import argparse
import os

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

from accelerate import Accelerator, DistributedType


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


_SCREAMING_SNAKE_CASE      						=	1_6
_SCREAMING_SNAKE_CASE      						=	3_2





def        lowercase(     UpperCamelCase_				, UpperCamelCase_ = 16    )						->     Optional[Any]:







 '''simple docstring'''
 UpperCamelCase          =	AutoTokenizer.from_pretrained("""bert-base-cased"""    )
 UpperCamelCase          =	load_dataset("""glue"""				, """mrpc"""    )

 def tokenize_function(UpperCamelCase_    ):
  # max_length=None => use the model max length (it's actually the default)
  UpperCamelCase          =	tokenizer(examples["""sentence1"""]				, examples["""sentence2"""]				, truncation=UpperCamelCase_				, max_length=UpperCamelCase_    )
  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(
      UpperCamelCase_				, batched=UpperCamelCase_				, 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(UpperCamelCase_    ):
  # 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(
      UpperCamelCase_				, padding="""longest"""				, max_length=UpperCamelCase_				, pad_to_multiple_of=UpperCamelCase_				, return_tensors="""pt"""				, )

 # Instantiate dataloaders.
 UpperCamelCase          =	DataLoader(
     tokenized_datasets["""train"""]				, shuffle=UpperCamelCase_				, collate_fn=UpperCamelCase_				, batch_size=UpperCamelCase_    )
 UpperCamelCase          =	DataLoader(
     tokenized_datasets["""validation"""]				, shuffle=UpperCamelCase_				, collate_fn=UpperCamelCase_				, batch_size=UpperCamelCase_    )

 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      						=	mocked_dataloaders  # noqa: F811





def        lowercase(     UpperCamelCase_				, UpperCamelCase_    )						->     Tuple:







 '''simple docstring'''
 # For testing only
 if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				, UpperCamelCase_    ) == "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"""]    )

 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

 set_seed(UpperCamelCase_    )
 UpperCamelCase          =	get_dataloaders(UpperCamelCase_				, UpperCamelCase_    )
 # 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=UpperCamelCase_    )

 # 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=UpperCamelCase_    )

 # Instantiate scheduler
 UpperCamelCase          =	get_linear_schedule_with_warmup(
     optimizer=UpperCamelCase_				, num_warmup_steps=100				, num_training_steps=(len(UpperCamelCase_    ) * 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          =	accelerator.prepare(
     UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_				, UpperCamelCase_    )

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

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

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





def        lowercase(     )						->     int:







 '''simple docstring'''
 UpperCamelCase          =	argparse.ArgumentParser(description="""Simple example of training script."""    )
 parser.add_argument(
     """--mixed_precision"""				, type=UpperCamelCase_				, default=UpperCamelCase_				, 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."""    )
 UpperCamelCase          =	parser.parse_args()
 UpperCamelCase          =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
 training_function(UpperCamelCase_				, UpperCamelCase_    )


if __name__ == "__main__":
     main()






from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


_UpperCAmelCase		:  Tuple	=   {
    "susnato/ernie-m-base_pytorch": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json",
    "susnato/ernie-m-large_pytorch": "https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json",
}




class       __lowerCAmelCase				(							lowerCAmelCase):
       _a           =  '''ernie_m'''
       _a           =  {"dropout": "classifier_dropout", "num_classes": "num_labels"}






       def __init__(      self:    List[Any]				,    _lowerCAmelCase:    int = 25_00_02				,    _lowerCAmelCase:    int = 7_68				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 12				,    _lowerCAmelCase:    int = 30_72				,    _lowerCAmelCase:    str = "gelu"				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    float = 0.1				,    _lowerCAmelCase:    int = 5_14				,    _lowerCAmelCase:    float = 0.02				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    float = 1e-0_5				,    _lowerCAmelCase:    Dict=None				,    _lowerCAmelCase:    Optional[int]=False				,    _lowerCAmelCase:    List[str]=0.0				,    **_lowerCAmelCase:    Tuple				,    ):
         super().__init__(pad_token_id=_lowerCAmelCase				,    **_lowerCAmelCase )
         lowercase			:Tuple							=  vocab_size
         lowercase			:List[str]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Optional[Any]							=  num_attention_heads
         lowercase			:Optional[Any]							=  intermediate_size
         lowercase			:Optional[Any]							=  hidden_act
         lowercase			:Any							=  hidden_dropout_prob
         lowercase			:int							=  attention_probs_dropout_prob
         lowercase			:Dict							=  max_position_embeddings
         lowercase			:Optional[Any]							=  initializer_range
         lowercase			:Any							=  layer_norm_eps
         lowercase			:Union[str, Any]							=  classifier_dropout
         lowercase			:int							=  is_decoder
         lowercase			:List[str]							=  act_dropout



