

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


a_     =			16
a_     =			32

def        lowerCamelCase__       (    _a):
	return int(x / 2**20)






class _UpperCamelCase							:
			'''simple docstring'''







			def __enter__(	self							:       Tuple		)							->				Any:

				"""simple docstring"""

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






			def __exit__(	self							:       List[str]					,					*a							:       int		)							->				List[str]:

				"""simple docstring"""

				gc.collect()
				torch.cuda.empty_cache()
				SCREAMING_SNAKE_CASE      :					List[Any]    =				torch.cuda.memory_allocated()
				SCREAMING_SNAKE_CASE      :					Dict    =				torch.cuda.max_memory_allocated()
				SCREAMING_SNAKE_CASE      :					str    =				bamb(self.end - self.begin		)
				SCREAMING_SNAKE_CASE      :					Any    =				bamb(self.peak - self.begin		)
				# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")

def        lowerCamelCase__       (    _a							,	_a = 16							,	_a = "bert-base-cased"							,	_a = 320							,	_a = 160							,	):
	SCREAMING_SNAKE_CASE      :					List[Any]    =				AutoTokenizer.from_pretrained(snake_case_)
	SCREAMING_SNAKE_CASE      :					List[str]    =				load_dataset(
	    "glue"							,	"mrpc"							,	split={"train": f"train[:{n_train}]", "validation": f"validation[:{n_val}]"})

	def tokenize_function(_a):
		# max_length=None => use the model max length (it's actually the default)
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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
	SCREAMING_SNAKE_CASE      :					List[Any]    =				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
	SCREAMING_SNAKE_CASE      :					List[Any]    =				tokenized_datasets.rename_column("label"							,	"labels")

	def collate_fn(_a):
		# 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.
	SCREAMING_SNAKE_CASE      :					Dict    =				DataLoader(
	    tokenized_datasets["train"]							,	shuffle=snake_case_							,	collate_fn=snake_case_							,	batch_size=snake_case_)
	SCREAMING_SNAKE_CASE      :					int    =				DataLoader(
	    tokenized_datasets["validation"]							,	shuffle=snake_case_							,	collate_fn=snake_case_							,	batch_size=snake_case_)

	return train_dataloader, eval_dataloader

def        lowerCamelCase__       (    _a							,	_a):
	# Initialize accelerator
	SCREAMING_SNAKE_CASE      :					Tuple    =				Accelerator()

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

	set_seed(snake_case_)
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				get_dataloaders(snake_case_							,	snake_case_							,	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)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				AutoModelForSequenceClassification.from_pretrained(snake_case_							,	return_dict=snake_case_)

	# Instantiate optimizer
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				(
	    AdamW
	    if accelerator.state.deepspeed_plugin is None
	    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
	    else DummyOptim
	)
	SCREAMING_SNAKE_CASE      :					Tuple    =				optimizer_cls(params=model.parameters()							,	lr=snake_case_)

	if accelerator.state.deepspeed_plugin is not None:
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				accelerator.state.deepspeed_plugin.deepspeed_config[
		    """gradient_accumulation_steps"""
		]
	else:
		SCREAMING_SNAKE_CASE      :					int    =				1
	SCREAMING_SNAKE_CASE      :					str    =				(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
	):
		SCREAMING_SNAKE_CASE      :					str    =				get_linear_schedule_with_warmup(
		    optimizer=snake_case_							,	num_warmup_steps=0							,	num_training_steps=snake_case_							,	)
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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.
	SCREAMING_SNAKE_CASE      :					Dict    =				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
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				0
	# We also need to keep track of the stating epoch so files are named properly
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				0

	# Now we train the model
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				{}
	for epoch in range(snake_case_							,	snake_case_):
		with TorchTracemalloc() as tracemalloc:
			model.train()
			for step, batch in enumerate(snake_case_):
				SCREAMING_SNAKE_CASE      :					Dict    =				model(**snake_case_)
				SCREAMING_SNAKE_CASE      :					Dict    =				outputs.loss
				SCREAMING_SNAKE_CASE      :					Dict    =				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

        # 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)))
		SCREAMING_SNAKE_CASE      :					List[str]    =				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(snake_case_							,	snake_case_)

def        lowerCamelCase__       (    ):
	SCREAMING_SNAKE_CASE      :					Optional[Any]    =				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(
	    "--peak_memory_upper_bound"							,	type=snake_case_							,	default=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=snake_case_							,	default=320							,	help="Number of training examples to use."							,	)
	parser.add_argument(
	    "--n_val"							,	type=snake_case_							,	default=160							,	help="Number of validation examples to use."							,	)
	parser.add_argument(
	    "--num_epochs"							,	type=snake_case_							,	default=1							,	help="Number of train epochs."							,	)
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				parser.parse_args()
	SCREAMING_SNAKE_CASE      :					Optional[int]    =				{"""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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

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

      _A							:     Optional[Any]			=						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:
            _A							:     Optional[int]			=						tokenizer.model_max_length
      if prefix is None:
            _A							:     Tuple			=						prefix or getattr(model.config,"""prefix""",""""""     ) or """"""
      _A							:     Optional[int]			=						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.
      _A							:     Optional[int]			=						ds.make_sortish_sampler(snake_case_,distributed=snake_case_,add_extra_examples=snake_case_,shuffle=snake_case_     )
      _A							:     Dict			=						DataLoader(snake_case_,sampler=snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn     )
      _A							:     Optional[Any]			=						[]
      for batch in tqdm(snake_case_     ):
            _A							:     Tuple			=						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_,)
            _A							:     Any			=						tokenizer.batch_decode(snake_case_,skip_special_tokens=snake_case_,clean_up_tokenization_spaces=snake_case_     )
            _A							:     Dict			=						batch["""ids"""]
            if num_return_sequences > 1:
                  _A							:     Any			=						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 				lowerCAmelCase_	(     ):
      _A							:     Tuple			=						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"""     )
      _A							:     Union[str, Any]			=						time.time()
      _A			,   _A							:     List[str]			=						parser.parse_known_args()
      _A							:     List[str]			=						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}'''     )
      _A							:     Dict			=						Path(args.save_dir + """_tmp"""     )
      Path(snake_case_     ).mkdir(exist_ok=snake_case_     )  # this handles locking.
      _A							:     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.
      _A							:     Any			=						{}
      if args.src_lang is not None:
            _A							:     int			=						args.src_lang
      if args.tgt_lang is not None:
            _A							:     Dict			=						args.tgt_lang

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						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:
            _A							:     List[Any]			=						Path(args.save_dir     )
            save_dir.mkdir(exist_ok=snake_case_     )
            _A							:     Tuple			=						gather_results_from_each_node(snake_case_,snake_case_,args.sync_timeout     )
            _A							:     Optional[int]			=						combine_partial_results(snake_case_     )
            if args.num_return_sequences > 1:
                  _A							:     Optional[Any]			=						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
            _A							:     List[str]			=						Path(args.data_dir     ).joinpath(args.type_path + """.target"""     )
            with open(snake_case_     ) as f:
                  _A							:     int			=						[x.rstrip() for x in f.readlines()][: len(snake_case_     )]

            # Calculate metrics, save metrics,  and save _generations.txt
            _A							:     Dict			=						"""translation""" in args.task
            _A							:     Optional[Any]			=						calculate_bleu if calc_bleu else calculate_rouge
            _A							:     Tuple			=						"""bleu""" if calc_bleu else """rouge"""
            _A							:     Dict			=						score_fn(snake_case_,snake_case_     )
            _A							:     List[Any]			=						len(snake_case_     )
            _A							:     Optional[int]			=						time.time() - start_time
            _A							:     Dict			=						round(runtime / metrics["""n_obs"""],4     )
            _A							:     Dict			=						num_replicas
            # TODO(@stas00): add whatever metadata to metrics
            _A							:     Any			=						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 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







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

