
"""simple docstring"""


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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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 					_lowerCamelCase(__UpperCamelCase					,				__UpperCamelCase )       ->				Optional[Any]:
	# Initialize accelerator
	if args.with_tracking:
		_lowerCAmelCase			=Accelerator(
		    cpu=args.cpu					,				mixed_precision=args.mixed_precision					,				log_with="""all"""					,				project_dir=args.project_dir )
	else:
		_lowerCAmelCase			=Accelerator(cpu=args.cpu					,				mixed_precision=args.mixed_precision )

	if hasattr(args.checkpointing_steps					,				"""isdigit""" ):
		if args.checkpointing_steps == "epoch":
			_lowerCAmelCase			=args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			_lowerCAmelCase			=int(args.checkpointing_steps )
		else:
			raise ValueError(
			    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.''' )
	else:
		_lowerCAmelCase			=None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	_lowerCAmelCase			=config["""lr"""]
	_lowerCAmelCase			=int(config["""num_epochs"""] )
	_lowerCAmelCase			=int(config["""seed"""] )
	_lowerCAmelCase			=int(config["""batch_size"""] )

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		_lowerCAmelCase			=os.path.split(__UpperCamelCase )[-1].split(""".""" )[0]
		accelerator.init_trackers(__UpperCamelCase					,				__UpperCamelCase )

	_lowerCAmelCase			=AutoTokenizer.from_pretrained("""bert-base-cased""" )
	_lowerCAmelCase			=load_dataset("""glue"""					,				"""mrpc""" )
	_lowerCAmelCase			=evaluate.load("""glue"""					,				"""mrpc""" )

	def tokenize_function(__UpperCamelCase ):
		# max_length=None => use the model max length (it's actually the default)
		_lowerCAmelCase			=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():
		_lowerCAmelCase			=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
	_lowerCAmelCase			=tokenized_datasets.rename_column("""label"""					,				"""labels""" )

	# If the batch size is too big we use gradient accumulation
	_lowerCAmelCase			=1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		_lowerCAmelCase			=batch_size // MAX_GPU_BATCH_SIZE
		_lowerCAmelCase			=MAX_GPU_BATCH_SIZE

	def collate_fn(__UpperCamelCase ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		_lowerCAmelCase			=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":
			_lowerCAmelCase			=16
		elif accelerator.mixed_precision != "no":
			_lowerCAmelCase			=8
		else:
			_lowerCAmelCase			=None

		return tokenizer.pad(
		    __UpperCamelCase					,				padding="""longest"""					,				max_length=__UpperCamelCase					,				pad_to_multiple_of=__UpperCamelCase					,				return_tensors="""pt"""					,				)

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

	set_seed(__UpperCamelCase )

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	_lowerCAmelCase			=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).
	_lowerCAmelCase			=model.to(accelerator.device )

	# Instantiate optimizer
	_lowerCAmelCase			=AdamW(params=model.parameters()					,				lr=__UpperCamelCase )

	# Instantiate scheduler
	_lowerCAmelCase			=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.
	_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase		,							_lowerCAmelCase			=accelerator.prepare(
	    __UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase					,				__UpperCamelCase )

	# We need to keep track of how many total steps we have iterated over
	_lowerCAmelCase			=0
	# We also need to keep track of the stating epoch so files are named properly
	_lowerCAmelCase			=0

	# Potentially load in the weights and states from a previous save
	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 )
			_lowerCAmelCase			=os.path.basename(args.resume_from_checkpoint )
		else:
			# Get the most recent checkpoint
			_lowerCAmelCase			=[f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
			dirs.sort(key=os.path.getctime )
			_lowerCAmelCase			=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		_lowerCAmelCase			=os.path.splitext(__UpperCamelCase )[0]

		if "epoch" in training_difference:
			_lowerCAmelCase			=int(training_difference.replace("""epoch_"""					,				"""""" ) ) + 1
			_lowerCAmelCase			=None
		else:
			_lowerCAmelCase			=int(training_difference.replace("""step_"""					,				"""""" ) )
			_lowerCAmelCase			=resume_step // len(__UpperCamelCase )
			resume_step -= starting_epoch * len(__UpperCamelCase )

    # Now we train the model
	for epoch in range(__UpperCamelCase					,				__UpperCamelCase ):
		model.train()
		if args.with_tracking:
			_lowerCAmelCase			=0
		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
			_lowerCAmelCase			=accelerator.skip_first_batches(__UpperCamelCase					,				__UpperCamelCase )
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			_lowerCAmelCase			=train_dataloader
		for step, batch in enumerate(__UpperCamelCase ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device )
			_lowerCAmelCase			=model(**__UpperCamelCase )
			_lowerCAmelCase			=outputs.loss
			_lowerCAmelCase			=loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(__UpperCamelCase )
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(__UpperCamelCase					,				__UpperCamelCase ):
				_lowerCAmelCase			=F'''step_{overall_step}'''
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						_lowerCAmelCase			=os.path.join(args.output_dir					,				__UpperCamelCase )
					accelerator.save_state(__UpperCamelCase )

		model.eval()
		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():
				_lowerCAmelCase			=model(**__UpperCamelCase )
			_lowerCAmelCase			=outputs.logits.argmax(dim=-1 )
			_lowerCAmelCase		,							_lowerCAmelCase			=accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
			metric.add_batch(
			    predictions=__UpperCamelCase					,				references=__UpperCamelCase					,				)

		_lowerCAmelCase			=metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'''epoch {epoch}:'''					,				__UpperCamelCase )
		if args.with_tracking:
			accelerator.log(
			    {
			        """accuracy""": eval_metric["""accuracy"""],
			        """f1""": eval_metric["""f1"""],
			        """train_loss""": total_loss.item() / len(__UpperCamelCase ),
			        """epoch""": epoch,
			    }					,				step=__UpperCamelCase					,				)

		if checkpointing_steps == "epoch":
			_lowerCAmelCase			=F'''epoch_{epoch}'''
			if args.output_dir is not None:
				_lowerCAmelCase			=os.path.join(args.output_dir					,				__UpperCamelCase )
			accelerator.save_state(__UpperCamelCase )

	if args.with_tracking:
		accelerator.end_training()



def 					_lowerCamelCase()       ->				str:
	_lowerCAmelCase			=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.""" )
	parser.add_argument(
	    """--checkpointing_steps"""					,				type=__UpperCamelCase					,				default=__UpperCamelCase					,				help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""					,				)
	parser.add_argument(
	    """--resume_from_checkpoint"""					,				type=__UpperCamelCase					,				default=__UpperCamelCase					,				help="""If the training should continue from a checkpoint folder."""					,				)
	parser.add_argument(
	    """--with_tracking"""					,				action="""store_true"""					,				help="""Whether to load in all available experiment trackers from the environment and use them for logging."""					,				)
	parser.add_argument(
	    """--output_dir"""					,				type=__UpperCamelCase					,				default="""."""					,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""					,				)
	parser.add_argument(
	    """--project_dir"""					,				type=__UpperCamelCase					,				default="""logs"""					,				help="""Location on where to store experiment tracking logs` and relevent project information"""					,				)
	_lowerCAmelCase			=parser.parse_args()
	_lowerCAmelCase			={"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__UpperCamelCase					,				__UpperCamelCase )


if __name__ == "__main__":
			main()





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A 		= logging.get_logger(__name__)

__A 		= {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					_lowerCAmelCase			={0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



