
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowercase__       =16
lowercase__       =32




def    __UpperCamelCase     (				lowerCAmelCase__							:				Accelerator  ,   lowerCAmelCase__							:				int = 1_6  ):
 __a :  Tuple						=  AutoTokenizer.from_pretrained('''bert-base-cased'''  )
 __a :  Union[str, Any]						=  load_dataset('''glue'''  ,   '''mrpc'''  )

 def tokenize_function(lowerCAmelCase__							:				Optional[int]  ):
  # max_length=None => use the model max length (it's actually the default)
  __a :  int						=  tokenizer(examples['''sentence1''']  ,   examples['''sentence2''']  ,   truncation=lowerCAmelCase__  ,   max_length=lowerCAmelCase__  )
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 # starting with the main process first:
 with accelerator.main_process_first():
  __a :  Dict						=  datasets.map(
      lowerCAmelCase__  ,   batched=lowerCAmelCase__  ,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']  ,   )

 # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
 # transformers library
 __a :  List[Any]						=  tokenized_datasets.rename_column('''label'''  ,   '''labels'''  )

 def collate_fn(lowerCAmelCase__							:				Optional[Any]  ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  __a :  Dict						=  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
  # When using mixed precision we want round multiples of 8/16
  if accelerator.mixed_precision == "fp8":
   __a :  int						=  1_6
  elif accelerator.mixed_precision != "no":
   __a :  Optional[int]						=  8
  else:
   __a :  Dict						=  None

  return tokenizer.pad(
      lowerCAmelCase__  ,   padding='''longest'''  ,   max_length=lowerCAmelCase__  ,   pad_to_multiple_of=lowerCAmelCase__  ,   return_tensors='''pt'''  ,   )

 # Instantiate dataloaders.
 __a :  Dict						=  DataLoader(
     tokenized_datasets['''train''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )
 __a :  Tuple						=  DataLoader(
     tokenized_datasets['''validation''']  ,   shuffle=lowerCAmelCase__  ,   collate_fn=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  )

 return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowercase__       =mocked_dataloaders  # noqa: F811




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[Any]  ,   lowerCAmelCase__							:				Optional[Any]  ):
 # For testing only
 if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  ,   lowerCAmelCase__  ) == "1":
  __a :  Tuple						=  2
 # Initialize accelerator
 __a :  Dict						=  Accelerator(cpu=args.cpu  ,   mixed_precision=args.mixed_precision  )
 # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
 __a :  Optional[int]						=  config['''lr''']
 __a :  Dict						=  int(config['''num_epochs''']  )
 __a :  Optional[Any]						=  int(config['''seed''']  )
 __a :  Dict						=  int(config['''batch_size''']  )

 __a :  str						=  evaluate.load('''glue'''  ,   '''mrpc'''  )

 # If the batch size is too big we use gradient accumulation
 __a :  List[Any]						=  1
 if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
  __a :  Any						=  batch_size // MAX_GPU_BATCH_SIZE
  __a :  List[str]						=  MAX_GPU_BATCH_SIZE

 set_seed(lowerCAmelCase__  )
 __a				,       __a :  Any						=  get_dataloaders(lowerCAmelCase__  ,   lowerCAmelCase__  )
 # Instantiate the model (we build the model here so that the seed also control new weights initialization)
 __a :  Union[str, Any]						=  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  ,   return_dict=lowerCAmelCase__  )

 # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
 # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
 # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
 __a :  List[Any]						=  model.to(accelerator.device  )

 # Instantiate optimizer
 __a :  Any						=  AdamW(params=model.parameters()  ,   lr=lowerCAmelCase__  )

 # Instantiate scheduler
 __a :  Tuple						=  get_linear_schedule_with_warmup(
     optimizer=lowerCAmelCase__  ,   num_warmup_steps=1_0_0  ,   num_training_steps=(len(lowerCAmelCase__  ) * num_epochs) // gradient_accumulation_steps  ,   )

 # Prepare everything
 # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
 # prepare method.
 __a				,       __a				,       __a				,       __a				,       __a :  Any						=  accelerator.prepare(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

 # Now we train the model
 for epoch in range(lowerCAmelCase__  ):
  model.train()
  for step, batch in enumerate(lowerCAmelCase__  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device  )
   __a :  str						=  model(**lowerCAmelCase__  )
   __a :  Tuple						=  outputs.loss
   __a :  Any						=  loss / gradient_accumulation_steps
   accelerator.backward(lowerCAmelCase__  )
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

  model.eval()
  __a :  List[str]						=  0
  for step, batch in enumerate(lowerCAmelCase__  ):
   # We could avoid this line since we set the accelerator with `device_placement=True`.
   batch.to(accelerator.device  )
   with torch.no_grad():
    __a :  Optional[Any]						=  model(**lowerCAmelCase__  )
   __a :  Dict						=  outputs.logits.argmax(dim=-1  )
   __a				,       __a :  int						=  accelerator.gather((predictions, batch['''labels'''])  )
   # New Code #
   # First we check if it's a distributed system
   if accelerator.use_distributed:
    # Then see if we're on the last batch of our eval dataloader
    if step == len(lowerCAmelCase__  ) - 1:
     # Last batch needs to be truncated on distributed systems as it contains additional samples
     __a :  int						=  predictions[: len(eval_dataloader.dataset  ) - samples_seen]
     __a :  Union[str, Any]						=  references[: len(eval_dataloader.dataset  ) - samples_seen]
    else:
     # Otherwise we add the number of samples seen
     samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
   metric.add_batch(
       predictions=lowerCAmelCase__  ,   references=lowerCAmelCase__  ,   )

  __a :  str						=  metric.compute()
  # Use accelerator.print to print only on the main process.
  accelerator.print(f"epoch {epoch}:"  ,   lowerCAmelCase__  )




def    __UpperCamelCase     (				):
 __a :  Union[str, Any]						=  argparse.ArgumentParser(description='''Simple example of training script.'''  )
 parser.add_argument(
     '''--mixed_precision'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,   help='''Whether to use mixed precision. Choose'''
     '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
     '''and an Nvidia Ampere GPU.'''  ,   )
 parser.add_argument('''--cpu'''  ,   action='''store_true'''  ,   help='''If passed, will train on the CPU.'''  )
 __a :  Tuple						=  parser.parse_args()
 __a :  List[Any]						=  {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 4_2, '''batch_size''': 1_6}
 training_function(lowerCAmelCase__  ,   lowerCAmelCase__  )


if __name__ == "__main__":
      main()


import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
          MobileBertModel,
      )



class 		UpperCamelCase__  :
   def __init__(self :					List[Any]					,				snake_case_ :					int					,				snake_case_ :					List[str]=1_3					,				snake_case_ :					Tuple=7					,				snake_case_ :					List[Any]=True					,				snake_case_ :					List[Any]=True					,				snake_case_ :					Dict=True					,				snake_case_ :					Optional[int]=True					,				snake_case_ :					str=9_9					,				snake_case_ :					Dict=6_4					,				snake_case_ :					Any=3_2					,				snake_case_ :					str=5					,				snake_case_ :					int=4					,				snake_case_ :					List[Any]=3_7					,				snake_case_ :					Any="gelu"					,				snake_case_ :					Dict=0.1					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					str=5_1_2					,				snake_case_ :					Any=1_6					,				snake_case_ :					str=2					,				snake_case_ :					int=0.02					,				snake_case_ :					Union[str, Any]=3					,				snake_case_ :					Optional[Any]=4					,				snake_case_ :					List[Any]=None					,				):
    __a :  Any						=  parent
    __a :  Optional[int]						=  batch_size
    __a :  Any						=  seq_length
    __a :  int						=  is_training
    __a :  Optional[int]						=  use_input_mask
    __a :  List[Any]						=  use_token_type_ids
    __a :  Dict						=  use_labels
    __a :  Tuple						=  vocab_size
    __a :  str						=  hidden_size
    __a :  List[Any]						=  embedding_size
    __a :  List[Any]						=  num_hidden_layers
    __a :  str						=  num_attention_heads
    __a :  str						=  intermediate_size
    __a :  Union[str, Any]						=  hidden_act
    __a :  Optional[Any]						=  hidden_dropout_prob
    __a :  Tuple						=  attention_probs_dropout_prob
    __a :  Union[str, Any]						=  max_position_embeddings
    __a :  Any						=  type_vocab_size
    __a :  int						=  type_sequence_label_size
    __a :  int						=  initializer_range
    __a :  int						=  num_labels
    __a :  Union[str, Any]						=  num_choices
    __a :  Dict						=  scope
   def       lowerCAmelCase			(self :					str   ):
    __a :  int						=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size   )

    __a :  List[Any]						=  None
    if self.use_input_mask:
     __a :  Dict						=  random_attention_mask([self.batch_size, self.seq_length]   )

    __a :  Optional[Any]						=  None
    if self.use_token_type_ids:
     __a :  List[Any]						=  ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size   )

    __a :  Dict						=  None
    __a :  List[str]						=  None
    __a :  Optional[Any]						=  None
    if self.use_labels:
     __a :  str						=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size   )
     __a :  Dict						=  ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels   )
     __a :  Union[str, Any]						=  ids_tensor([self.batch_size]					,				self.num_choices   )

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

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def       lowerCAmelCase			(self :					int   ):
    return MobileBertConfig(
        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					,				embedding_size=self.embedding_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 :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[str]					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					Any					,				snake_case_ :					List[Any]					,				snake_case_ :					Any   ):
    __a :  Any						=  MobileBertModel(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[str]						=  model(snake_case_					,				attention_mask=snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  model(snake_case_					,				token_type_ids=snake_case_   )
    __a :  Optional[Any]						=  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 :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Tuple					,				snake_case_ :					Dict					,				snake_case_ :					Tuple					,				snake_case_ :					str					,				snake_case_ :					List[Any]   ):
    __a :  str						=  MobileBertForMaskedLM(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  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_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					Dict   ):
    __a :  Optional[Any]						=  MobileBertForNextSentencePrediction(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  int						=  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 :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]					,				snake_case_ :					Any					,				snake_case_ :					Any					,				snake_case_ :					Union[str, Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[Any]   ):
    __a :  str						=  MobileBertForPreTraining(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Union[str, Any]						=  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_ :					Tuple					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					int					,				snake_case_ :					int					,				snake_case_ :					str					,				snake_case_ :					str   ):
    __a :  str						=  MobileBertForQuestionAnswering(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Optional[Any]						=  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_ :					Any					,				snake_case_ :					List[str]					,				snake_case_ :					List[Any]					,				snake_case_ :					str					,				snake_case_ :					Any					,				snake_case_ :					Tuple					,				snake_case_ :					Optional[int]   ):
    __a :  Any						=  self.num_labels
    __a :  Union[str, Any]						=  MobileBertForSequenceClassification(snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Tuple						=  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 :					List[Any]					,				snake_case_ :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					Dict					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Dict					,				snake_case_ :					Optional[int]   ):
    __a :  Union[str, Any]						=  self.num_labels
    __a :  str						=  MobileBertForTokenClassification(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  Any						=  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 :					Union[str, Any]					,				snake_case_ :					List[str]					,				snake_case_ :					Any					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Optional[Any]					,				snake_case_ :					Any					,				snake_case_ :					Dict					,				snake_case_ :					Union[str, Any]   ):
    __a :  Union[str, Any]						=  self.num_choices
    __a :  List[str]						=  MobileBertForMultipleChoice(config=snake_case_   )
    model.to(snake_case_   )
    model.eval()
    __a :  List[Any]						=  input_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  int						=  token_type_ids.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  str						=  input_mask.unsqueeze(1   ).expand(-1					,				self.num_choices					,				-1   ).contiguous()
    __a :  Any						=  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 :					Optional[Any]   ):
    __a :  Optional[Any]						=  self.prepare_config_and_inputs()
    (
        (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       (
        __a
    )				,       
    ) :  int						=  config_and_inputs
    __a :  Union[str, Any]						=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
    return config, inputs_dict





@require_torch
class 		UpperCamelCase__  (       __lowercase    ,__lowercase    ,unittest.TestCase    ):
   _SCREAMING_SNAKE_CASE      :							str	       =      (
       (
           MobileBertModel,
           MobileBertForMaskedLM,
           MobileBertForMultipleChoice,
           MobileBertForNextSentencePrediction,
           MobileBertForPreTraining,
           MobileBertForQuestionAnswering,
           MobileBertForSequenceClassification,
           MobileBertForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _SCREAMING_SNAKE_CASE      :							Any	       =      (
       {
           "feature-extraction": MobileBertModel,
           "fill-mask": MobileBertForMaskedLM,
           "question-answering": MobileBertForQuestionAnswering,
           "text-classification": MobileBertForSequenceClassification,
           "token-classification": MobileBertForTokenClassification,
           "zero-shot": MobileBertForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE      :							Union[str, Any]	       =      True
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					Tuple					,				snake_case_ :					List[Any]					,				snake_case_ :					Union[str, Any]=False   ):
    __a :  List[str]						=  super()._prepare_for_class(snake_case_					,				snake_case_					,				return_labels=snake_case_   )

    if return_labels:
     if model_class in get_values(snake_case_   ):
      __a :  Optional[int]						=  torch.zeros(
          (self.model_tester.batch_size, self.model_tester.seq_length)					,				dtype=torch.long					,				device=snake_case_   )
      __a :  int						=  torch.zeros(
          self.model_tester.batch_size					,				dtype=torch.long					,				device=snake_case_   )
    return inputs_dict
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  List[Any]						=  MobileBertModelTester(self   )
    __a :  int						=  ConfigTester(self					,				config_class=snake_case_					,				hidden_size=3_7   )
   def       lowerCAmelCase			(self :					Union[str, Any]   ):
    self.config_tester.run_common_tests()
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    __a :  Dict						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_model(*snake_case_   )
   def       lowerCAmelCase			(self :					str   ):
    __a :  Any						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case_   )
   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  str						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case_   )
   def       lowerCAmelCase			(self :					Dict   ):
    __a :  Optional[Any]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Tuple						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case_   )
   def       lowerCAmelCase			(self :					List[Any]   ):
    __a :  int						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case_   )
   def       lowerCAmelCase			(self :					int   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case_   )







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[int]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case_   )





def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 return torch.tensor(
     lowerCAmelCase__  ,   dtype=torch.long  ,   device=lowerCAmelCase__  ,   )


lowercase__       =1e-3



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (       unittest.TestCase    ):
   @slow
   def       lowerCAmelCase			(self :					Any   ):
    __a :  Dict						=  MobileBertModel.from_pretrained('''google/mobilebert-uncased'''   ).to(snake_case_   )
    __a :  Tuple						=  _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]   )
    with torch.no_grad():
     __a :  str						=  model(snake_case_   )[0]
    __a :  List[Any]						=  torch.Size((1, 9, 5_1_2)   )
    self.assertEqual(output.shape					,				snake_case_   )
    __a :  Union[str, Any]						=  torch.tensor(
        [
            [
                [-2.473_6526E07, 8.269_1656E04, 1.652_1838E05],
                [-5.754_1704E-01, 3.905_6022E00, 4.401_1507E00],
                [2.604_7359E00, 1.567_7652E00, -1.732_4188E-01],
            ]
        ]					,				device=snake_case_					,				)

    # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
    # ~1 difference, it's therefore not a good idea to measure using addition.
    # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
    # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
    __a :  List[str]						=  torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE   )
    __a :  Any						=  torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE   )

    self.assertTrue(lower_bound and upper_bound   )
