

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 the checkpointing capability,
# 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							:						Optional[Any]     			=		16
lowerCAmelCase							:						List[str]     			=		32







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase = 16					):
						SCREAMING_SNAKE_CASE_:				int							= AutoTokenizer.from_pretrained("bert-base-cased"					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= load_dataset("glue"       ,			"mrpc"					)

						def tokenize_function(_UpperCAmelCase					):
												# max_length=None => use the model max length (it's actually the default)
												SCREAMING_SNAKE_CASE_:				Tuple							= 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():
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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
						SCREAMING_SNAKE_CASE_:				int							= tokenized_datasets.rename_column("label"       ,			"labels"					)

						def collate_fn(_UpperCAmelCase					):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												SCREAMING_SNAKE_CASE_:				str							= 1_28 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_:				Tuple							= 16
												elif accelerator.mixed_precision != "no":
																		SCREAMING_SNAKE_CASE_:				Any							= 8
												else:
																		SCREAMING_SNAKE_CASE_:				Dict							= None

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

						# Instantiate dataloaders.
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= DataLoader(
						    tokenized_datasets["train"]       ,			shuffle=_UpperCAmelCase       ,			collate_fn=_UpperCAmelCase       ,			batch_size=_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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

						lowerCAmelCase							:						Optional[Any]     			=		mocked_dataloaders  # noqa: F811







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						# For testing only
						if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,			_UpperCAmelCase					) == "1":
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 2
						# Initialize accelerator
						SCREAMING_SNAKE_CASE_:				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_:				Optional[int]							= config["lr"]
						SCREAMING_SNAKE_CASE_:				List[str]							= int(config["num_epochs"]					)
						SCREAMING_SNAKE_CASE_:				List[Any]							= int(config["seed"]					)
						SCREAMING_SNAKE_CASE_:				List[str]							= int(config["batch_size"]					)

						# New Code #
						# Parse out whether we are saving every epoch or after a certain number of batches
						if hasattr(args.checkpointing_steps       ,			"isdigit"					):
												if args.checkpointing_steps == "epoch":
																		SCREAMING_SNAKE_CASE_:				Any							= args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		SCREAMING_SNAKE_CASE_:				Dict							= int(args.checkpointing_steps					)
												else:
																		raise ValueError(
																		    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."					)
						else:
												SCREAMING_SNAKE_CASE_:				Optional[int]							= None

						set_seed(_UpperCAmelCase					)

						SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				int							= get_dataloaders(_UpperCAmelCase       ,			_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Tuple							= evaluate.load("glue"       ,			"mrpc"					)

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

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

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

						# Instantiate scheduler
						SCREAMING_SNAKE_CASE_:				List[Any]							= get_linear_schedule_with_warmup(
						    optimizer=_UpperCAmelCase       ,			num_warmup_steps=1_00       ,			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.
						SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				List[str]							= accelerator.prepare(
						    _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)

						# New Code #
						# We need to keep track of how many total steps we have iterated over
						SCREAMING_SNAKE_CASE_:				str							= 0
						# We also need to keep track of the stating epoch so files are named properly
						SCREAMING_SNAKE_CASE_:				List[Any]							= 0

						# We need to load the checkpoint back in before training here with `load_state`
						# The total number of epochs is adjusted based on where the state is being loaded from,
						# as we assume continuation of the same training script
						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					)
																		SCREAMING_SNAKE_CASE_:				List[str]							= os.path.basename(args.resume_from_checkpoint					)
												else:
																		# Get the most recent checkpoint
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= [f.name for f in os.scandir(os.getcwd()					) if f.is_dir()]
																		dirs.sort(key=os.path.getctime					)
																		SCREAMING_SNAKE_CASE_:				List[str]							= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												SCREAMING_SNAKE_CASE_:				Any							= os.path.splitext(_UpperCAmelCase					)[0]

												if "epoch" in training_difference:
																		SCREAMING_SNAKE_CASE_:				Tuple							= int(training_difference.replace("epoch_"       ,			""					)					) + 1
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= None
												else:
																		SCREAMING_SNAKE_CASE_:				Tuple							= int(training_difference.replace("step_"       ,			""					)					)
																		SCREAMING_SNAKE_CASE_:				Any							= resume_step // len(_UpperCAmelCase					)
																		resume_step -= starting_epoch * len(_UpperCAmelCase					)

    # Now we train the model
						for epoch in range(_UpperCAmelCase       ,			_UpperCAmelCase					):
												model.train()
												# New Code #
												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
																		SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= 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					)
																		SCREAMING_SNAKE_CASE_:				int							= model(**_UpperCAmelCase					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= outputs.loss
																		SCREAMING_SNAKE_CASE_:				str							= loss / gradient_accumulation_steps
																		accelerator.backward(_UpperCAmelCase					)
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()
																		# New Code #
																		overall_step += 1

																		# New Code #
																		# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
																		# These are saved to folders named `step_{overall_step}`
																		# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
																		# If mixed precision was used, will also save a "scalar.bin" file
																		if isinstance(_UpperCAmelCase       ,			_UpperCAmelCase					):
																								SCREAMING_SNAKE_CASE_:				List[Any]							= f"step_{overall_step}"
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				SCREAMING_SNAKE_CASE_:				Optional[int]							= 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` (the default).
																		batch.to(accelerator.device					)
																		with torch.no_grad():
																								SCREAMING_SNAKE_CASE_:				Optional[int]							= model(**_UpperCAmelCase					)
																		SCREAMING_SNAKE_CASE_:				str							= outputs.logits.argmax(dim=-1					)
																		SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Dict							= accelerator.gather_for_metrics((predictions, batch["labels"])					)
																		metric.add_batch(
																		    predictions=_UpperCAmelCase       ,			references=_UpperCAmelCase       ,			)

												SCREAMING_SNAKE_CASE_:				Optional[int]							= metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:"       ,			_UpperCAmelCase					)

												# New Code #
												# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
												# These are saved to folders named `epoch_{epoch}`
												# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
												# If mixed precision was used, will also save a "scalar.bin" file
												if checkpointing_steps == "epoch":
																		SCREAMING_SNAKE_CASE_:				Tuple							= f"epoch_{epoch}"
																		if args.output_dir is not None:
																								SCREAMING_SNAKE_CASE_:				Union[str, Any]							= os.path.join(args.output_dir       ,			_UpperCAmelCase					)
																		accelerator.save_state(_UpperCAmelCase					)







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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(
						    "--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(
						    "--resume_from_checkpoint"       ,			type=_UpperCAmelCase       ,			default=_UpperCAmelCase       ,			help="If the training should continue from a checkpoint folder."       ,			)
						SCREAMING_SNAKE_CASE_:				int							= parser.parse_args()
						SCREAMING_SNAKE_CASE_:				List[Any]							= {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
						training_function(_UpperCAmelCase       ,			_UpperCAmelCase					)


if __name__ == "__main__":
						main()









import io
import json
import subprocess


lowerCAmelCase							:						Dict     			=		[
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

lowerCAmelCase							:						List[str]     			=		8







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Dict							= {}
						for src, tgt in pairs:
												SCREAMING_SNAKE_CASE_:				Tuple							= f"{src}-{tgt}"
												SCREAMING_SNAKE_CASE_:				List[str]							= f"sacrebleu -t wmt19 -l {pair} --echo src".split()
												SCREAMING_SNAKE_CASE_:				int							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
												SCREAMING_SNAKE_CASE_:				Dict							= subprocess.run(_UpperCAmelCase       ,			stdout=subprocess.PIPE					).stdout.decode("utf-8"					).splitlines()
												SCREAMING_SNAKE_CASE_:				Tuple							= {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
						return text


lowerCAmelCase							:						Tuple     			=		get_all_data(pairs, n_objs)
lowerCAmelCase							:						Optional[Any]     			=		"""./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
						lowerCAmelCase							:						List[str]     			=		json.dump(text, f, indent=2, ensure_ascii=False)






