




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,
)


__UpperCamelCase					:       str    					=      getLogger(__name__)





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase = 8							,			__lowerCamelCase = 1024							,			__lowerCamelCase="val"							,			__lowerCamelCase=None							,			__lowerCamelCase=False							,			__lowerCamelCase="summarization"							,			__lowerCamelCase=None							,			__lowerCamelCase=1							,			__lowerCamelCase = None							,			__lowerCamelCase=""							,			**__lowerCamelCase							,			)      ->       Dict:
	a										=							str(__lowerCamelCase    )
	assert local_rank is not None
	torch.distributed.init_process_group(backend="""nccl"""							,			rank=__lowerCamelCase    )

	a										=							Path(__lowerCamelCase    )
	a										=							save_dir.joinpath(f'rank_{local_rank}_output.json'    )
	torch.cuda.set_device(__lowerCamelCase    )
	a										=							AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase    ).cuda()
	if fpaa:
		a										=							model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(__lowerCamelCase							,			__lowerCamelCase    )  # update config with task specific params
	a										=							generate_kwargs.pop("""num_beams"""							,			model.config.num_beams    )  # AttributeError risk?
	if num_return_sequences > num_beams:
		a										=							num_return_sequences

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

	if max_source_length is None:
		a										=							tokenizer.model_max_length
	if prefix is None:
		a										=							prefix or getattr(model.config							,			"""prefix"""							,			""""""    ) or """"""
	a										=							SeqaSeqDataset(
	    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			max_target_length=1024							,			type_path=__lowerCamelCase							,			n_obs=__lowerCamelCase							,			prefix=__lowerCamelCase							,			**__lowerCamelCase							,			)
	# 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.
	a										=							ds.make_sortish_sampler(__lowerCamelCase							,			distributed=__lowerCamelCase							,			add_extra_examples=__lowerCamelCase							,			shuffle=__lowerCamelCase    )
	a										=							DataLoader(__lowerCamelCase							,			sampler=__lowerCamelCase							,			batch_size=__lowerCamelCase							,			collate_fn=ds.collate_fn    )
	a										=							[]
	for batch in tqdm(__lowerCamelCase    ):
		a										=							model.generate(
		    input_ids=batch["""input_ids"""].to(model.device    )							,			attention_mask=batch["""attention_mask"""].to(model.device    )							,			num_return_sequences=__lowerCamelCase							,			num_beams=__lowerCamelCase							,			**__lowerCamelCase							,			)
		a										=							tokenizer.batch_decode(__lowerCamelCase							,			skip_special_tokens=__lowerCamelCase							,			clean_up_tokenization_spaces=__lowerCamelCase    )
		a										=							batch["""ids"""]
		if num_return_sequences > 1:
			a										=							chunks(__lowerCamelCase							,			__lowerCamelCase    )  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(__lowerCamelCase    ):
			results.append({"""pred""": pred, """id""": ids[i].item()}    )
	save_json(__lowerCamelCase							,			__lowerCamelCase    )
	return results, sampler.num_replicas





def 		__A		(	)      ->       Optional[Any]:
	a										=							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=__lowerCamelCase							,			help="""like cnn_dm/test.source"""    )
	parser.add_argument(
	    """--model_name"""							,			type=__lowerCamelCase							,			help="""like facebook/bart-large-cnn,t5-base, etc."""							,			default="""sshleifer/distilbart-xsum-12-3"""							,			)
	parser.add_argument("""--save_dir"""							,			type=__lowerCamelCase							,			help="""where to save"""							,			default="""tmp_gen"""    )
	parser.add_argument("""--max_source_length"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase    )
	parser.add_argument(
	    """--type_path"""							,			type=__lowerCamelCase							,			default="""test"""							,			help="""which subset to evaluate typically train/val/test"""    )
	parser.add_argument("""--task"""							,			type=__lowerCamelCase							,			default="""summarization"""							,			help="""used for task_specific_params + metrics"""    )
	parser.add_argument("""--bs"""							,			type=__lowerCamelCase							,			default=8							,			required=__lowerCamelCase							,			help="""batch size"""    )
	parser.add_argument(
	    """--local_rank"""							,			type=__lowerCamelCase							,			default=-1							,			required=__lowerCamelCase							,			help="""should be passed by distributed.launch"""    )

	parser.add_argument(
	    """--n_obs"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			required=__lowerCamelCase							,			help="""How many observations. Defaults to all."""    )
	parser.add_argument(
	    """--num_return_sequences"""							,			type=__lowerCamelCase							,			default=1							,			required=__lowerCamelCase							,			help="""How many sequences to return"""    )
	parser.add_argument(
	    """--sync_timeout"""							,			type=__lowerCamelCase							,			default=600							,			required=__lowerCamelCase							,			help="""How long should master process wait for other processes to finish."""							,			)
	parser.add_argument("""--src_lang"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			required=__lowerCamelCase    )
	parser.add_argument("""--tgt_lang"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			required=__lowerCamelCase    )
	parser.add_argument(
	    """--prefix"""							,			type=__lowerCamelCase							,			required=__lowerCamelCase							,			default=__lowerCamelCase							,			help="""will be added to the begininng of src examples"""    )
	parser.add_argument("""--fp16"""							,			action="""store_true"""    )
	parser.add_argument("""--debug"""							,			action="""store_true"""    )
	a										=							time.time()
	a				, a										=							parser.parse_known_args()
	a										=							parse_numeric_n_bool_cl_kwargs(__lowerCamelCase    )
	if generate_kwargs and args.local_rank <= 0:
		print(f'parsed the following generate kwargs: {generate_kwargs}'    )
	a										=							Path(args.save_dir + """_tmp"""    )
	Path(__lowerCamelCase    ).mkdir(exist_ok=__lowerCamelCase    )  # this handles locking.
	a										=							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.
	a										=							{}
	if args.src_lang is not None:
		a										=							args.src_lang
	if args.tgt_lang is not None:
		a										=							args.tgt_lang

	Path(args.save_dir    ).mkdir(exist_ok=__lowerCamelCase    )
	a				, a										=							eval_data_dir(
	    args.data_dir							,			__lowerCamelCase							,			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=__lowerCamelCase							,			**__lowerCamelCase							,			)

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

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





def 		__A		(	__lowerCamelCase    )      ->       List:
	a										=							[]
	for partial_result in partial_results:
		records.extend(__lowerCamelCase    )
	a										=							sorted(__lowerCamelCase							,			key=lambda __lowerCamelCase    : x["id"]    )
	a										=							[x["""pred"""] for x in records]
	return preds





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Dict[str, List]]:
	# WAIT FOR lots of .json files
	a										=							time.time()
	logger.info("""waiting for all nodes to finish"""    )
	a										=							None
	while (time.time() - start_wait) < timeout:
		a										=							list(save_dir.glob("""rank_*.json"""    )    )
		if len(__lowerCamelCase    ) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			a										=							lmap(__lowerCamelCase							,			__lowerCamelCase    )
			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()







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


__UpperCamelCase					:       Any    					=      16
__UpperCamelCase					:       Union[str, Any]    					=      32





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16    )      ->       List[str]:
	a										=							AutoTokenizer.from_pretrained("""bert-base-cased"""    )
	a										=							load_dataset("""glue"""							,			"""mrpc"""    )

	def tokenize_function(__lowerCamelCase    ):
		# max_length=None => use the model max length (it's actually the default)
		a										=							tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=__lowerCamelCase							,			max_length=__lowerCamelCase    )
		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():
		a										=							datasets.map(
		    __lowerCamelCase							,			batched=__lowerCamelCase							,			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
	a										=							tokenized_datasets.rename_column("""label"""							,			"""labels"""    )

	def collate_fn(__lowerCamelCase    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		a										=							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":
			a										=							16
		elif accelerator.mixed_precision != "no":
			a										=							8
		else:
			a										=							None

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

	# Instantiate dataloaders.
	a										=							DataLoader(
	    tokenized_datasets["""train"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )
	a										=							DataLoader(
	    tokenized_datasets["""validation"""]							,			shuffle=__lowerCamelCase							,			collate_fn=__lowerCamelCase							,			batch_size=__lowerCamelCase    )

	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

		__UpperCamelCase					:       Any    					=      mocked_dataloaders  # noqa: F811





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       List[str]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			__lowerCamelCase    ) == "1":
		a										=							2
	# New Code #
	a										=							int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	a										=							Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=__lowerCamelCase    )
	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
	a										=							config["""lr"""]
	a										=							int(config["""num_epochs"""]    )
	a										=							int(config["""seed"""]    )
	a										=							int(config["""batch_size"""]    )

	a										=							evaluate.load("""glue"""							,			"""mrpc"""    )

	set_seed(__lowerCamelCase    )
	a				, a										=							get_dataloaders(__lowerCamelCase							,			__lowerCamelCase    )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	a										=							AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""							,			return_dict=__lowerCamelCase    )

	# 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).
	a										=							model.to(accelerator.device    )

	# Instantiate optimizer
	a										=							AdamW(params=model.parameters()							,			lr=__lowerCamelCase    )

	# Instantiate scheduler
	a										=							get_linear_schedule_with_warmup(
	    optimizer=__lowerCamelCase							,			num_warmup_steps=100							,			num_training_steps=(len(__lowerCamelCase    ) * 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.
	a				, a				, a				, a				, a										=							accelerator.prepare(
	    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )

	# Now we train the model
	for epoch in range(__lowerCamelCase    ):
		model.train()
		for step, batch in enumerate(__lowerCamelCase    ):
			# 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(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							output.loss
				accelerator.backward(__lowerCamelCase    )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

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





def 		__A		(	)      ->       int:
	a										=							argparse.ArgumentParser(description="""Simple example of training script."""    )
	parser.add_argument(
	    """--mixed_precision"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			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=__lowerCamelCase							,			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."""    )
	a										=							parser.parse_args()
	a										=							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()

