



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


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





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

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

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

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

		# Instantiate dataloaders.
		__UpperCAmelCase    :     Optional[Any]													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[Any]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

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

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

		# Instantiate optimizer
		__UpperCAmelCase    :     Any													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)

		# Instantiate scheduler
		__UpperCAmelCase    :     str													=	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.
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	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						)
						__UpperCAmelCase    :     int													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.loss
						__UpperCAmelCase    :     str													=	loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__UpperCAmelCase    :     Tuple													=	0
				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():
								__UpperCAmelCase    :     Dict													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	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(_UpperCAmelCase						) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										__UpperCAmelCase    :     Dict													=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
										__UpperCAmelCase    :     List[str]													=	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=_UpperCAmelCase ,   references=_UpperCAmelCase ,   )

				__UpperCAmelCase    :     Tuple													=	metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'epoch {epoch}:' ,   _UpperCAmelCase						)

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	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.'''						)
		__UpperCAmelCase    :     int													=	parser.parse_args()
		__UpperCAmelCase    :     Union[str, Any]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()










import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__A   			=getLogger(__name__)

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					str ,   _UpperCAmelCase							:					int = 8 ,   _UpperCAmelCase							:					int = 10_24 ,   _UpperCAmelCase							:					Any="val" ,   _UpperCAmelCase							:					Dict=None ,   _UpperCAmelCase							:					Any=False ,   _UpperCAmelCase							:					Union[str, Any]="summarization" ,   _UpperCAmelCase							:					Optional[Any]=None ,   _UpperCAmelCase							:					Union[str, Any]=1 ,   _UpperCAmelCase							:					Dict = None ,   _UpperCAmelCase							:					Dict="" ,   **_UpperCAmelCase							:					List[str] ,   ):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	str(_UpperCAmelCase						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='''nccl''' ,   rank=_UpperCAmelCase						)

		__UpperCAmelCase    :     Union[str, Any]													=	Path(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	save_dir.joinpath(f'rank_{local_rank}_output.json'						)
		torch.cuda.set_device(_UpperCAmelCase						)
		__UpperCAmelCase    :     int													=	AutoModelForSeqaSeqLM.from_pretrained(_UpperCAmelCase						).cuda()
		if fpaa:
				__UpperCAmelCase    :     Any													=	model.half()
		# determine if we need to increase num_beams
		use_task_specific_params(_UpperCAmelCase ,   _UpperCAmelCase						)  # update config with task specific params
		__UpperCAmelCase    :     List[str]													=	generate_kwargs.pop('''num_beams''' ,   model.config.num_beams						)  # AttributeError risk?
		if num_return_sequences > num_beams:
				__UpperCAmelCase    :     Any													=	num_return_sequences

		__UpperCAmelCase    :     Tuple													=	AutoTokenizer.from_pretrained(_UpperCAmelCase						)
		logger.info(f'Inferred tokenizer type: {tokenizer.__class__}'						)  # if this is wrong, check config.model_type.

		if max_source_length is None:
				__UpperCAmelCase    :     Optional[Any]													=	tokenizer.model_max_length
		if prefix is None:
				__UpperCAmelCase    :     str													=	prefix or getattr(model.config ,   '''prefix''' ,   ''''''						) or ''''''
		__UpperCAmelCase    :     Union[str, Any]													=	SeqaSeqDataset(
		    _UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase ,   max_target_length=10_24 ,   type_path=_UpperCAmelCase ,   n_obs=_UpperCAmelCase ,   prefix=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
		# I set shuffle=True for a more accurate progress bar.
		# If all the longest samples are first, the prog bar estimate is too high at the beginning.
		__UpperCAmelCase    :     str													=	ds.make_sortish_sampler(_UpperCAmelCase ,   distributed=_UpperCAmelCase ,   add_extra_examples=_UpperCAmelCase ,   shuffle=_UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	DataLoader(_UpperCAmelCase ,   sampler=_UpperCAmelCase ,   batch_size=_UpperCAmelCase ,   collate_fn=ds.collate_fn						)
		__UpperCAmelCase    :     List[Any]													=	[]
		for batch in tqdm(_UpperCAmelCase						):
				__UpperCAmelCase    :     str													=	model.generate(
				    input_ids=batch['''input_ids'''].to(model.device						) ,   attention_mask=batch['''attention_mask'''].to(model.device						) ,   num_return_sequences=_UpperCAmelCase ,   num_beams=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
				__UpperCAmelCase    :     Optional[int]													=	tokenizer.batch_decode(_UpperCAmelCase ,   skip_special_tokens=_UpperCAmelCase ,   clean_up_tokenization_spaces=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	batch['''ids''']
				if num_return_sequences > 1:
						__UpperCAmelCase    :     Any													=	chunks(_UpperCAmelCase ,   _UpperCAmelCase						)  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(_UpperCAmelCase						):
						results.append({'''pred''': pred, '''id''': ids[i].item()}						)
		save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
		return results, sampler.num_replicas

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	argparse.ArgumentParser(
		    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''						)
		parser.add_argument('''--data_dir''' ,   type=_UpperCAmelCase ,   help='''like cnn_dm/test.source'''						)
		parser.add_argument(
		    '''--model_name''' ,   type=_UpperCAmelCase ,   help='''like facebook/bart-large-cnn,t5-base, etc.''' ,   default='''sshleifer/distilbart-xsum-12-3''' ,   )
		parser.add_argument('''--save_dir''' ,   type=_UpperCAmelCase ,   help='''where to save''' ,   default='''tmp_gen'''						)
		parser.add_argument('''--max_source_length''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase						)
		parser.add_argument(
		    '''--type_path''' ,   type=_UpperCAmelCase ,   default='''test''' ,   help='''which subset to evaluate typically train/val/test'''						)
		parser.add_argument('''--task''' ,   type=_UpperCAmelCase ,   default='''summarization''' ,   help='''used for task_specific_params + metrics'''						)
		parser.add_argument('''--bs''' ,   type=_UpperCAmelCase ,   default=8 ,   required=_UpperCAmelCase ,   help='''batch size'''						)
		parser.add_argument(
		    '''--local_rank''' ,   type=_UpperCAmelCase ,   default=-1 ,   required=_UpperCAmelCase ,   help='''should be passed by distributed.launch'''						)

		parser.add_argument(
		    '''--n_obs''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase ,   help='''How many observations. Defaults to all.'''						)
		parser.add_argument(
		    '''--num_return_sequences''' ,   type=_UpperCAmelCase ,   default=1 ,   required=_UpperCAmelCase ,   help='''How many sequences to return'''						)
		parser.add_argument(
		    '''--sync_timeout''' ,   type=_UpperCAmelCase ,   default=6_00 ,   required=_UpperCAmelCase ,   help='''How long should master process wait for other processes to finish.''' ,   )
		parser.add_argument('''--src_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument('''--tgt_lang''' ,   type=_UpperCAmelCase ,   default=_UpperCAmelCase ,   required=_UpperCAmelCase						)
		parser.add_argument(
		    '''--prefix''' ,   type=_UpperCAmelCase ,   required=_UpperCAmelCase ,   default=_UpperCAmelCase ,   help='''will be added to the begininng of src examples'''						)
		parser.add_argument('''--fp16''' ,   action='''store_true'''						)
		parser.add_argument('''--debug''' ,   action='''store_true'''						)
		__UpperCAmelCase    :     Any													=	time.time()
		__UpperCAmelCase							,					__UpperCAmelCase    :     Any													=	parser.parse_known_args()
		__UpperCAmelCase    :     List[Any]													=	parse_numeric_n_bool_cl_kwargs(_UpperCAmelCase						)
		if generate_kwargs and args.local_rank <= 0:
				print(f'parsed the following generate kwargs: {generate_kwargs}'						)
		__UpperCAmelCase    :     Union[str, Any]													=	Path(args.save_dir + '''_tmp'''						)
		Path(_UpperCAmelCase						).mkdir(exist_ok=_UpperCAmelCase						)  # this handles locking.
		__UpperCAmelCase    :     int													=	list(json_save_dir.glob('''rank_*.json'''						)						)
		if intermediate_files:
				raise ValueError(f'Found files at {json_save_dir} please move or remove them.'						)
				# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
		__UpperCAmelCase    :     List[Any]													=	{}
		if args.src_lang is not None:
				__UpperCAmelCase    :     List[str]													=	args.src_lang
		if args.tgt_lang is not None:
				__UpperCAmelCase    :     List[Any]													=	args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	eval_data_dir(
		    args.data_dir ,   _UpperCAmelCase ,   args.model_name ,   type_path=args.type_path ,   bs=args.bs ,   fpaa=args.fpaa ,   task=args.task ,   local_rank=args.local_rank ,   n_obs=args.n_obs ,   max_source_length=args.max_source_length ,   num_return_sequences=args.num_return_sequences ,   prefix=args.prefix ,   dataset_kwargs=_UpperCAmelCase ,   **_UpperCAmelCase ,   )

		if args.local_rank <= 0:
				__UpperCAmelCase    :     int													=	Path(args.save_dir						)
				save_dir.mkdir(exist_ok=_UpperCAmelCase						)
				__UpperCAmelCase    :     List[str]													=	gather_results_from_each_node(_UpperCAmelCase ,   _UpperCAmelCase ,   args.sync_timeout						)
				__UpperCAmelCase    :     List[Any]													=	combine_partial_results(_UpperCAmelCase						)
				if args.num_return_sequences > 1:
						__UpperCAmelCase    :     int													=	save_dir.joinpath('''pseudolabel_results.json'''						)
						print(f'Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'						)
						save_json(_UpperCAmelCase ,   _UpperCAmelCase						)
						return
				__UpperCAmelCase    :     str													=	Path(args.data_dir						).joinpath(args.type_path + '''.target'''						)
				with open(_UpperCAmelCase						) as f:
						__UpperCAmelCase    :     int													=	[x.rstrip() for x in f.readlines()][: len(_UpperCAmelCase						)]

				# Calculate metrics, save metrics,  and save _generations.txt
				__UpperCAmelCase    :     Optional[Any]													=	'''translation''' in args.task
				__UpperCAmelCase    :     Optional[int]													=	calculate_bleu if calc_bleu else calculate_rouge
				__UpperCAmelCase    :     Union[str, Any]													=	'''bleu''' if calc_bleu else '''rouge'''
				__UpperCAmelCase    :     Dict													=	score_fn(_UpperCAmelCase ,   _UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	len(_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	time.time() - start_time
				__UpperCAmelCase    :     List[str]													=	round(runtime / metrics['''n_obs'''] ,   4						)
				__UpperCAmelCase    :     List[str]													=	num_replicas
				# TODO(@stas00): add whatever metadata to metrics
				__UpperCAmelCase    :     List[Any]													=	save_dir.joinpath(f'{args.type_path}_{metric_name}.json'						)
				save_json(_UpperCAmelCase ,   _UpperCAmelCase ,   indent=_UpperCAmelCase						)
				print(_UpperCAmelCase						)
				write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}_generations.txt'						)						)
				if args.debug:
						write_txt_file(_UpperCAmelCase ,   save_dir.joinpath(f'{args.type_path}.target'						)						)
				else:
						shutil.rmtree(_UpperCAmelCase						)

def        a						(      _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[Any]													=	[]
		for partial_result in partial_results:
				records.extend(_UpperCAmelCase						)
		__UpperCAmelCase    :     List[str]													=	sorted(_UpperCAmelCase ,   key=lambda _UpperCAmelCase						: x["id"]						)
		__UpperCAmelCase    :     Union[str, Any]													=	[x['''pred'''] for x in records]
		return preds

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Union[str, Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     List[str]													=	time.time()
		logger.info('''waiting for all nodes to finish'''						)
		__UpperCAmelCase    :     Any													=	None
		while (time.time() - start_wait) < timeout:
				__UpperCAmelCase    :     List[Any]													=	list(save_dir.glob('''rank_*.json'''						)						)
				if len(_UpperCAmelCase						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						__UpperCAmelCase    :     Union[str, Any]													=	lmap(_UpperCAmelCase ,   _UpperCAmelCase						)
						return json_data
				except JSONDecodeError:
						continue
		else:
				raise TimeoutError('''Rank 0 gave up on waiting for other processes'''						)
		# Unreachable


if __name__ == "__main__":
				# Usage for MT:
				run_generate()





