


import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


A   :					Optional[int]	=      logging.get_logger(__name__)







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys())})
       A__      		=    field(
           metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."})
       A__      		=    field(
           default=1_28							,						metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           }							,						)
       A__      		=    field(
           default=lowercase__							,						metadata={"help": "Overwrite the cached training and evaluation sets"})





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.task_name.lower()







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "train"
       A__      		=    "dev"
       A__      		=    "test"







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    42
       A__      		=    42
       A__      		=    42





       def __init__(     self							:    Any      ,   __lowerCamelCase							:    GlueDataTrainingArguments      ,   __lowerCamelCase							:    PreTrainedTokenizerBase      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    Union[str, Split] = Split.train      ,   __lowerCamelCase							:    Optional[str] = None      ,   ):


             '''simple docstring'''



             warnings.warn(
                 "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
                 "library. You can have a look at this example script for pointers: "
                 "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"      ,   __A      ,   )
             lowerCamelCase__	:       int					=						args
             lowerCamelCase__	:       int					=						glue_processors[args.task_name]()
             lowerCamelCase__	:       int					=						glue_output_modes[args.task_name]
             if isinstance(__A      ,   __A ):
                   try:
                         lowerCamelCase__	:       Optional[Any]					=						Split[mode]
                   except KeyError:
                         raise KeyError("mode is not a valid split name" )
        # Load data features from cache or dataset file
             lowerCamelCase__	:       Optional[int]					=						os.path.join(
                 cache_dir if cache_dir is not None else args.data_dir      ,   f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"      ,   )
             lowerCamelCase__	:       Tuple					=						self.processor.get_labels()
             if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
                 "RobertaTokenizer",
                 "RobertaTokenizerFast",
                 "XLMRobertaTokenizer",
                 "BartTokenizer",
                 "BartTokenizerFast",
             ):
                   # HACK(label indices are swapped in RoBERTa pretrained model)
                   lowerCamelCase__	:       Dict					=						label_list[2], label_list[1]
             lowerCamelCase__	:       Dict					=						label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             lowerCamelCase__	:       Tuple					=						cached_features_file + ".lock"
             with FileLock(__A ):
                   if os.path.exists(__A ) and not args.overwrite_cache:
                         lowerCamelCase__	:       Any					=						time.time()
                         lowerCamelCase__	:       Dict					=						torch.load(__A )
                         logger.info(
                             f"Loading features from cached file {cached_features_file} [took %.3f s]"      ,   time.time() - start )
                   else:
                         logger.info(f"Creating features from dataset file at {args.data_dir}" )

                         if mode == Split.dev:
                               lowerCamelCase__	:       Any					=						self.processor.get_dev_examples(args.data_dir )
                         elif mode == Split.test:
                               lowerCamelCase__	:       Optional[int]					=						self.processor.get_test_examples(args.data_dir )
                         else:
                               lowerCamelCase__	:       int					=						self.processor.get_train_examples(args.data_dir )
                         if limit_length is not None:
                               lowerCamelCase__	:       Union[str, Any]					=						examples[:limit_length]
                         lowerCamelCase__	:       List[str]					=						glue_convert_examples_to_features(
                             __A      ,   __A      ,   max_length=args.max_seq_length      ,   label_list=__A      ,   output_mode=self.output_mode      ,   )
                         lowerCamelCase__	:       int					=						time.time()
                         torch.save(self.features      ,   __A )
                         # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                         logger.info(
                             f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]" )





       def __len__(     self							:    str ):


             '''simple docstring'''



             return len(self.features )





       def __getitem__(     self							:    str      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             return self.features[i]





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             return self.label_list






import unittest

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

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


if is_torch_available():
    import torch

    from transformers import (
        MODEL_FOR_PRETRAINING_MAPPING,
        AlbertForMaskedLM,
        AlbertForMultipleChoice,
        AlbertForPreTraining,
        AlbertForQuestionAnswering,
        AlbertForSequenceClassification,
        AlbertForTokenClassification,
        AlbertModel,
    )
    from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]=13      ,   __lowerCamelCase							:    Dict=7      ,   __lowerCamelCase							:    List[Any]=True      ,   __lowerCamelCase							:    List[Any]=True      ,   __lowerCamelCase							:    Union[str, Any]=True      ,   __lowerCamelCase							:    Optional[Any]=True      ,   __lowerCamelCase							:    Optional[int]=99      ,   __lowerCamelCase							:    List[Any]=16      ,   __lowerCamelCase							:    Optional[Any]=36      ,   __lowerCamelCase							:    Optional[int]=6      ,   __lowerCamelCase							:    Union[str, Any]=6      ,   __lowerCamelCase							:    Optional[int]=6      ,   __lowerCamelCase							:    Dict=37      ,   __lowerCamelCase							:    List[Any]="gelu"      ,   __lowerCamelCase							:    Tuple=0.1      ,   __lowerCamelCase							:    Optional[Any]=0.1      ,   __lowerCamelCase							:    List[Any]=512      ,   __lowerCamelCase							:    Dict=16      ,   __lowerCamelCase							:    Union[str, Any]=2      ,   __lowerCamelCase							:    Dict=0.0_2      ,   __lowerCamelCase							:    Optional[Any]=3      ,   __lowerCamelCase							:    Dict=4      ,   __lowerCamelCase							:    Dict=None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						parent
             lowerCamelCase__	:       List[Any]					=						batch_size
             lowerCamelCase__	:       Any					=						seq_length
             lowerCamelCase__	:       List[str]					=						is_training
             lowerCamelCase__	:       int					=						use_input_mask
             lowerCamelCase__	:       List[str]					=						use_token_type_ids
             lowerCamelCase__	:       int					=						use_labels
             lowerCamelCase__	:       Dict					=						vocab_size
             lowerCamelCase__	:       List[Any]					=						embedding_size
             lowerCamelCase__	:       Dict					=						hidden_size
             lowerCamelCase__	:       Any					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_hidden_groups
             lowerCamelCase__	:       Optional[int]					=						num_attention_heads
             lowerCamelCase__	:       List[str]					=						intermediate_size
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       str					=						hidden_dropout_prob
             lowerCamelCase__	:       Union[str, Any]					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Optional[int]					=						max_position_embeddings
             lowerCamelCase__	:       List[Any]					=						type_vocab_size
             lowerCamelCase__	:       Optional[Any]					=						type_sequence_label_size
             lowerCamelCase__	:       Optional[int]					=						initializer_range
             lowerCamelCase__	:       str					=						num_labels
             lowerCamelCase__	:       List[Any]					=						num_choices
             lowerCamelCase__	:       Any					=						scope





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size )

             lowerCamelCase__	:       Optional[int]					=						None
             if self.use_input_mask:
                   lowerCamelCase__	:       Any					=						random_attention_mask([self.batch_size, self.seq_length] )

             lowerCamelCase__	:       Optional[Any]					=						None
             if self.use_token_type_ids:
                   lowerCamelCase__	:       Union[str, Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.type_vocab_size )

             lowerCamelCase__	:       Tuple					=						None
             lowerCamelCase__	:       List[str]					=						None
             lowerCamelCase__	:       int					=						None
             if self.use_labels:
                   lowerCamelCase__	:       Tuple					=						ids_tensor([self.batch_size]      ,   self.type_sequence_label_size )
                   lowerCamelCase__	:       Union[str, Any]					=						ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels )
                   lowerCamelCase__	:       str					=						ids_tensor([self.batch_size]      ,   self.num_choices )

             lowerCamelCase__	:       Union[str, Any]					=						self.get_config()

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





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             return AlbertConfig(
                 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      ,   num_hidden_groups=self.num_hidden_groups      ,   )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						AlbertModel(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   token_type_ids=__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						model(__lowerCamelCase )
             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							:    Union[str, Any]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						AlbertForPreTraining(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Union[str, Any]					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase      ,   sentence_order_label=__lowerCamelCase      ,   )
             self.parent.assertEqual(result.prediction_logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )
             self.parent.assertEqual(result.sop_logits.shape      ,   (self.batch_size, config.num_labels) )





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						AlbertForMaskedLM(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size) )





       def 		lowerCAmelCase  (     self							:    Union[str, Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						AlbertForQuestionAnswering(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       str					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   start_positions=__lowerCamelCase      ,   end_positions=__lowerCamelCase      ,   )
             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]      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Optional[int]      ,   __lowerCamelCase							:    Union[str, Any]      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.num_labels
             lowerCamelCase__	:       Optional[int]					=						AlbertForSequenceClassification(__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    Dict      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						self.num_labels
             lowerCamelCase__	:       List[str]					=						AlbertForTokenClassification(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Tuple					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.num_labels) )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    Union[str, Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.num_choices
             lowerCamelCase__	:       Optional[int]					=						AlbertForMultipleChoice(config=__lowerCamelCase )
             model.to(__lowerCamelCase )
             model.eval()
             lowerCamelCase__	:       Any					=						input_ids.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       Optional[int]					=						token_type_ids.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       Dict					=						input_mask.unsqueeze(1 ).expand(-1      ,   self.num_choices      ,   -1 ).contiguous()
             lowerCamelCase__	:       int					=						model(
                 __lowerCamelCase      ,   attention_mask=__lowerCamelCase      ,   token_type_ids=__lowerCamelCase      ,   labels=__lowerCamelCase      ,   )
             self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_choices) )





       def 		lowerCAmelCase  (     self							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.prepare_config_and_inputs()
             (
                 (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     (
                 lowerCamelCase__
             )					,     
             )	:       Union[str, Any]					=						config_and_inputs
             lowerCamelCase__	:       str					=						{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
             return config, inputs_dict







@require_torch
class     _lowercase				(							lowercase__							,						lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    (
           (
               AlbertModel,
               AlbertForPreTraining,
               AlbertForMaskedLM,
               AlbertForMultipleChoice,
               AlbertForSequenceClassification,
               AlbertForTokenClassification,
               AlbertForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       A__      		=    (
           {
               "feature-extraction": AlbertModel,
               "fill-mask": AlbertForMaskedLM,
               "question-answering": AlbertForQuestionAnswering,
               "text-classification": AlbertForSequenceClassification,
               "token-classification": AlbertForTokenClassification,
               "zero-shot": AlbertForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    List[str]      ,   __lowerCamelCase							:    int      ,   __lowerCamelCase							:    Tuple      ,   __lowerCamelCase							:    Dict=False ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						super()._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase )

             if return_labels:
                   if model_class in get_values(__lowerCamelCase ):
                         lowerCamelCase__	:       Union[str, Any]					=						torch.zeros(
                             (self.model_tester.batch_size, self.model_tester.seq_length)      ,   dtype=torch.long      ,   device=__lowerCamelCase )
                         lowerCamelCase__	:       List[str]					=						torch.zeros(
                             self.model_tester.batch_size      ,   dtype=torch.long      ,   device=__lowerCamelCase )
             return inputs_dict





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						AlbertModelTester(self )
             lowerCamelCase__	:       Optional[Any]					=						ConfigTester(self      ,   config_class=__lowerCamelCase      ,   hidden_size=37 )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             self.config_tester.run_common_tests()





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



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





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


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_pretraining(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__lowerCamelCase )





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


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       List[str]					=						self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   lowerCamelCase__	:       Dict					=						type
                   self.model_tester.create_and_check_model(*__lowerCamelCase )





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


             '''simple docstring'''



             for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase__	:       List[str]					=						AlbertModel.from_pretrained(__lowerCamelCase )
                   self.assertIsNotNone(__lowerCamelCase )







@require_torch
class     _lowercase				(							unittest.TestCase):


       """simple docstring"""




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


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						AlbertModel.from_pretrained("albert-base-v2" )
             lowerCamelCase__	:       Any					=						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
             lowerCamelCase__	:       int					=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
             with torch.no_grad():
                   lowerCamelCase__	:       List[Any]					=						model(__lowerCamelCase      ,   attention_mask=__lowerCamelCase )[0]
             lowerCamelCase__	:       Tuple					=						torch.Size((1, 11, 768) )
             self.assertEqual(output.shape      ,   __lowerCamelCase )
             lowerCamelCase__	:       Dict					=						torch.tensor(
                 [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]] )

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


