






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()












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			)			)




