


'''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()



'''simple docstring'''
from pathlib import Path

import fire
from tqdm import tqdm


def 			a      (			__a="ro" ,					__a="en" ,					__a="wmt16" ,					__a=None   )  ->	None:



					'''simple docstring'''






					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''   )
					UpperCamelCase__   :int												= f'''{src_lang}-{tgt_lang}'''
					print(f'''Converting {dataset}-{pair}'''   )
					UpperCamelCase__   :Tuple												= datasets.load_dataset(__a ,					__a   )
					if save_dir is None:
										UpperCamelCase__   :Any												= f'''{dataset}-{pair}'''
					UpperCamelCase__   :Dict												= Path(__a   )
					save_dir.mkdir(exist_ok=__a   )

					for split in ds.keys():
										print(f'''Splitting {split} with {ds[split].num_rows} records'''   )

										# to save to val.source, val.target like summary datasets
										UpperCamelCase__   :Dict												= '''val''' if split == '''validation''' else split
										UpperCamelCase__   :List[Any]												= save_dir.joinpath(f'''{fn}.source'''   )
										UpperCamelCase__   :int												= save_dir.joinpath(f'''{fn}.target'''   )
										UpperCamelCase__   :Union[str, Any]												= src_path.open('''w+'''   )
										UpperCamelCase__   :Tuple												= tgt_path.open('''w+'''   )

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]   ):
															UpperCamelCase__   :Union[str, Any]												= x['''translation''']
															src_fp.write(ex[src_lang] + '''\n'''   )
															tgt_fp.write(ex[tgt_lang] + '''\n'''   )

					print(f'''Saved {dataset} dataset to {save_dir}'''   )


if __name__ == "__main__":
		fire.Fire(download_wmt_dataset)