



import argparse
import gc
import json
import os

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


snake_case__					:     Optional[Any]    		=   16
snake_case__					:     Tuple    		=   32



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	return int(x / 2**2_0   )

class   _A   :


						'''simple docstring'''





						def __enter__(	self	:							Union[str, Any]	):






							'''simple docstring'''



							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							__lowercase		       =			torch.cuda.memory_allocated()
							return self



						def __exit__(	self	:							Optional[int]							,      *lowerCamelCase	:							Any	):






							'''simple docstring'''



							gc.collect()
							torch.cuda.empty_cache()
							__lowercase		       =			torch.cuda.memory_allocated()
							__lowercase		       =			torch.cuda.max_memory_allocated()
							__lowercase		       =			bamb(self.end - self.begin	)
							__lowercase		       =			bamb(self.peak - self.begin	)
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 1_6  ,	_SCREAMING_SNAKE_CASE = "bert-base-cased"  ,	_SCREAMING_SNAKE_CASE = 3_2_0  ,	_SCREAMING_SNAKE_CASE = 1_6_0  ,	):
	__lowercase		       =			AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			load_dataset(
	    "glue"  ,	"mrpc"  ,	split={"train": F"""train[:{n_train}]""", "validation": F"""validation[:{n_val}]"""}   )

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

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

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

	def collate_fn(_SCREAMING_SNAKE_CASE   ):
		# 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(_SCREAMING_SNAKE_CASE  ,	padding="max_length"  ,	max_length=1_2_8  ,	return_tensors="pt"   )
		return tokenizer.pad(_SCREAMING_SNAKE_CASE  ,	padding="longest"  ,	return_tensors="pt"   )

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

	return train_dataloader, eval_dataloader



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	# Initialize accelerator
	__lowercase		       =			Accelerator()

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

	set_seed(_SCREAMING_SNAKE_CASE   )
	__lowercase       ,    __lowercase		       =			get_dataloaders(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	args.n_train  ,	args.n_val   )

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

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

	if accelerator.state.deepspeed_plugin is not None:
		__lowercase		       =			accelerator.state.deepspeed_plugin.deepspeed_config[
		    "gradient_accumulation_steps"
		]
	else:
		__lowercase		       =			1
	__lowercase		       =			(len(_SCREAMING_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
	):
		__lowercase		       =			get_linear_schedule_with_warmup(
		    optimizer=_SCREAMING_SNAKE_CASE  ,	num_warmup_steps=0  ,	num_training_steps=_SCREAMING_SNAKE_CASE  ,	)
	else:
		__lowercase		       =			DummyScheduler(_SCREAMING_SNAKE_CASE  ,	total_num_steps=_SCREAMING_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.
	__lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase       ,    __lowercase		       =			accelerator.prepare(
	    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

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

	# Now we train the model
	__lowercase		       =			{}
	for epoch in range(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(_SCREAMING_SNAKE_CASE   ):
				__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
				__lowercase		       =			outputs.loss
				__lowercase		       =			loss / gradient_accumulation_steps
				accelerator.backward(_SCREAMING_SNAKE_CASE   )
				if step % gradient_accumulation_steps == 0:
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()

				overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
		accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
		accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
		accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
		accelerator.print(
		    "Total Peak Memory consumed during the train (max): {}".format(
		        tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
		__lowercase		       =			tracemalloc.peaked + bamb(tracemalloc.begin   )
		if args.peak_memory_upper_bound is not None:
			assert (
			    train_total_peak_memory[F"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
			), "Peak memory usage exceeded the upper bound"

	accelerator.wait_for_everyone()
	if accelerator.is_main_process:
		with open(os.path.join(args.output_dir  ,	"peak_memory_utilization.json"   )  ,	"w"   ) as f:
			json.dump(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    ):
	__lowercase		       =			argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
	parser.add_argument(
	    "--model_name_or_path"  ,	type=_SCREAMING_SNAKE_CASE  ,	default="bert-base-cased"  ,	help="Path to pretrained model or model identifier from huggingface.co/models."  ,	required=_SCREAMING_SNAKE_CASE  ,	)
	parser.add_argument(
	    "--output_dir"  ,	type=_SCREAMING_SNAKE_CASE  ,	default="."  ,	help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."  ,	)
	parser.add_argument(
	    "--peak_memory_upper_bound"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=_SCREAMING_SNAKE_CASE  ,	help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."  ,	)
	parser.add_argument(
	    "--n_train"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=3_2_0  ,	help="Number of training examples to use."  ,	)
	parser.add_argument(
	    "--n_val"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=1_6_0  ,	help="Number of validation examples to use."  ,	)
	parser.add_argument(
	    "--num_epochs"  ,	type=_SCREAMING_SNAKE_CASE  ,	default=1  ,	help="Number of train epochs."  ,	)
	__lowercase		       =			parser.parse_args()
	__lowercase		       =			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
	training_function(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	main()








import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case__					:     Optional[Any]    		=   logging.getLogger()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"\n".join(_SCREAMING_SNAKE_CASE   )
	Path(_SCREAMING_SNAKE_CASE   ).open("w"   ).writelines(_SCREAMING_SNAKE_CASE   )


snake_case__					:     List[str]    		=   """patrickvonplaten/t5-tiny-random"""
snake_case__					:     int    		=   """sshleifer/bart-tiny-random"""
snake_case__					:     Union[str, Any]    		=   """sshleifer/tiny-mbart"""

snake_case__					:     List[str]    		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class   _A   ( _lowercase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()
							__lowercase		       =			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
							_dump_articles(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								run_generate()
								assert Path(lowerCamelCase	).exists()
								# os.remove(Path(output_file_name))



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()

							__lowercase		       =			{
							    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
							    "de": [
							        "Maschinelles Lernen ist großartig, oder?",
							        "Ich esse gerne Bananen",
							        "Morgen ist wieder ein toller Tag!",
							    ],
							}

							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	)
							__lowercase		       =			str(tmp_dir / "scores.json"	)
							__lowercase		       =			str(tmp_dir / "val.target"	)
							_dump_articles(lowerCamelCase							,      text["en"]	)
							_dump_articles(lowerCamelCase							,      text["de"]	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {str(lowerCamelCase	)}
            {str(lowerCamelCase	)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								with CaptureStdout() as cs:
									run_search()
								__lowercase		       =			[" num_beams | length_penalty", model, "Best score args"]
								__lowercase		       =			["Info"]
								if "translation" in task:
									expected_strings.append("bleu"	)
								else:
									expected_strings.extend(lowerCamelCase	)
								for w in expected_strings:
									assert w in cs.out
								for w in un_expected_strings:
									assert w not in cs.out
								assert Path(lowerCamelCase	).exists()
								os.remove(Path(lowerCamelCase	)	)



