import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCamelCase__    							= logging.get_logger(__name__)





@dataclass
class 					A							:
   __UpperCAmelCase			:			str              =      field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys()					)}					)
   __UpperCAmelCase			:			str              =      field(
       metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
   __UpperCAmelCase			:			int              =      field(
       default=1_28		,    metadata={
           'help': (
               'The maximum total input sequence length after tokenization. Sequences longer '
               'than this will be truncated, sequences shorter will be padded.'
           )
       }		,    )
   __UpperCAmelCase			:			bool              =      field(
       default=UpperCAmelCase_		,    metadata={'help': 'Overwrite the cached training and evaluation sets'}					)


   def       lowercase_				(self					:						Optional[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.task_name.lower()





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'train'
   __UpperCAmelCase			:			Union[str, Any]              =      'dev'
   __UpperCAmelCase			:			Optional[Any]              =      'test'





class 					A							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			GlueDataTrainingArguments
   __UpperCAmelCase			:			str
   __UpperCAmelCase			:			List[InputFeatures]


   def __init__(self					:						Dict	,  __UpperCAmelCase					:						GlueDataTrainingArguments	,  __UpperCAmelCase					:						PreTrainedTokenizerBase	,  __UpperCAmelCase					:						Optional[int] = None	,  __UpperCAmelCase					:						Union[str, Split] = Split.train	,  __UpperCAmelCase					:						Optional[str] = None	,  )						->    Optional[Any]:
         """simple docstring"""



         warnings.warn(
             "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
             "library. You can have a look at this example script for pointers: "
             "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"	,  __UpperCAmelCase	,  )
         UpperCAmelCase__								=  args
         UpperCAmelCase__								=  glue_processors[args.task_name]()
         UpperCAmelCase__								=  glue_output_modes[args.task_name]
         if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		):
               try:
                     UpperCAmelCase__								=  Split[mode]
               except KeyError:
                     raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
         UpperCAmelCase__								=  os.path.join(
             cache_dir if cache_dir is not None else args.data_dir	,  f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""	,  )
         UpperCAmelCase__								=  self.processor.get_labels()
         if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
             "RobertaTokenizer",
             "RobertaTokenizerFast",
             "XLMRobertaTokenizer",
             "BartTokenizer",
             "BartTokenizerFast",
         ):
               # HACK(label indices are swapped in RoBERTa pretrained model)
               UpperCAmelCase__		,				UpperCAmelCase__								=  label_list[2], label_list[1]
         UpperCAmelCase__								=  label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         UpperCAmelCase__								=  cached_features_file + ".lock"
         with FileLock(__UpperCAmelCase		):
               if os.path.exists(__UpperCAmelCase		) and not args.overwrite_cache:
                     UpperCAmelCase__								=  time.time()
                     UpperCAmelCase__								=  torch.load(__UpperCAmelCase		)
                     logger.info(
                         f"""Loading features from cached file {cached_features_file} [took %.3f s]"""	,  time.time() - start		)
               else:
                     logger.info(f"""Creating features from dataset file at {args.data_dir}"""		)

                     if mode == Split.dev:
                           UpperCAmelCase__								=  self.processor.get_dev_examples(args.data_dir		)
                     elif mode == Split.test:
                           UpperCAmelCase__								=  self.processor.get_test_examples(args.data_dir		)
                     else:
                           UpperCAmelCase__								=  self.processor.get_train_examples(args.data_dir		)
                     if limit_length is not None:
                           UpperCAmelCase__								=  examples[:limit_length]
                     UpperCAmelCase__								=  glue_convert_examples_to_features(
                         __UpperCAmelCase	,  __UpperCAmelCase	,  max_length=args.max_seq_length	,  label_list=__UpperCAmelCase	,  output_mode=self.output_mode	,  )
                     UpperCAmelCase__								=  time.time()
                     torch.save(self.features	,  __UpperCAmelCase		)
                     # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                     logger.info(
                         f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)


   def __len__(self					:						Any		)						->    Any:
         """simple docstring"""



         return len(self.features		)


   def __getitem__(self					:						int	,  __UpperCAmelCase					:						List[Any]		)						->    InputFeatures:
         """simple docstring"""



         return self.features[i]





   def       lowercase_				(self					:						Tuple		)						->    Any:
         """simple docstring"""



         return self.label_list




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__    							= 1_6
UpperCamelCase__    							= 3_2




def        lowerCAmelCase_			(					__A							)					->    Union[str, Any]:







      '''simple docstring'''


      return int(x / 2**20							)





class 					A							:


   def __enter__(self					:						Dict		)						->    int:
         """simple docstring"""



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





   def __exit__(self					:						List[str]	,  *__UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         UpperCAmelCase__								=  torch.cuda.memory_allocated()
         UpperCAmelCase__								=  torch.cuda.max_memory_allocated()
         UpperCAmelCase__								=  bamb(self.end - self.begin		)
         UpperCAmelCase__								=  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,  )					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  AutoTokenizer.from_pretrained(__A							)
      UpperCAmelCase__								=  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)
            UpperCAmelCase__								=  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
      UpperCAmelCase__								=  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
      UpperCAmelCase__								=  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.
      UpperCAmelCase__								=  DataLoader(
          tokenized_datasets["train"],  shuffle=__A,  collate_fn=__A,  batch_size=__A							)
      UpperCAmelCase__								=  DataLoader(
          tokenized_datasets["validation"],  shuffle=__A,  collate_fn=__A,  batch_size=__A							)

      return train_dataloader, eval_dataloader




def        lowerCAmelCase_			(					__A,  __A							)					->    Dict:







      '''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(__A							)
      UpperCAmelCase__		,				UpperCAmelCase__								=  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)
      UpperCAmelCase__								=  AutoModelForSequenceClassification.from_pretrained(__A,  return_dict=__A							)

      # 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=__A							)

      if accelerator.state.deepspeed_plugin is not None:
            UpperCAmelCase__								=  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            UpperCAmelCase__								=  1
      UpperCAmelCase__								=  (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
      ):
            UpperCAmelCase__								=  get_linear_schedule_with_warmup(
                optimizer=__A,  num_warmup_steps=0,  num_training_steps=__A,  )
      else:
            UpperCAmelCase__								=  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.
      UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  accelerator.prepare(
          __A,  __A,  __A,  __A,  __A							)

      # 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__								=  {}
      for epoch in range(__A,  __A							):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(__A							):
                        UpperCAmelCase__								=  model(**__A							)
                        UpperCAmelCase__								=  outputs.loss
                        UpperCAmelCase__								=  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							)							)							)
            UpperCAmelCase__								=  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        lowerCAmelCase_			(					)					->    Any:







      '''simple docstring'''


      UpperCAmelCase__								=  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.",  )
      UpperCAmelCase__								=  parser.parse_args()
      UpperCAmelCase__								=  {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(__A,  __A							)


if __name__ == "__main__":
      main()



