


'''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
#
########################################################################


__snake_case							      =     16
__snake_case							      =     32


def 			a      (			__a ,					__a = 16   )  ->	Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= AutoTokenizer.from_pretrained('''bert-base-cased'''   )
					UpperCamelCase__   :List[str]												= load_dataset('''glue''' ,					'''mrpc'''   )

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

					def collate_fn(__a   ):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCamelCase__   :Tuple												= 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__   :int												= 16
										elif accelerator.mixed_precision != "no":
															UpperCamelCase__   :Union[str, Any]												= 8
										else:
															UpperCamelCase__   :Any												= None

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

					# Instantiate dataloaders.
					UpperCamelCase__   :Any												= DataLoader(
					    tokenized_datasets['''train'''] ,					shuffle=__a ,					collate_fn=__a ,					batch_size=__a   )
					UpperCamelCase__   :Union[str, Any]												= 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

		__snake_case							      =     mocked_dataloaders  # noqa: F811


def 			a      (			__a ,					__a   )  ->	Optional[int]:



					'''simple docstring'''






					if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,					__a   ) == "1":
										UpperCamelCase__   :Union[str, Any]												= 2
					# New Code #
					UpperCamelCase__   :Union[str, Any]												= int(args.gradient_accumulation_steps   )
					# Initialize accelerator
					UpperCamelCase__   :int												= Accelerator(
					    cpu=args.cpu ,					mixed_precision=args.mixed_precision ,					gradient_accumulation_steps=__a   )
					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
					UpperCamelCase__   :Union[str, Any]												= config['''lr''']
					UpperCamelCase__   :Dict												= int(config['''num_epochs''']   )
					UpperCamelCase__   :str												= int(config['''seed''']   )
					UpperCamelCase__   :Optional[Any]												= int(config['''batch_size''']   )

					UpperCamelCase__   :Tuple												= evaluate.load('''glue''' ,					'''mrpc'''   )

					set_seed(__a   )
					UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= get_dataloaders(__a ,					__a   )
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__   :Union[str, Any]												= 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__   :Any												= model.to(accelerator.device   )

					# Instantiate optimizer
					UpperCamelCase__   :Union[str, Any]												= AdamW(params=model.parameters() ,					lr=__a   )

					# Instantiate scheduler
					UpperCamelCase__   :List[Any]												= get_linear_schedule_with_warmup(
					    optimizer=__a ,					num_warmup_steps=100 ,					num_training_steps=(len(__a   ) * 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.
					UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :str												= accelerator.prepare(
					    __a ,					__a ,					__a ,					__a ,					__a   )

					# Now we train the model
					for epoch in range(__a   ):
										model.train()
										for step, batch in enumerate(__a   ):
															# 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(__a   ):
																				UpperCamelCase__   :Optional[Any]												= model(**__a   )
																				UpperCamelCase__   :List[Any]												= output.loss
																				accelerator.backward(__a   )
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										for step, batch in enumerate(__a   ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device   )
															with torch.no_grad():
																				UpperCamelCase__   :int												= model(**__a   )
															UpperCamelCase__   :str												= outputs.logits.argmax(dim=-1   )
															UpperCamelCase__					,				UpperCamelCase__   :List[Any]												= accelerator.gather_for_metrics((predictions, batch['''labels'''])   )
															metric.add_batch(
															    predictions=__a ,					references=__a ,					)

										UpperCamelCase__   :int												= metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}:''' ,					__a   )


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :Optional[Any]												= 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.''' ,					)
					# New Code #
					parser.add_argument(
					    '''--gradient_accumulation_steps''' ,					type=__a ,					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.'''   )
					UpperCamelCase__   :Tuple												= parser.parse_args()
					UpperCamelCase__   :List[Any]												= {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(__a ,					__a   )


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
