"""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
# and perform 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__      						=  16
SCREAMING_SNAKE_CASE__      						=  32





def    lowerCAmelCase__    (			_UpperCamelCase       :				Accelerator ,			_UpperCamelCase       :				int = 1_6					)					->				Union[str, Any]:





						"""simple docstring"""






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

						def tokenize_function(_UpperCamelCase       :				List[Any]					):
												# max_length=None => use the model max length (it's actually the default)
												snake_case								=			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():
												snake_case								=			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
						snake_case								=			tokenized_datasets.rename_column('label' ,			'labels'					)

						def collate_fn(_UpperCamelCase       :				Tuple					):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												snake_case								=			1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		snake_case								=			1_6
												elif accelerator.mixed_precision != "no":
																		snake_case								=			8
												else:
																		snake_case								=			None

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

						# Instantiate dataloaders.
						snake_case								=			DataLoader(
						    tokenized_datasets['train'] ,			shuffle=_UpperCamelCase ,			collate_fn=_UpperCamelCase ,			batch_size=_UpperCamelCase					)
						snake_case								=			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    lowerCAmelCase__    (			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				Optional[Any]					)					->				Optional[int]:





						"""simple docstring"""






						if os.environ.get('TESTING_MOCKED_DATALOADERS' ,			_UpperCamelCase					) == "1":
												snake_case								=			2
						# New Code #
						snake_case								=			int(args.gradient_accumulation_steps					)
						# Initialize accelerator
						snake_case								=			Accelerator(
						    cpu=args.cpu ,			mixed_precision=args.mixed_precision ,			gradient_accumulation_steps=_UpperCamelCase					)
						if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
												raise NotImplementedError(
												    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'					)
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						snake_case								=			config['lr']
						snake_case								=			int(config['num_epochs']					)
						snake_case								=			int(config['seed']					)
						snake_case								=			int(config['batch_size']					)

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

						set_seed(_UpperCamelCase					)
						snake_case					,snake_case								=			get_dataloaders(_UpperCamelCase ,			_UpperCamelCase					)
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case								=			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).
						snake_case								=			model.to(accelerator.device					)

						# Instantiate optimizer
						snake_case								=			AdamW(params=model.parameters() ,			lr=_UpperCamelCase					)

						# Instantiate scheduler
						snake_case								=			get_linear_schedule_with_warmup(
						    optimizer=_UpperCamelCase ,			num_warmup_steps=1_0_0 ,			num_training_steps=(len(_UpperCamelCase					) * 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.
						snake_case					,snake_case					,snake_case					,snake_case					,snake_case								=			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					)
																		# 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(_UpperCamelCase					):
																								snake_case								=			model(**_UpperCamelCase					)
																								snake_case								=			output.loss
																								accelerator.backward(_UpperCamelCase					)
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

												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():
																								snake_case								=			model(**_UpperCamelCase					)
																		snake_case								=			outputs.logits.argmax(dim=-1					)
																		snake_case					,snake_case								=			accelerator.gather_for_metrics((predictions, batch['labels'])					)
																		metric.add_batch(
																		    predictions=_UpperCamelCase ,			references=_UpperCamelCase ,			)

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





def    lowerCAmelCase__    (			)					->				Dict:





						"""simple docstring"""






						snake_case								=			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.' ,			)
						# New Code #
						parser.add_argument(
						    '--gradient_accumulation_steps' ,			type=_UpperCamelCase ,			default=1 ,			help='The number of minibatches to be ran before gradients are accumulated.' ,			)
						parser.add_argument('--cpu' ,			action='store_true' ,			help='If passed, will train on the CPU.'					)
						snake_case								=			parser.parse_args()
						snake_case								=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
						training_function(_UpperCamelCase ,			_UpperCamelCase					)


if __name__ == "__main__":
					main()



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      						=  {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""roc_bert"""

				def __init__(				self		,    lowerCAmelCase=3_05_22		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-12		,    lowerCAmelCase=True		,    lowerCAmelCase=0		,    lowerCAmelCase="absolute"		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=7_68		,    lowerCAmelCase=9_10		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2_48_58		,    lowerCAmelCase=True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			vocab_size
										snake_case								=			max_position_embeddings
										snake_case								=			hidden_size
										snake_case								=			num_hidden_layers
										snake_case								=			num_attention_heads
										snake_case								=			intermediate_size
										snake_case								=			hidden_act
										snake_case								=			hidden_dropout_prob
										snake_case								=			attention_probs_dropout_prob
										snake_case								=			initializer_range
										snake_case								=			type_vocab_size
										snake_case								=			layer_norm_eps
										snake_case								=			use_cache
										snake_case								=			enable_pronunciation
										snake_case								=			enable_shape
										snake_case								=			pronunciation_embed_dim
										snake_case								=			pronunciation_vocab_size
										snake_case								=			shape_embed_dim
										snake_case								=			shape_vocab_size
										snake_case								=			concat_input
										snake_case								=			position_embedding_type
										snake_case								=			classifier_dropout
										super().__init__(pad_token_id=lowerCAmelCase		,    **lowerCAmelCase    )


