


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      :    Dict		=					16
A      :    Optional[Any]		=					32

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      return int(x / 2**20						)







class 			_lowercase :


       """simple docstring"""




       def __enter__(   self							:			Optional[int]       ):


             '''simple docstring'''



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





       def __exit__(   self							:			List[Any]					,      *__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



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



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

      """simple docstring"""




      lowerCamelCase__		:				List[str]    =  AutoTokenizer.from_pretrained(_A						)
      lowerCamelCase__		:				Tuple    =  load_dataset(
          "glue"    ,					"mrpc"    ,					split={"train": F"train[:{n_train}]", "validation": F"validation[:{n_val}]"}						)

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

      def collate_fn(_A			:    int						):
            # 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__		:				List[Any]    =  DataLoader(
          tokenized_datasets["train"]    ,					shuffle=_A    ,					collate_fn=_A    ,					batch_size=_A						)
      lowerCamelCase__		:				int    =  DataLoader(
          tokenized_datasets["validation"]    ,					shuffle=_A    ,					collate_fn=_A    ,					batch_size=_A						)

      return train_dataloader, eval_dataloader

def       lowercase_		(		_A			:    List[Any]    ,					_A			:    Dict						):

      """simple docstring"""




      lowerCamelCase__		:				Union[str, Any]    =  Accelerator()

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

      set_seed(_A						)
      lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  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__		:				Union[str, Any]    =  AutoModelForSequenceClassification.from_pretrained(_A    ,					return_dict=_A						)

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

      if accelerator.state.deepspeed_plugin is not None:
            lowerCamelCase__		:				Optional[int]    =  accelerator.state.deepspeed_plugin.deepspeed_config[
                "gradient_accumulation_steps"
            ]
      else:
            lowerCamelCase__		:				str    =  1
      lowerCamelCase__		:				List[str]    =  (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__		:				Optional[int]    =  get_linear_schedule_with_warmup(
                optimizer=_A    ,					num_warmup_steps=0    ,					num_training_steps=_A    ,					)
      else:
            lowerCamelCase__		:				Optional[int]    =  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__		:				List[Any]    =  accelerator.prepare(
          _A    ,					_A    ,					_A    ,					_A    ,					_A						)

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

      # Now we train the model
      lowerCamelCase__		:				Optional[int]    =  {}
      for epoch in range(_A    ,					_A						):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(_A						):
                        lowerCamelCase__		:				Tuple    =  model(**_A						)
                        lowerCamelCase__		:				Union[str, Any]    =  outputs.loss
                        lowerCamelCase__		:				int    =  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__		:				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(_A    ,					_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  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__		:				Union[str, Any]    =  parser.parse_args()
      lowerCamelCase__		:				Optional[int]    =  {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
      training_function(_A    ,					_A						)


if __name__ == "__main__":
    main()






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A      :    Union[str, Any]		=					logging.get_logger(__name__)

A      :    Optional[Any]		=					{
    "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/config.json",
    "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/config.json",
    "xlm-roberta-large-finetuned-conll02-dutch": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll02-spanish": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-english": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"
    ),
    "xlm-roberta-large-finetuned-conll03-german": (
        "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"
    ),
}







class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			"xlm-roberta"





       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Optional[Any]=30522					,      __lowerCamelCase							:			List[Any]=768					,      __lowerCamelCase							:			Union[str, Any]=12					,      __lowerCamelCase							:			int=12					,      __lowerCamelCase							:			Dict=3072					,      __lowerCamelCase							:			Optional[int]="gelu"					,      __lowerCamelCase							:			Optional[Any]=0.1					,      __lowerCamelCase							:			Tuple=0.1					,      __lowerCamelCase							:			str=512					,      __lowerCamelCase							:			Optional[Any]=2					,      __lowerCamelCase							:			Dict=0.0_2					,      __lowerCamelCase							:			List[str]=1E-1_2					,      __lowerCamelCase							:			Optional[int]=1					,      __lowerCamelCase							:			Union[str, Any]=0					,      __lowerCamelCase							:			Optional[int]=2					,      __lowerCamelCase							:			Dict="absolute"					,      __lowerCamelCase							:			Optional[Any]=True					,      __lowerCamelCase							:			Tuple=None					,      **__lowerCamelCase							:			int					,      ):


             '''simple docstring'''



             super().__init__(pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      **__lowerCamelCase       )

             lowerCamelCase__		:				str    =  vocab_size
             lowerCamelCase__		:				Any    =  hidden_size
             lowerCamelCase__		:				Optional[Any]    =  num_hidden_layers
             lowerCamelCase__		:				Union[str, Any]    =  num_attention_heads
             lowerCamelCase__		:				Any    =  hidden_act
             lowerCamelCase__		:				str    =  intermediate_size
             lowerCamelCase__		:				Optional[int]    =  hidden_dropout_prob
             lowerCamelCase__		:				Any    =  attention_probs_dropout_prob
             lowerCamelCase__		:				Union[str, Any]    =  max_position_embeddings
             lowerCamelCase__		:				List[str]    =  type_vocab_size
             lowerCamelCase__		:				Tuple    =  initializer_range
             lowerCamelCase__		:				Tuple    =  layer_norm_eps
             lowerCamelCase__		:				Any    =  position_embedding_type
             lowerCamelCase__		:				Any    =  use_cache
             lowerCamelCase__		:				Any    =  classifier_dropout







class 			_lowercase (							lowercase__):


       """simple docstring"""




       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             if self.task == "multiple-choice":
                   lowerCamelCase__		:				List[str]    =  {0: "batch", 1: "choice", 2: "sequence"}
             else:
                   lowerCamelCase__		:				Any    =  {0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                 ]       )


