


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
#
########################################################################


__lowerCAmelCase	: Dict  				=1_6
__lowerCAmelCase	: List[str]  				=3_2


def        _UpperCamelCase							(      lowercase__    ,       lowercase__ = 16      ):
						__SCREAMING_SNAKE_CASE						:     Dict					=		AutoTokenizer.from_pretrained('''bert-base-cased'''      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		load_dataset('''glue'''    ,       '''mrpc'''      )

						def tokenize_function(lowercase__      ):
												# max_length=None => use the model max length (it's actually the default)
												__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tokenizer(examples['''sentence1''']    ,       examples['''sentence2''']    ,       truncation=lowercase__    ,       max_length=lowercase__      )
												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():
												__SCREAMING_SNAKE_CASE						:     Dict					=		datasets.map(
												    lowercase__    ,       batched=lowercase__    ,       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
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenized_datasets.rename_column('''label'''    ,       '''labels'''      )

						def collate_fn(lowercase__      ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__SCREAMING_SNAKE_CASE						:     List[str]					=		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":
																		__SCREAMING_SNAKE_CASE						:     str					=		16
												elif accelerator.mixed_precision != "no":
																		__SCREAMING_SNAKE_CASE						:     Tuple					=		8
												else:
																		__SCREAMING_SNAKE_CASE						:     Dict					=		None

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

						# Instantiate dataloaders.
						__SCREAMING_SNAKE_CASE						:     Tuple					=		DataLoader(
						    tokenized_datasets['''train''']    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=lowercase__      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		DataLoader(
						    tokenized_datasets['''validation''']    ,       shuffle=lowercase__    ,       collate_fn=lowercase__    ,       batch_size=lowercase__      )

						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

						__lowerCAmelCase	: Dict  				=mocked_dataloaders  # noqa: F811


def        _UpperCamelCase							(      lowercase__    ,       lowercase__      ):
						# For testing only
						if os.environ.get('''TESTING_MOCKED_DATALOADERS'''    ,       lowercase__      ) == "1":
												__SCREAMING_SNAKE_CASE						:     Dict					=		2
						# Initialize accelerator
						__SCREAMING_SNAKE_CASE						:     List[str]					=		Accelerator(cpu=args.cpu    ,       mixed_precision=args.mixed_precision      )
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__SCREAMING_SNAKE_CASE						:     str					=		config['''lr''']
						__SCREAMING_SNAKE_CASE						:     Tuple					=		int(config['''num_epochs''']      )
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		int(config['''seed''']      )
						__SCREAMING_SNAKE_CASE						:     Optional[int]					=		int(config['''batch_size''']      )

						__SCREAMING_SNAKE_CASE						:     Dict					=		evaluate.load('''glue'''    ,       '''mrpc'''      )

						# If the batch size is too big we use gradient accumulation
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__SCREAMING_SNAKE_CASE						:     List[Any]					=		batch_size // MAX_GPU_BATCH_SIZE
												__SCREAMING_SNAKE_CASE						:     List[str]					=		MAX_GPU_BATCH_SIZE

						set_seed(lowercase__      )
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		get_dataloaders(lowercase__    ,       lowercase__      )
						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''    ,       return_dict=lowercase__      )

						# 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).
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model.to(accelerator.device      )

						# Instantiate optimizer
						__SCREAMING_SNAKE_CASE						:     List[Any]					=		AdamW(params=model.parameters()    ,       lr=lowercase__      )

						# Instantiate scheduler
						__SCREAMING_SNAKE_CASE						:     str					=		get_linear_schedule_with_warmup(
						    optimizer=lowercase__    ,       num_warmup_steps=100    ,       num_training_steps=(len(lowercase__      ) * 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.
						__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Any					=		accelerator.prepare(
						    lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__      )

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

												model.eval()
												__SCREAMING_SNAKE_CASE						:     Dict					=		0
												for step, batch in enumerate(lowercase__      ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device      )
																		with torch.no_grad():
																								__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		model(**lowercase__      )
																		__SCREAMING_SNAKE_CASE						:     Optional[int]					=		outputs.logits.argmax(dim=-1      )
																		__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		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(lowercase__      ) - 1:
																														# Last batch needs to be truncated on distributed systems as it contains additional samples
																														__SCREAMING_SNAKE_CASE						:     int					=		predictions[: len(eval_dataloader.dataset      ) - samples_seen]
																														__SCREAMING_SNAKE_CASE						:     Any					=		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=lowercase__    ,       references=lowercase__    ,       )

												__SCREAMING_SNAKE_CASE						:     Dict					=		metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''    ,       lowercase__      )


def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     int					=		argparse.ArgumentParser(description='''Simple example of training script.'''      )
						parser.add_argument(
						    '''--mixed_precision'''    ,       type=lowercase__    ,       default=lowercase__    ,       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.'''      )
						__SCREAMING_SNAKE_CASE						:     str					=		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:     Dict					=		{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
						training_function(lowercase__    ,       lowercase__      )


if __name__ == "__main__":
						main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
