code: 
  
  
  
  
  
  
  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 the checkpointing capability,
  # 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
  #
  ########################################################################
  
  __A  							=  16
  __A  							=  32
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Accelerator     ,			A_:	int = 16		)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=AutoTokenizer.from_pretrained("""bert-base-cased"""		)
         __UpperCAmelCase    							=load_dataset("""glue"""     ,			"""mrpc"""		)
  
         def tokenize_function(A_:	Tuple		):
                # max_length=None => use the model max length (it's actually the default)
                __UpperCAmelCase    							=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():
                __UpperCAmelCase    							=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
         __UpperCAmelCase    							=tokenized_datasets.rename_column("""label"""     ,			"""labels"""		)
  
         def collate_fn(A_:	Dict		):
                # On TPU it's best to pad everything to the same length or training will be very slow.
                __UpperCAmelCase    							=128 if accelerator.distributed_type == DistributedType.TPU else None
                # When using mixed precision we want round multiples of 8/16
                if accelerator.mixed_precision == "fp8":
                       __UpperCAmelCase    							=16
                elif accelerator.mixed_precision != "no":
                       __UpperCAmelCase    							=8
                else:
                       __UpperCAmelCase    							=None
  
                return tokenizer.pad(
                    A_     ,			padding="""longest"""     ,			max_length=A_     ,			pad_to_multiple_of=A_     ,			return_tensors="""pt"""     ,			)
  
         # Instantiate dataloaders.
         __UpperCAmelCase    							=DataLoader(
             tokenized_datasets["""train"""]     ,			shuffle=A_     ,			collate_fn=A_     ,			batch_size=A_		)
         __UpperCAmelCase    							=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
  
     __A  							=  mocked_dataloaders  # noqa: F811
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Optional[int]     ,			A_:	List[Any]		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,			A_		) == "1":
                __UpperCAmelCase    							=2
         # Initialize accelerator
         __UpperCAmelCase    							=Accelerator(cpu=args.cpu     ,			mixed_precision=args.mixed_precision		)
         # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
         __UpperCAmelCase    							=config["""lr"""]
         __UpperCAmelCase    							=int(config["""num_epochs"""]		)
         __UpperCAmelCase    							=int(config["""seed"""]		)
         __UpperCAmelCase    							=int(config["""batch_size"""]		)
  
         # New Code #
         # Parse out whether we are saving every epoch or after a certain number of batches
         if hasattr(args.checkpointing_steps     ,			"""isdigit"""		):
                if args.checkpointing_steps == "epoch":
                       __UpperCAmelCase    							=args.checkpointing_steps
                elif args.checkpointing_steps.isdigit():
                       __UpperCAmelCase    							=int(args.checkpointing_steps		)
                else:
                       raise ValueError(
                           F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''		)
         else:
                __UpperCAmelCase    							=None
  
         set_seed(A_		)
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=get_dataloaders(A_     ,			A_		)
         __UpperCAmelCase    							=evaluate.load("""glue"""     ,			"""mrpc"""		)
  
         # If the batch size is too big we use gradient accumulation
         __UpperCAmelCase    							=1
         if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
                __UpperCAmelCase    							=batch_size // MAX_GPU_BATCH_SIZE
                __UpperCAmelCase    							=MAX_GPU_BATCH_SIZE
  
         # Instantiate the model (we build the model here so that the seed also control new weights initialization)
         __UpperCAmelCase    							=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).
         __UpperCAmelCase    							=model.to(accelerator.device		)
  
         # Instantiate optimizer
         __UpperCAmelCase    							=AdamW(params=model.parameters()     ,			lr=A_		)
  
         # Instantiate scheduler
         __UpperCAmelCase    							=get_linear_schedule_with_warmup(
             optimizer=A_     ,			num_warmup_steps=100     ,			num_training_steps=(len(A_		) * 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.
         __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase    							=accelerator.prepare(
             A_     ,			A_     ,			A_     ,			A_     ,			A_		)
  
         # New Code #
         # We need to keep track of how many total steps we have iterated over
         __UpperCAmelCase    							=0
         # We also need to keep track of the stating epoch so files are named properly
         __UpperCAmelCase    							=0
  
         # We need to load the checkpoint back in before training here with `load_state`
         # The total number of epochs is adjusted based on where the state is being loaded from,
         # as we assume continuation of the same training script
         if args.resume_from_checkpoint:
                if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
                       accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''		)
                       accelerator.load_state(args.resume_from_checkpoint		)
                       __UpperCAmelCase    							=os.path.basename(args.resume_from_checkpoint		)
                else:
                       # Get the most recent checkpoint
                       __UpperCAmelCase    							=[f.name for f in os.scandir(os.getcwd()		) if f.is_dir()]
                       dirs.sort(key=os.path.getctime		)
                       __UpperCAmelCase    							=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
                # Extract `epoch_{i}` or `step_{i}`
                __UpperCAmelCase    							=os.path.splitext(A_		)[0]
  
                if "epoch" in training_difference:
                       __UpperCAmelCase    							=int(training_difference.replace("""epoch_"""     ,			""""""		)		) + 1
                       __UpperCAmelCase    							=None
                else:
                       __UpperCAmelCase    							=int(training_difference.replace("""step_"""     ,			""""""		)		)
                       __UpperCAmelCase    							=resume_step // len(A_		)
                       resume_step -= starting_epoch * len(A_		)
  
      # Now we train the model
         for epoch in range(A_     ,			A_		):
                model.train()
                # New Code #
                if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
                       # We need to skip steps until we reach the resumed step
                       __UpperCAmelCase    							=accelerator.skip_first_batches(A_     ,			A_		)
                       overall_step += resume_step
                else:
                       # After the first iteration though, we need to go back to the original dataloader
                       __UpperCAmelCase    							=train_dataloader
                for step, batch in enumerate(A_		):
                       # We could avoid this line since we set the accelerator with `device_placement=True`.
                       batch.to(accelerator.device		)
                       __UpperCAmelCase    							=model(**A_		)
                       __UpperCAmelCase    							=outputs.loss
                       __UpperCAmelCase    							=loss / gradient_accumulation_steps
                       accelerator.backward(A_		)
                       if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()
                       # New Code #
                       overall_step += 1
  
                       # New Code #
                       # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                       # These are saved to folders named `step_{overall_step}`
                       # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                       # If mixed precision was used, will also save a "scalar.bin" file
                       if isinstance(A_     ,			A_		):
                              __UpperCAmelCase    							=F'''step_{overall_step}'''
                              if overall_step % checkpointing_steps == 0:
                                     if args.output_dir is not None:
                                            __UpperCAmelCase    							=os.path.join(args.output_dir     ,			A_		)
                                     accelerator.save_state(A_		)
  
                model.eval()
                for step, batch in enumerate(A_		):
                       # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
                       batch.to(accelerator.device		)
                       with torch.no_grad():
                              __UpperCAmelCase    							=model(**A_		)
                       __UpperCAmelCase    							=outputs.logits.argmax(dim=-1		)
                       __UpperCAmelCase							,   __UpperCAmelCase    							=accelerator.gather_for_metrics((predictions, batch["""labels"""])		)
                       metric.add_batch(
                           predictions=A_     ,			references=A_     ,			)
  
                __UpperCAmelCase    							=metric.compute()
                # Use accelerator.print to print only on the main process.
                accelerator.print(F'''epoch {epoch}:'''     ,			A_		)
  
                # New Code #
                # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
                # These are saved to folders named `epoch_{epoch}`
                # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
                # If mixed precision was used, will also save a "scalar.bin" file
                if checkpointing_steps == "epoch":
                       __UpperCAmelCase    							=F'''epoch_{epoch}'''
                       if args.output_dir is not None:
                              __UpperCAmelCase    							=os.path.join(args.output_dir     ,			A_		)
                       accelerator.save_state(A_		)
  
  
  
  
  
  
  
  def 			lowercase__  (				)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=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."""     ,			)
         parser.add_argument("""--cpu"""     ,			action="""store_true"""     ,			help="""If passed, will train on the CPU."""		)
         parser.add_argument(
             """--checkpointing_steps"""     ,			type=A_     ,			default=A_     ,			help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""     ,			)
         parser.add_argument(
             """--output_dir"""     ,			type=A_     ,			default="""."""     ,			help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""     ,			)
         parser.add_argument(
             """--resume_from_checkpoint"""     ,			type=A_     ,			default=A_     ,			help="""If the training should continue from a checkpoint folder."""     ,			)
         __UpperCAmelCase    							=parser.parse_args()
         __UpperCAmelCase    							={"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
         training_function(A_     ,			A_		)
  
  
  if __name__ == "__main__":
     main()
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import unittest
  
  from transformers import ErnieMConfig, is_torch_available
  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 (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    List[str]=13   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=7   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    str=99   ,    __SCREAMING_SNAKE_CASE			:    int=32   ,    __SCREAMING_SNAKE_CASE			:    str=5   ,    __SCREAMING_SNAKE_CASE			:    int=4   ,    __SCREAMING_SNAKE_CASE			:    List[str]=37   ,    __SCREAMING_SNAKE_CASE			:    int="gelu"   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=512   ,    __SCREAMING_SNAKE_CASE			:    int=16   ,    __SCREAMING_SNAKE_CASE			:    int=2   ,    __SCREAMING_SNAKE_CASE			:    Any=0.02   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=4   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    )			->       Optional[Any]:
             __UpperCAmelCase    							=parent
             __UpperCAmelCase    							=batch_size
             __UpperCAmelCase    							=seq_length
             __UpperCAmelCase    							=is_training
             __UpperCAmelCase    							=use_input_mask
             __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 _a       (							self			:    str			)			->       Optional[Any]:
             __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
             __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, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
      def _a       (							self			:    Optional[Any]			)			->       Any:
             __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    							=self.get_config()
  
             return config, input_ids, input_mask
  
  
  
      def _a       (							self			:    List[str]			)			->       List[str]:
             return ErnieMConfig(
                 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   ,    )
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=ErnieMModel(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    return_dict=__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       int:
             __UpperCAmelCase    							=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_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 _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Any:
             __UpperCAmelCase    							=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=torch.ones_like(__SCREAMING_SNAKE_CASE   ,    dtype=torch.floataa			)
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_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 _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       str:
             __UpperCAmelCase    							=self.num_labels
             __UpperCAmelCase    							=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Dict:
             __UpperCAmelCase    							=self.num_labels
             __UpperCAmelCase    							=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             input_ids.to(__SCREAMING_SNAKE_CASE			)
             input_mask.to(__SCREAMING_SNAKE_CASE			)
             token_labels.to(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
  
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)			)
  
  
  
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       List[Any]:
             __UpperCAmelCase    							=self.num_choices
             __UpperCAmelCase    							=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=input_ids.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
             __UpperCAmelCase    							=input_mask.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE   ,    )
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_choices)			)
  
  
  
      def _a       (							self			:    Any			)			->       List[str]:
             __UpperCAmelCase    							=self.prepare_config_and_inputs()
             (
                 (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   
             )    							=config_and_inputs
             __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask}
             return config, inputs_dict
  
  
  
  
  @require_torch
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   (
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase  :       Optional[Any]   =   ()
      lowerCamelCase  :       str   =   (
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase  :       Optional[int]   =   False
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Optional[int]:
             if pipeline_test_casse_name == "QAPipelineTests":
                    return True
  
             return False
  
  
  
      def _a       (							self			:    Optional[int]			)			->       List[Any]:
             __UpperCAmelCase    							=ErnieMModelTester(self			)
             __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    hidden_size=37			)
  
  
  
      def _a       (							self			:    int			)			->       List[str]:
             self.config_tester.run_common_tests()
  
  
  
      def _a       (							self			:    str			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       List[str]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    __UpperCAmelCase    							=type
                    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[str]			)			->       Union[str, Any]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Any			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    str			)			->       List[Any]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       List[str]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      @slow
      def _a       (							self			:    List[str]			)			->       Optional[Any]:
             for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __UpperCAmelCase    							=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
                    self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
  
  
  
  
  @require_torch
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      @slow
      def _a       (							self			:    Tuple			)			->       Optional[Any]:
             __UpperCAmelCase    							=ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""			)
             model.eval()
             __UpperCAmelCase    							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)[0]
  
             # TODO Replace vocab size
             __UpperCAmelCase    							=768
  
             __UpperCAmelCase    							=torch.Size((1, 6, hidden_size)			)
             self.assertEqual(output.shape   ,    __SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=torch.tensor(
                 [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]			)
  
             self.assertTrue(torch.allclose(output[:, :3, :3]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)
  
  
  
  
  