
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__							:							Optional[int]									=			1_6
lowercase__							:							Tuple									=			3_2




def 	A_ (							snake_case							:   Accelerator ,      snake_case							:   int = 16 ,      snake_case							:   str = "bert-base-cased" )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			AutoTokenizer.from_pretrained(snake_case )
						__UpperCamelCase       =			load_dataset('''glue''' ,      '''mrpc''' )

						def tokenize_function(snake_case							:   Dict ):
												# max_length=None => use the model max length (it's actually the default)
												__UpperCamelCase       =			tokenizer(examples['''sentence1'''] ,      examples['''sentence2'''] ,      truncation=snake_case ,      max_length=snake_case )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						__UpperCamelCase       =			datasets.map(
						    snake_case ,      batched=snake_case ,      remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,      load_from_cache_file=snake_case )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__UpperCamelCase       =			tokenized_datasets.rename_column('''label''' ,      '''labels''' )

						def collate_fn(snake_case							:   Tuple ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(snake_case ,      padding='''max_length''' ,      max_length=128 ,      return_tensors='''pt''' )
												return tokenizer.pad(snake_case ,      padding='''longest''' ,      return_tensors='''pt''' )

						# Instantiate dataloaders.
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''train'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''validation'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )

						return train_dataloader, eval_dataloader




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__UpperCamelCase       =			config['''lr''']
						__UpperCamelCase       =			int(config['''num_epochs'''] )
						__UpperCamelCase       =			int(config['''seed'''] )
						__UpperCamelCase       =			int(config['''batch_size'''] )
						__UpperCamelCase       =			args.model_name_or_path

						set_seed(snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			get_dataloaders(snake_case ,      snake_case ,      snake_case )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase       =			AutoModelForSequenceClassification.from_pretrained(snake_case ,      return_dict=snake_case )

						# Instantiate optimizer
						__UpperCamelCase       =			(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCamelCase       =			optimizer_cls(params=model.parameters() ,      lr=snake_case )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCamelCase       =			accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCamelCase       =			1
						__UpperCamelCase       =			(len(snake_case ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__UpperCamelCase       =			get_linear_schedule_with_warmup(
												    optimizer=snake_case ,      num_warmup_steps=0 ,      num_training_steps=snake_case ,      )
						else:
												__UpperCamelCase       =			DummyScheduler(snake_case ,      total_num_steps=snake_case ,      warmup_num_steps=0 )

						# 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       =			accelerator.prepare(
						    snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )

						# We need to keep track of how many total steps we have iterated over
						__UpperCamelCase       =			0
						# We also need to keep track of the stating epoch so files are named properly
						__UpperCamelCase       =			0

						# Now we train the model
						__UpperCamelCase       =			evaluate.load('''glue''' ,      '''mrpc''' )
						__UpperCamelCase       =			0
						__UpperCamelCase       =			{}
						for epoch in range(snake_case ,      snake_case ):
												model.train()
												for step, batch in enumerate(snake_case ):
																		__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.loss
																		__UpperCamelCase       =			loss / gradient_accumulation_steps
																		accelerator.backward(snake_case )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												__UpperCamelCase       =			0
												for step, batch in enumerate(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		with torch.no_grad():
																								__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.logits.argmax(dim=-1 )
																		# It is slightly faster to call this once, than multiple times
																		__UpperCamelCase  ,							__UpperCamelCase       =			accelerator.gather(
																		    (predictions, batch['''labels''']) )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(snake_case ) - 1:
																														__UpperCamelCase       =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
																														__UpperCamelCase       =			references[: len(eval_dataloader.dataset ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=snake_case ,      references=snake_case ,      )

												__UpperCamelCase       =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:" ,      snake_case )
												__UpperCamelCase       =			eval_metric['''accuracy''']

												if best_performance < eval_metric["accuracy"]:
																		__UpperCamelCase       =			eval_metric['''accuracy''']

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir ,      '''all_results.json''' ) ,      '''w''' ) as f:
																		json.dump(snake_case ,      snake_case )




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
						parser.add_argument(
						    '''--model_name_or_path''' ,      type=snake_case ,      default='''bert-base-cased''' ,      help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,      required=snake_case ,      )
						parser.add_argument(
						    '''--output_dir''' ,      type=snake_case ,      default='''.''' ,      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,      )
						parser.add_argument(
						    '''--performance_lower_bound''' ,      type=snake_case ,      default=snake_case ,      help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' ,      )
						parser.add_argument(
						    '''--num_epochs''' ,      type=snake_case ,      default=3 ,      help='''Number of train epochs.''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case ,      snake_case )


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,
)


lowercase__							:							Union[str, Any]									=			getLogger(__name__)




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      snake_case							:   str ,      snake_case							:   int = 8 ,      snake_case							:   int = 1024 ,      snake_case							:   Dict="val" ,      snake_case							:   Tuple=None ,      snake_case							:   int=False ,      snake_case							:   Union[str, Any]="summarization" ,      snake_case							:   Optional[Any]=None ,      snake_case							:   List[str]=1 ,      snake_case							:   Dict = None ,      snake_case							:   Optional[Any]="" ,      **snake_case							:   Tuple ,      )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			str(snake_case )
						assert local_rank is not None
						torch.distributed.init_process_group(backend='''nccl''' ,      rank=snake_case )

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

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

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




def 	A_ (							)      ->    int:



						'''simple docstring'''

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

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

						Path(args.save_dir ).mkdir(exist_ok=snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			eval_data_dir(
						    args.data_dir ,      snake_case ,      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=snake_case ,      **snake_case ,      )

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

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




def 	A_ (							snake_case							:   Optional[int] )      ->    List:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						for partial_result in partial_results:
												records.extend(snake_case )
						__UpperCamelCase       =			sorted(snake_case ,      key=lambda snake_case : x["id"] )
						__UpperCamelCase       =			[x['''pred'''] for x in records]
						return preds




def 	A_ (							snake_case							:   List[str] ,      snake_case							:   int ,      snake_case							:   str )      ->    List[Dict[str, List]]:



						'''simple docstring'''

						__UpperCamelCase       =			time.time()
						logger.info('''waiting for all nodes to finish''' )
						__UpperCamelCase       =			None
						while (time.time() - start_wait) < timeout:
												__UpperCamelCase       =			list(save_dir.glob('''rank_*.json''' ) )
												if len(snake_case ) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		__UpperCamelCase       =			lmap(snake_case ,      snake_case )
																		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()




