Any]					,						_snake_case		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str					,						_snake_case		:						str):

        """simple docstring"""


        UpperCAmelCase_          =							self.num_choices
        UpperCAmelCase_          =							NezhaForMultipleChoice(config=_snake_case)
        model.to(_snake_case)
        model.eval()
        UpperCAmelCase_          =							input_ids.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							token_type_ids.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							input_mask.unsqueeze(1).expand(-1					,						self.num_choices					,						-1).contiguous()
        UpperCAmelCase_          =							model(
            _snake_case					,						attention_mask=_snake_case					,						token_type_ids=_snake_case					,						labels=_snake_case					,						)
        self.parent.assertEqual(result.logits.shape					,						(self.batch_size, self.num_choices))







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							self.prepare_config_and_inputs()
        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							config_and_inputs
        UpperCAmelCase_          =							{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict







@require_torch
class __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[str]          =  (
       (
           NezhaModel,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   UpperCAmelCase__  :    str          =  (
       {
           '''feature-extraction''': NezhaModel,
           '''fill-mask''': NezhaForMaskedLM,
           '''question-answering''': NezhaForQuestionAnswering,
           '''text-classification''': NezhaForSequenceClassification,
           '''token-classification''': NezhaForTokenClassification,
           '''zero-shot''': NezhaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCAmelCase__  :    Union[str, Any]          =  True







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Any					,						_snake_case		:						int					,						_snake_case		:						List[str]=False):

        """simple docstring"""


        UpperCAmelCase_          =							super()._prepare_for_class(_snake_case					,						_snake_case					,						return_labels=_snake_case)

        if return_labels:
             if model_class in get_values(_snake_case):
                  UpperCAmelCase_          =							torch.zeros(
                      (self.model_tester.batch_size, self.model_tester.seq_length)					,						dtype=torch.long					,						device=_snake_case)
                  UpperCAmelCase_          =							torch.zeros(
                      self.model_tester.batch_size					,						dtype=torch.long					,						device=_snake_case)
        return inputs_dict







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModelTester(self)
        UpperCAmelCase_          =							ConfigTester(self					,						config_class=_snake_case					,						hidden_size=37)







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        self.config_tester.run_common_tests()







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        (
            (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       (
            UpperCAmelCase_
        )     ,       
        )          =							self.model_tester.prepare_config_and_inputs_for_decoder()

        UpperCAmelCase_          =							None

        self.model_tester.create_and_check_model_as_decoder(
            _snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						_snake_case					,						)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*_snake_case)







   def lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*_snake_case)







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_next_sequence_prediction(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*_snake_case)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*_snake_case)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*_snake_case)







   @slow
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_          =							NezhaModel.from_pretrained(_snake_case)
             self.assertIsNotNone(_snake_case)







   @slow
   @require_torch_gpu
   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_     ,       UpperCAmelCase_          =							self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
             # NezhaForMultipleChoice behaves incorrectly in JIT environments.
             if model_class == NezhaForMultipleChoice:
                  return

             UpperCAmelCase_          =							True
             UpperCAmelCase_          =							model_class(config=_snake_case)

             UpperCAmelCase_          =							self._prepare_for_class(_snake_case					,						_snake_case)
             UpperCAmelCase_          =							torch.jit.trace(
                 _snake_case					,						(inputs_dict['''input_ids'''].to('''cpu'''), inputs_dict['''attention_mask'''].to('''cpu''')))

             with tempfile.TemporaryDirectory() as tmp:
                  torch.jit.save(_snake_case					,						os.path.join(_snake_case					,						'''bert.pt'''))
                  UpperCAmelCase_          =							torch.jit.load(os.path.join(_snake_case					,						'''bert.pt''')					,						map_location=_snake_case)
                  loaded(inputs_dict['''input_ids'''].to(_snake_case)					,						inputs_dict['''attention_mask'''].to(_snake_case))







@require_torch
class __snake_case			(			unittest.TestCase		):







   @slow
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaModel.from_pretrained('''sijunhe/nezha-cn-base''')
        UpperCAmelCase_          =							torch.tensor([[0, 1, 2, 3, 4, 5]])
        UpperCAmelCase_          =							torch.tensor([[0, 1, 1, 1, 1, 1]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)[0]
        UpperCAmelCase_          =							torch.Size((1, 6, 768))
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor([[[0.0_6_8_5, 0.2_4_4_1, 0.1_1_0_2], [0.0_6_0_0, 0.1_9_0_6, 0.1_3_4_9], [0.0_2_2_1, 0.0_8_1_9, 0.0_5_8_6]]])

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,						_snake_case					,						atol=1e-4))







   @slow
   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base''')
        UpperCAmelCase_          =							torch.tensor([[0, 1, 2, 3, 4, 5]])
        UpperCAmelCase_          =							torch.tensor([[1, 1, 1, 1, 1, 1]])
        with torch.no_grad():
             UpperCAmelCase_          =							model(_snake_case					,						attention_mask=_snake_case)[0]
        UpperCAmelCase_          =							torch.Size((1, 6, 21128))
        self.assertEqual(output.shape					,						_snake_case)
        UpperCAmelCase_          =							torch.tensor(
            [[-2.7_9_3_9, -1.7_9_0_2, -2.2_1_8_9], [-2.8_5_8_5, -1.8_9_0_8, -2.3_7_2_3], [-2.6_4_9_9, -1.7_7_5_0, -2.2_5_5_8]])

        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,						_snake_case					,						atol=1e-4))








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					1_0
	def 						_lowercase  (       self  :			int       )       ->     List[str]:
					lowercase_       =					[1, 2, 3, 4]
					lowercase_       =					[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     Optional[Any]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
					lowercase_       =					[1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
					self.assertEqual(truncate_or_pad(SCREAMING_SNAKE_CASE_       ,							self.block_size       ,							0       )       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Any       )       ->     List[Any]:
					lowercase_       =					'''It was the year of Our Lord one thousand seven hundred and
        seventy-five.\n\nSpiritual revelations were conceded to England at that
        favoured period, as at this.'''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					''''''
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							[]       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Union[str, Any]:
					lowercase_       =					(
					    '''It was the year of Our Lord one thousand seven hundred and '''
					    '''seventy-five\n\nSpiritual revelations were conceded to England '''
					    '''at that favoured period, as at this.\n@highlight\n\nIt was the best of times'''
					)
					lowercase_  ,	lowercase_       =					process_story(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					[
					    '''It was the year of Our Lord one thousand seven hundred and seventy-five.''',
					    '''Spiritual revelations were conceded to England at that favoured period, as at this.''',
					]
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )

					lowercase_       =					['''It was the best of times.''']
					self.assertEqual(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[Any]:
					lowercase_       =					torch.tensor([1, 2, 3, 4]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							0       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     Tuple:
					lowercase_       =					torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							2_3       ).numpy()       ,							expected.numpy()       )
	def 						_lowercase  (       self  :			int       )       ->     Dict:
					lowercase_       =					torch.tensor([8, 2, 3, 4, 1, 1, 1]       )
					lowercase_       =					torch.tensor([1, 1, 1, 1, 0, 0, 0]       )
					np.testing.assert_array_equal(build_mask(SCREAMING_SNAKE_CASE_       ,							1       ).numpy()       ,							expected.numpy()       )




	def 						_lowercase  (       self  :			List[str]       )       ->     Tuple:
					lowercase_       =					1_0_1
					lowercase_       =					torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]       )
					lowercase_       =					torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]       )

					lowercase_       =					compute_token_type_ids(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )
					np.testing.assert_array_equal(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       )




