

"""simple docstring"""




import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


SCREAMING_SNAKE_CASE_	  =			getLogger(__name__)


SCREAMING_SNAKE_CASE_	  =			'''cuda''' if torch.cuda.is_available() else '''cpu'''



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__ = 8	,    A__ = DEFAULT_DEVICE	,    A__=False	,    A__="summarization"	,    A__=None	,    **A__	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			Path(A__   ).open("w"	,    encoding="utf-8"   )
   lowerCamelCase__						=			str(A__   )
   lowerCamelCase__						=			AutoModelForSeqaSeqLM.from_pretrained(A__   ).to(A__   )
   if fpaa:
      lowerCamelCase__						=			model.half()

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

   lowerCamelCase__						=			time.time()
   # update config with task specific params
   use_task_specific_params(A__	,    A__   )
   if prefix is None:
      lowerCamelCase__						=			prefix or getattr(model.config	,    "prefix"	,    ""   ) or ""
   for examples_chunk in tqdm(list(chunks(A__	,    A__   )   )   ):
      lowerCamelCase__						=			[prefix + text for text in examples_chunk]
      lowerCamelCase__						=			tokenizer(A__	,    return_tensors="pt"	,    truncation=A__	,    padding="longest"   ).to(A__   )
      lowerCamelCase__						=			model.generate(
          input_ids=batch.input_ids	,    attention_mask=batch.attention_mask	,    **A__	,    )
      lowerCamelCase__						=			tokenizer.batch_decode(A__	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
      for hypothesis in dec:
         fout.write(hypothesis + "\n"   )
         fout.flush()
   fout.close()
   lowerCamelCase__						=			int(time.time() - start_time   )  # seconds
   lowerCamelCase__						=			len(A__   )
   return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs	,    4   )}



def 	UpperCAmelCase__   (				)      ->      Optional[int]:




   """simple docstring"""
   return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"   )



def 	UpperCAmelCase__   (				A__=True   )      ->      List[str]:




   """simple docstring"""
   lowerCamelCase__						=			argparse.ArgumentParser()
   parser.add_argument("model_name"	,    type=A__	,    help="like facebook/bart-large-cnn,t5-base, etc."   )
   parser.add_argument("input_path"	,    type=A__	,    help="like cnn_dm/test.source"   )
   parser.add_argument("save_path"	,    type=A__	,    help="where to save summaries"   )
   parser.add_argument("--reference_path"	,    type=A__	,    required=A__	,    help="like cnn_dm/test.target"   )
   parser.add_argument("--score_path"	,    type=A__	,    required=A__	,    default="metrics.json"	,    help="where to save metrics"   )
   parser.add_argument("--device"	,    type=A__	,    required=A__	,    default=A__	,    help="cuda, cuda:1, cpu etc."   )
   parser.add_argument(
       "--prefix"	,    type=A__	,    required=A__	,    default=A__	,    help="will be added to the begininng of src examples"   )
   parser.add_argument("--task"	,    type=A__	,    default="summarization"	,    help="used for task_specific_params + metrics"   )
   parser.add_argument("--bs"	,    type=A__	,    default=8	,    required=A__	,    help="batch size"   )
   parser.add_argument(
       "--n_obs"	,    type=A__	,    default=-1	,    required=A__	,    help="How many observations. Defaults to all."   )
   parser.add_argument("--fp16"	,    action="store_true"   )
   parser.add_argument("--dump-args"	,    action="store_true"	,    help="print the custom hparams with the results"   )
   parser.add_argument(
       "--info"	,    nargs="?"	,    type=A__	,    const=datetime_now()	,    help=(
           "use in conjunction w/ --dump-args to print with the results whatever other info you'd like, e.g."
           " lang=en-ru. If no value is passed, the current datetime string will be used."
       )	,    )
   # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
   lowerCamelCase__					,       lowerCamelCase__						=			parser.parse_known_args()
   lowerCamelCase__						=			parse_numeric_n_bool_cl_kwargs(A__   )
   if parsed_args and verbose:
      print(f'parsed the following generate kwargs: {parsed_args}'   )
   lowerCamelCase__						=			[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
   if args.n_obs > 0:
      lowerCamelCase__						=			examples[: args.n_obs]
   Path(args.save_path   ).parent.mkdir(exist_ok=A__   )

   if args.reference_path is None and Path(args.score_path   ).exists():
      warnings.warn(f'score_path {args.score_path} will be overwritten unless you type ctrl-c.'   )

   if args.device == "cpu" and args.fpaa:
      # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
      raise ValueError("Can't mix --fp16 and --device cpu"   )

   lowerCamelCase__						=			generate_summaries_or_translations(
       A__	,    args.save_path	,    args.model_name	,    batch_size=args.bs	,    device=args.device	,    fpaa=args.fpaa	,    task=args.task	,    prefix=args.prefix	,    **A__	,    )

   if args.reference_path is None:
      return {}

   # Compute scores
   lowerCamelCase__						=			calculate_bleu if "translation" in args.task else calculate_rouge
   lowerCamelCase__						=			[x.rstrip() for x in open(args.save_path   ).readlines()]
   lowerCamelCase__						=			[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(A__   )]
   lowerCamelCase__						=			score_fn(A__	,    A__   )
   scores.update(A__   )

   if args.dump_args:
      scores.update(A__   )
   if args.info:
      lowerCamelCase__						=			args.info

   if verbose:
      print(A__   )

   if args.score_path is not None:
      json.dump(A__	,    open(args.score_path	,    "w"   )   )

   return scores


if __name__ == "__main__":
     # Usage for MT:
     # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
     run_generate(verbose=True)







"""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


SCREAMING_SNAKE_CASE_     :			int	  =			16
SCREAMING_SNAKE_CASE_     :			int	  =			32



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   return int(x / 2**20   )


class 		_A       :





      def __enter__(  self		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         return self





      def __exit__(  self    ,						*SCREAMING_SNAKE_CASE__		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         lowerCamelCase__						=			bamb(self.end - self.begin		)
         lowerCamelCase__						=			bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCAmelCase__   (				A__	,    A__ = 16	,    A__ = "bert-base-cased"	,    A__ = 320	,    A__ = 160	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			AutoTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			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)
      lowerCamelCase__						=			tokenizer(examples["sentence1"]	,    examples["sentence2"]	,    truncation=A__	,    max_length=A__   )
      return outputs

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

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowerCamelCase__						=			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(A__	,    padding="max_length"	,    max_length=128	,    return_tensors="pt"   )
      return tokenizer.pad(A__	,    padding="longest"	,    return_tensors="pt"   )

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

   return train_dataloader, eval_dataloader



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   # Initialize accelerator
   lowerCamelCase__						=			Accelerator()

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

   set_seed(A__   )
   lowerCamelCase__					,       lowerCamelCase__						=			get_dataloaders(A__	,    A__	,    A__	,    args.n_train	,    args.n_val   )

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

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

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase__						=			accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCamelCase__						=			1
   lowerCamelCase__						=			(len(A__   ) * num_epochs) // gradient_accumulation_steps

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

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

   # Now we train the model
   lowerCamelCase__						=			{}
   for epoch in range(A__	,    A__   ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(A__   ):
            lowerCamelCase__						=			model(**A__   )
            lowerCamelCase__						=			outputs.loss
            lowerCamelCase__						=			loss / gradient_accumulation_steps
            accelerator.backward(A__   )
            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   )   )   )
      lowerCamelCase__						=			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(A__	,    A__   )



def 	UpperCAmelCase__   (				)      ->      Any:




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


if __name__ == "__main__":
     main()




