

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_SCREAMING_SNAKE_CASE  :						List[str]         =    16
_SCREAMING_SNAKE_CASE  :						Any         =    32
def        UpperCAmelCase_     (		_A   ,	_A = 16       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''bert-base-cased'''       )
     SCREAMING_SNAKE_CASE__					=					load_dataset('''glue'''   ,	'''mrpc'''       )

     def tokenize_function(_A       ):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE__					=					tokenizer(examples['''sentence1''']   ,	examples['''sentence2''']   ,	truncation=_A   ,	max_length=_A       )
          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():
          SCREAMING_SNAKE_CASE__					=					datasets.map(
              _A   ,	batched=_A   ,	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
     SCREAMING_SNAKE_CASE__					=					tokenized_datasets.rename_column('''label'''   ,	'''labels'''       )

     def collate_fn(_A       ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE__					=					1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               SCREAMING_SNAKE_CASE__					=					16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE__					=					8
          else:
               SCREAMING_SNAKE_CASE__					=					None

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

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''train''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )
     SCREAMING_SNAKE_CASE__					=					DataLoader(
         tokenized_datasets['''validation''']   ,	shuffle=_A   ,	collate_fn=_A   ,	batch_size=_A       )

     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

     _SCREAMING_SNAKE_CASE  :						Tuple         =    mocked_dataloaders  # noqa: F811
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,	_A       ) == "1":
          SCREAMING_SNAKE_CASE__					=					2
     # New Code #
     SCREAMING_SNAKE_CASE__					=					int(args.gradient_accumulation_steps       )
     SCREAMING_SNAKE_CASE__					=					int(args.local_sgd_steps       )
     # Initialize accelerator
     SCREAMING_SNAKE_CASE__					=					Accelerator(
         cpu=args.cpu   ,	mixed_precision=args.mixed_precision   ,	gradient_accumulation_steps=_A       )
     if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
          raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE__					=					config['''lr''']
     SCREAMING_SNAKE_CASE__					=					int(config['''num_epochs''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''seed''']       )
     SCREAMING_SNAKE_CASE__					=					int(config['''batch_size''']       )

     SCREAMING_SNAKE_CASE__					=					evaluate.load('''glue'''   ,	'''mrpc'''       )

     set_seed(_A       )
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					get_dataloaders(_A   ,	_A       )
     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE__					=					AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''   ,	return_dict=_A       )

     # 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).
     SCREAMING_SNAKE_CASE__					=					model.to(accelerator.device       )

     # Instantiate optimizer
     SCREAMING_SNAKE_CASE__					=					AdamW(params=model.parameters()   ,	lr=_A       )

     # Instantiate scheduler
     SCREAMING_SNAKE_CASE__					=					get_linear_schedule_with_warmup(
         optimizer=_A   ,	num_warmup_steps=1_00   ,	num_training_steps=(len(_A       ) * num_epochs)   ,	)

     # 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.
     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					accelerator.prepare(
         _A   ,	_A   ,	_A   ,	_A   ,	_A       )

     # Now we train the model
     for epoch in range(_A       ):
          model.train()
          with LocalSGD(
              accelerator=_A   ,	model=_A   ,	local_sgd_steps=_A   ,	enabled=local_sgd_steps is not None       ) as local_sgd:
               for step, batch in enumerate(_A       ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device       )
                    # New code #
                    # We use the new `accumulate` context manager to perform gradient accumulation
                    # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                    with accelerator.accumulate(_A       ):
                         SCREAMING_SNAKE_CASE__					=					model(**_A       )
                         SCREAMING_SNAKE_CASE__					=					output.loss
                         accelerator.backward(_A       )
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # LocalSGD-specific line
                         local_sgd.step()

          model.eval()
          for step, batch in enumerate(_A       ):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device       )
               with torch.no_grad():
                    SCREAMING_SNAKE_CASE__					=					model(**_A       )
               SCREAMING_SNAKE_CASE__					=					outputs.logits.argmax(dim=-1       )
               SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
               metric.add_batch(
                   predictions=_A   ,	references=_A   ,	)

          SCREAMING_SNAKE_CASE__					=					metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(F'''epoch {epoch}:'''   ,	_A       )
def        UpperCAmelCase_     (		):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					argparse.ArgumentParser(description='''Simple example of training script.'''       )
     parser.add_argument(
         '''--mixed_precision'''   ,	type=_A   ,	default=_A   ,	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.'''   ,	)
     # New Code #
     parser.add_argument(
         '''--gradient_accumulation_steps'''   ,	type=_A   ,	default=1   ,	help='''The number of minibatches to be ran before gradients are accumulated.'''   ,	)
     parser.add_argument(
         '''--local_sgd_steps'''   ,	type=_A   ,	default=8   ,	help='''Number of local SGD steps or None to disable local SGD'''       )
     parser.add_argument('''--cpu'''   ,	action='''store_true'''   ,	help='''If passed, will train on the CPU.'''       )
     SCREAMING_SNAKE_CASE__					=					parser.parse_args()
     SCREAMING_SNAKE_CASE__					=					{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(_A   ,	_A       )


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__	:






  """simple docstring"""


  def __init__(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]=13			,     __lowerCamelCase		:	Any=7			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	Tuple=True			,     __lowerCamelCase		:	Tuple=True			,     __lowerCamelCase		:	str=True			,     __lowerCamelCase		:	int=99			,     __lowerCamelCase		:	Optional[int]=64			,     __lowerCamelCase		:	Dict=32			,     __lowerCamelCase		:	Dict=5			,     __lowerCamelCase		:	Optional[Any]=4			,     __lowerCamelCase		:	Optional[Any]=37			,     __lowerCamelCase		:	Dict="gelu"			,     __lowerCamelCase		:	Any=0.1			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	Optional[Any]=512			,     __lowerCamelCase		:	List[Any]=16			,     __lowerCamelCase		:	Optional[int]=2			,     __lowerCamelCase		:	Union[str, Any]=0.02			,     __lowerCamelCase		:	List[str]=3			,     __lowerCamelCase		:	Any=4			,     __lowerCamelCase		:	Dict=None			,     )	->							int:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					seq_length
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_input_mask
       SCREAMING_SNAKE_CASE__					=					use_token_type_ids
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					embedding_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					num_choices
       SCREAMING_SNAKE_CASE__					=					scope






  def    lowercase_	(							self		:	List[str]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size      )

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

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

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

       SCREAMING_SNAKE_CASE__					=					self.get_config()

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






  def    lowercase_	(							self		:	Dict      )	->							Any:
       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=__lowerCamelCase			,     initializer_range=self.initializer_range			,     )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[Any]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					MobileBertModel(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     token_type_ids=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Any      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					MobileBertForMaskedLM(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	List[Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					MobileBertForNextSentencePrediction(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(
           __lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     labels=__lowerCamelCase			,     )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, 2)      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any      )	->							int:
       SCREAMING_SNAKE_CASE__					=					MobileBertForPreTraining(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(
           __lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     labels=__lowerCamelCase			,     next_sentence_label=__lowerCamelCase			,     )
       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    lowercase_	(							self		:	List[Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Any      )	->							Dict:
       SCREAMING_SNAKE_CASE__					=					MobileBertForQuestionAnswering(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Optional[Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Union[str, Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					MobileBertForSequenceClassification(__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     labels=__lowerCamelCase      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Union[str, Any]      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					MobileBertForTokenClassification(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					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    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Tuple      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.num_choices
       SCREAMING_SNAKE_CASE__					=					MobileBertForMultipleChoice(config=__lowerCamelCase      )
       model.to(__lowerCamelCase      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					input_ids.unsqueeze(1      ).expand(-1			,     self.num_choices			,     -1      ).contiguous()
       SCREAMING_SNAKE_CASE__					=					token_type_ids.unsqueeze(1      ).expand(-1			,     self.num_choices			,     -1      ).contiguous()
       SCREAMING_SNAKE_CASE__					=					input_mask.unsqueeze(1      ).expand(-1			,     self.num_choices			,     -1      ).contiguous()
       SCREAMING_SNAKE_CASE__					=					model(
           __lowerCamelCase			,     attention_mask=__lowerCamelCase			,     token_type_ids=__lowerCamelCase			,     labels=__lowerCamelCase			,     )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_choices)      )







  def    lowercase_	(							self		:	List[str]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),(
           SCREAMING_SNAKE_CASE__
       ),
       )					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (
      (
          MobileBertModel,
          MobileBertForMaskedLM,
          MobileBertForMultipleChoice,
          MobileBertForNextSentencePrediction,
          MobileBertForPreTraining,
          MobileBertForQuestionAnswering,
          MobileBertForSequenceClassification,
          MobileBertForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  a     =      (
      {
          "feature-extraction": MobileBertModel,
          "fill-mask": MobileBertForMaskedLM,
          "question-answering": MobileBertForQuestionAnswering,
          "text-classification": MobileBertForSequenceClassification,
          "token-classification": MobileBertForTokenClassification,
          "zero-shot": MobileBertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a     =      True






  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]			,     __lowerCamelCase		:	Optional[int]=False      )	->							str:
       SCREAMING_SNAKE_CASE__					=					super()._prepare_for_class(__lowerCamelCase			,     __lowerCamelCase			,     return_labels=__lowerCamelCase      )

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






  def    lowercase_	(							self		:	Optional[Any]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					MobileBertModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=__lowerCamelCase			,     hidden_size=37      )






  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	Optional[int]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_model(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Tuple      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_masked_lm(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_multiple_choice(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Any      )	->							str:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_pretraining(*__lowerCamelCase      )






  def    lowercase_	(							self		:	Dict      )	->							str:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_question_answering(*__lowerCamelCase      )






  def    lowercase_	(							self		:	List[str]      )	->							str:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_sequence_classification(*__lowerCamelCase      )







  def    lowercase_	(							self		:	Tuple      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_token_classification(*__lowerCamelCase      )



def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return torch.tensor(
         _A   ,	dtype=torch.long   ,	device=_A   ,	)


_SCREAMING_SNAKE_CASE  :						Optional[Any]         =    1e-3







@require_torch
@require_sentencepiece
@require_tokenizers
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @slow
  def    lowercase_	(							self		:	List[str]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					MobileBertModel.from_pretrained('''google/mobilebert-uncased'''      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					_long_tensor([[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]      )
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					=					model(__lowerCamelCase      )[0]
       SCREAMING_SNAKE_CASE__					=					torch.Size((1, 9, 512)      )
       self.assertEqual(output.shape			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					torch.tensor(
           [
               [
                   [-2.4_73_65_26e07, 8.2_69_16_56e04, 1.6_52_18_38e05],
                   [-5.7_54_17_04e-01, 3.9_05_60_22e00, 4.4_01_15_07e00],
                   [2.6_04_73_59e00, 1.5_67_76_52e00, -1.7_32_41_88e-01],
               ]
           ]			,     device=__lowerCamelCase			,     )

       # 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
       SCREAMING_SNAKE_CASE__					=					torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE      )
       SCREAMING_SNAKE_CASE__					=					torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE      )

       self.assertTrue(lower_bound and upper_bound      )


