"""simple docstring"""





from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


_UpperCamelCase     :		Optional[int]											=					[
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

_UpperCamelCase     :		Optional[int]											=					[
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				bootstrap_aggregation=_lowerCAmelCase     ,				rouge_keys=['rouge2', 'rougeL']  )
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				bootstrap_aggregation=_lowerCAmelCase     ,				rouge_keys=['rouge2']  )
       assert (
           pd.DataFrame(no_aggregation['rouge2']  ).fmeasure.mean()
           == pd.DataFrame(no_aggregation_just_ra['rouge2']  ).fmeasure.mean()
       )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			'rougeLsum'
       lowercase__    :	int     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase     ,				rouge_keys=[k]  )[k]
       lowercase__    :	Tuple     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase     ,				rouge_keys=[k]  )[k]
       assert score > score_no_sep





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			['rouge1', 'rouge2', 'rougeL']
       lowercase__    :	str     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase     ,				rouge_keys=_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase     ,				rouge_keys=_lowerCAmelCase  )
       assert score_sep == score_no_sep





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			[
           'Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.',
           'Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports .',
       ]
       lowercase__    :	Optional[int]     					=			[
           'Margot Frank, died in 1945, a month earlier than previously thought.',
           'Prosecutor: "No videos were used in the crash investigation" German papers say they saw a cell phone video of'
           ' the final seconds on board Flight 9525.',
       ]
       assert calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase  ) == calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				newline_sep=_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Tuple     					=			[
           '" "a person who has such a video needs to immediately give it to the investigators," prosecutor says .<n> "it is a very disturbing scene," editor-in-chief of bild online tells "erin burnett: outfront" '
       ]
       lowercase__    :	Optional[Any]     					=			[
           ' Marseille prosecutor says "so far no videos were used in the crash investigation" despite media reports . Journalists at Bild and Paris Match are "very confident" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says .'
       ]

       lowercase__    :	Tuple     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				rouge_keys=['rougeLsum']     ,				newline_sep=_lowerCAmelCase  )['rougeLsum']
       lowercase__    :	List[str]     					=			calculate_rouge(_lowerCAmelCase     ,				_lowerCAmelCase     ,				rouge_keys=['rougeLsum']  )['rougeLsum']
       assert new_score > prev_score






def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	str     					=			Path('examples/seq2seq/test_data/wmt_en_ro'  )
       lowercase__    :	Any     					=			calculate_rouge_path(data_dir.joinpath('test.source'  )     ,				data_dir.joinpath('test.target'  )  )
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )
       lowercase__    :	Tuple     					=			calculate_rouge_path(
           data_dir.joinpath('test.source'  )     ,				data_dir.joinpath('test.target'  )     ,				bootstrap_aggregation=_lowerCAmelCase  )
       assert isinstance(_lowerCAmelCase     ,				_lowerCAmelCase  )


"""simple docstring"""





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


_UpperCamelCase     :		int											=					16
_UpperCamelCase     :		Union[str, Any]											=					32





def        a_  (   _lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       return int(x / 2**20  )


class   UpperCAmelCase_			:


      def __enter__(     self							) ->      Union[str, Any]:
             gc.collect()
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
             lowercase__    :	List[str]     					=			torch.cuda.memory_allocated()
             return self


      def __exit__(     self	,    *a							) ->      Any:
             gc.collect()
             torch.cuda.empty_cache()
             lowercase__    :	Optional[Any]     					=			torch.cuda.memory_allocated()
             lowercase__    :	Union[str, Any]     					=			torch.cuda.max_memory_allocated()
             lowercase__    :	List[Any]     					=			bamb(self.end - self.begin							)
             lowercase__    :	List[Any]     					=			bamb(self.peak - self.begin							)
             # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				int = 16     ,				_lowerCAmelCase						:				str = "bert-base-cased"     ,				_lowerCAmelCase						:				int = 320     ,				_lowerCAmelCase						:				int = 160     ,				):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			load_dataset(
           'glue'     ,				'mrpc'     ,				split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""}  )

       def tokenize_function(_lowerCAmelCase						:				int  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[str]     					=			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
       lowercase__    :	Union[str, Any]     					=			datasets.map(
           _lowerCAmelCase     ,				batched=_lowerCAmelCase     ,				remove_columns=['idx', 'sentence1', 'sentence2']     ,				load_from_cache_file=_lowerCAmelCase  )

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowercase__    :	Union[str, Any]     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

       def collate_fn(_lowerCAmelCase						:				Any  ):
              # 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(_lowerCAmelCase     ,				padding='max_length'     ,				max_length=128     ,				return_tensors='pt'  )
              return tokenizer.pad(_lowerCAmelCase     ,				padding='longest'     ,				return_tensors='pt'  )

       # Instantiate dataloaders.
       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['validation']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       return train_dataloader, eval_dataloader





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			Accelerator()

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

       set_seed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	Tuple     					=			get_dataloaders(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				args.n_train     ,				args.n_val  )

       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowercase__    :	List[Any]     					=			AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase     ,				return_dict=_lowerCAmelCase  )

       # Instantiate optimizer
       lowercase__    :	List[Any]     					=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__    :	Optional[Any]     					=			optimizer_cls(params=model.parameters()     ,				lr=_lowerCAmelCase  )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__    :	Optional[Any]     					=			accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              lowercase__    :	List[Any]     					=			1
       lowercase__    :	List[Any]     					=			(len(_lowerCAmelCase  ) * 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__    :	Optional[int]     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=0     ,				num_training_steps=_lowerCAmelCase     ,				)
       else:
              lowercase__    :	Tuple     					=			DummyScheduler(_lowerCAmelCase     ,				total_num_steps=_lowerCAmelCase     ,				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__    :	Dict     					=			accelerator.prepare(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

       # We need to keep track of how many total steps we have iterated over
       lowercase__    :	Optional[int]     					=			0
       # We also need to keep track of the stating epoch so files are named properly
       lowercase__    :	Tuple     					=			0

       # Now we train the model
       lowercase__    :	Optional[Any]     					=			{}
       for epoch in range(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            lowercase__    :	List[Any]     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	Dict     					=			outputs.loss
                            lowercase__    :	int     					=			loss / gradient_accumulation_steps
                            accelerator.backward(_lowerCAmelCase  )
                            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__    :	int     					=			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(_lowerCAmelCase     ,				_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'  )
       parser.add_argument(
           '--model_name_or_path'     ,				type=_lowerCAmelCase     ,				default='bert-base-cased'     ,				help='Path to pretrained model or model identifier from huggingface.co/models.'     ,				required=_lowerCAmelCase     ,				)
       parser.add_argument(
           '--output_dir'     ,				type=_lowerCAmelCase     ,				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=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				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=_lowerCAmelCase     ,				default=320     ,				help='Number of training examples to use.'     ,				)
       parser.add_argument(
           '--n_val'     ,				type=_lowerCAmelCase     ,				default=160     ,				help='Number of validation examples to use.'     ,				)
       parser.add_argument(
           '--num_epochs'     ,				type=_lowerCAmelCase     ,				default=1     ,				help='Number of train epochs.'     ,				)
       lowercase__    :	Any     					=			parser.parse_args()
       lowercase__    :	Optional[Any]     					=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()

