



'''simple docstring'''







import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
      import torch

      from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
      import pyanvml.pyanvml as nvml


lowercase__  :	Any            =      logging.get_logger(__name__)

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  PyTorchBenchmarkArguments
   A			:  PretrainedConfig
   A			:  str					=						"PyTorch"


   @property
   def 						_UpperCamelCase							(	self			):
       return torch.__version__


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self._prepare_inference_func(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
       return self._measure_speed(_inference			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self._prepare_inference_func(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
       return self._measure_memory(_inference			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self._prepare_train_func(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
       return self._measure_speed(_train			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self._prepare_train_func(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			)
       return self._measure_memory(_train			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.config_dict[model_name]

       if self.args.torchscript:
           UpperCamelCase__	     =      True

       UpperCamelCase__	     =      (
           hasattr(__UpperCamelCase  ,    "architectures"			)
           and isinstance(config.architectures  ,    __UpperCamelCase			)
           and len(config.architectures			) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
           try:
               UpperCamelCase__	     =      config.architectures[0]
               UpperCamelCase__	     =      __import__("transformers"  ,    fromlist=[model_class]			)
               UpperCamelCase__	     =      getattr(__UpperCamelCase  ,    __UpperCamelCase			)
               UpperCamelCase__	     =      model_cls(__UpperCamelCase			)
           except ImportError:
               raise ImportError(
                   f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                   " set `--only_pretrain_model` or `args.only_pretrain_model=True`."			)
       else:
           UpperCamelCase__	     =      MODEL_MAPPING[config.__class__](__UpperCamelCase			)

       model.eval()
       model.to(self.args.device			)

       # encoder-decoder has vocab size saved differently
       UpperCamelCase__	     =      config.vocab_size if hasattr(__UpperCamelCase  ,    "vocab_size"			) else config.encoder.vocab_size
       UpperCamelCase__	     =      torch.randint(__UpperCamelCase  ,    (batch_size, sequence_length)  ,    dtype=torch.long  ,    device=self.args.device			)

       if self.args.fpaa:
           logger.info("Running training in Mixed Precision..."			)
           if not self.args.is_gpu:
               raise ValueError("Mixed precision is possible only for GPU."			)
           # amp seems to have memory leaks so that memory usage
           # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
           model.half()

       if self.args.torchscript:
           with torch.no_grad():
               UpperCamelCase__	     =      torch.jit.trace(__UpperCamelCase  ,    __UpperCamelCase			)
       else:
           UpperCamelCase__	     =      model

       def encoder_decoder_forward():
           with torch.no_grad():
               UpperCamelCase__	     =      inference_model(__UpperCamelCase  ,    decoder_input_ids=__UpperCamelCase			)
           return outputs

       def encoder_forward():
           with torch.no_grad():
               UpperCamelCase__	     =      inference_model(__UpperCamelCase			)
           return outputs

       UpperCamelCase__	     =      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
       return _forward


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.config_dict[model_name]

       UpperCamelCase__	     =      (
           hasattr(__UpperCamelCase  ,    "architectures"			)
           and isinstance(config.architectures  ,    __UpperCamelCase			)
           and len(config.architectures			) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
           try:
               UpperCamelCase__	     =      config.architectures[0]
               UpperCamelCase__	     =      __import__("transformers"  ,    fromlist=[model_class]			)
               UpperCamelCase__	     =      getattr(__UpperCamelCase  ,    __UpperCamelCase			)
               UpperCamelCase__	     =      model_cls(__UpperCamelCase			)
           except ImportError:
               raise ImportError(
                   f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                   " set `--only_pretrain_model` or `args.only_pretrain_model=True`."			)
       else:
           UpperCamelCase__	     =      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__UpperCamelCase			)

       if self.args.torchscript:
           raise NotImplementedError("Training for torchscript is currently not implemented"			)
       else:
           UpperCamelCase__	     =      model

       model.train()
       model.to(self.args.device			)

       # encoder-decoder has vocab size saved differently
       UpperCamelCase__	     =      config.vocab_size if hasattr(__UpperCamelCase  ,    "vocab_size"			) else config.encoder.vocab_size
       UpperCamelCase__	     =      torch.randint(__UpperCamelCase  ,    (batch_size, sequence_length)  ,    dtype=torch.long  ,    device=self.args.device			)

       if self.args.fpaa:
           logger.info("Running training in Mixed Precision..."			)
           if not self.args.is_gpu:
               raise ValueError("Mixed precision is possible only for GPU."			)

           # amp seems to have memory leaks so that memory usage
           # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
           model.half()

       def compute_loss_and_backprob_encoder():
           UpperCamelCase__	     =      train_model(__UpperCamelCase  ,    labels=__UpperCamelCase			)[0]
           loss.backward()
           return loss

       def compute_loss_and_backprob_encoder_decoder():
           UpperCamelCase__	     =      train_model(__UpperCamelCase  ,    decoder_input_ids=__UpperCamelCase  ,    labels=__UpperCamelCase			)[0]
           loss.backward()
           return loss

       UpperCamelCase__	     =      (
           compute_loss_and_backprob_encoder_decoder
           if config.is_encoder_decoder
           else compute_loss_and_backprob_encoder
       )
       return _train


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       try:
           if self.args.is_tpu or self.args.torchscript:
               # run additional 10 times to stabilize compilation for tpu and torchscript
               logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"			)
               timeit.repeat(
                   __UpperCamelCase  ,    repeat=1  ,    number=5  ,    )

           # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
           UpperCamelCase__	     =      timeit.repeat(
               __UpperCamelCase  ,    repeat=self.args.repeat  ,    number=10  ,    )

           if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
               import torch_xla.debug.metrics as met

               self.print_fn(met.metrics_report()			)

           return min(__UpperCamelCase			) / 10.0
       except RuntimeError as e:
           self.print_fn(f'Doesn\'t fit on GPU. {e}'			)
           return "N/A"







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase			):
       try:
           if self.args.trace_memory_line_by_line:
               UpperCamelCase__	     =      start_memory_tracing("transformers"			)

           if self.args.is_tpu:
               # tpu
               raise NotImplementedError(
                   "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
                   " `--no-memory` or `args.memory=False`"			)
           elif self.args.is_gpu:
               if not is_pyanvml_available():
                   logger.warning(
                       "py3nvml not installed, we won't log GPU memory usage. "
                       "Install py3nvml (pip install py3nvml) to log information about GPU."			)
                   UpperCamelCase__	     =      "N/A"
               else:
                   logger.info(
                       "Measuring total GPU usage on GPU device. Make sure to not have additional processes running"
                       " on the same GPU."			)
                   # init nvml
                   nvml.nvmlInit()
                   func()
                   UpperCamelCase__	     =      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx			)
                   UpperCamelCase__	     =      nvml.nvmlDeviceGetMemoryInfo(__UpperCamelCase			)
                   UpperCamelCase__	     =      meminfo.used
                   UpperCamelCase__	     =      Memory(__UpperCamelCase			)
                   # shutdown nvml
                   nvml.nvmlShutdown()
           else:
               # cpu
               UpperCamelCase__	     =      measure_peak_memory_cpu(__UpperCamelCase			)
               UpperCamelCase__	     =      Memory(__UpperCamelCase			) if isinstance(__UpperCamelCase  ,    __UpperCamelCase			) else memory_bytes

           if self.args.trace_memory_line_by_line:
               UpperCamelCase__	     =      stop_memory_tracing(__UpperCamelCase			)
           else:
               UpperCamelCase__	     =      None

           return memory, summary
       except RuntimeError as e:
           self.print_fn(f'Doesn\'t fit on GPU. {e}'			)
           return "N/A", None









'''simple docstring'''







import argparse
import os

# New Code #
import evaluate
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

lowercase__  :	Optional[Any]            =      32





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_ = 16						):
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained("bert-base-cased"						)
    UpperCamelCase__	     =      load_dataset("glue"       ,	"mrpc"						)

    def tokenize_function(lowerCAmelCase_						):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase__	     =      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
    # starting with the main process first:
    with accelerator.main_process_first():
        UpperCamelCase__	     =      datasets.map(
            lowerCAmelCase_       ,	batched=lowerCAmelCase_       ,	remove_columns=["idx", "sentence1", "sentence2"]       ,	)

    # 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(lowerCAmelCase_						):
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            UpperCamelCase__	     =      16
        elif accelerator.mixed_precision != "no":
            UpperCamelCase__	     =      8
        else:
            UpperCamelCase__	     =      None

        return tokenizer.pad(
            lowerCAmelCase_       ,	padding="longest"       ,	pad_to_multiple_of=lowerCAmelCase_       ,	return_tensors="pt"       ,	)

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

    return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
      from accelerate.test_utils.training import mocked_dataloaders

      lowercase__  :	Dict            =      mocked_dataloaders  # noqa: F811





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,	lowerCAmelCase_						) == "1":
        UpperCamelCase__	     =      2
    # Initialize accelerator
    UpperCamelCase__	     =      Accelerator(cpu=args.cpu       ,	mixed_precision=args.mixed_precision						)
    # 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__	     =      evaluate.load("glue"       ,	"mrpc"						)

    # New Code #
    # We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
    # to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
    # half each time. From this, we can calculate the number of gradient accumulation steps needed
    # and modify the Accelerator object as a result
    @find_executable_batch_size(starting_batch_size=int(lowerCAmelCase_						)						)
    def inner_training_loop(lowerCAmelCase_						):
        # Since we need to modify the outside accelerator object, we need to bring it
        # to the local scope
        nonlocal accelerator

        # We can calculate the number of gradient accumulation steps based on the current
        # batch size vs the starting batch size
        UpperCamelCase__	     =      observed_batch_size // batch_size

        # And then set it in the Accelerator directly:
        UpperCamelCase__	     =      num_gradient_accumulation_steps

        # Next we need to free all of the stored model references in the Accelerator each time
        accelerator.free_memory()

        # And set the seed so our results are reproducable each reset
        set_seed(lowerCAmelCase_						)

        # Instantiate the model (we build the model here so that the seed also control new weights initialization)
        UpperCamelCase__	     =      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,	return_dict=lowerCAmelCase_						)

        # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
        # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
        # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
        UpperCamelCase__	     =      model.to(accelerator.device						)

        # Instantiate optimizer
        UpperCamelCase__	     =      AdamW(params=model.parameters()       ,	lr=lowerCAmelCase_						)
        UpperCamelCase__     ,	UpperCamelCase__	     =      get_dataloaders(lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Instantiate scheduler
        UpperCamelCase__	     =      get_linear_schedule_with_warmup(
            optimizer=lowerCAmelCase_       ,	num_warmup_steps=1_00       ,	num_training_steps=(len(lowerCAmelCase_						) * num_epochs)       ,	)

        # 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(
            lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)

        # Now we train the model
        for epoch in range(lowerCAmelCase_						):
            model.train()
            for step, batch in enumerate(lowerCAmelCase_						):
                # And perform gradient accumulation
                with accelerator.accumulate(lowerCAmelCase_						):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device						)
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                    UpperCamelCase__	     =      outputs.loss
                    accelerator.backward(lowerCAmelCase_						)
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

            model.eval()
            for step, batch in enumerate(lowerCAmelCase_						):
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                batch.to(accelerator.device						)
                with torch.no_grad():
                    UpperCamelCase__	     =      model(**lowerCAmelCase_						)
                UpperCamelCase__	     =      outputs.logits.argmax(dim=-1						)
                UpperCamelCase__     ,	UpperCamelCase__	     =      accelerator.gather_for_metrics((predictions, batch["labels"])						)
                metric.add_batch(
                    predictions=lowerCAmelCase_       ,	references=lowerCAmelCase_       ,	)

            UpperCamelCase__	     =      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(F'epoch {epoch}:'       ,	lowerCAmelCase_						)

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
    inner_training_loop()





def     snake_case__			():
    UpperCamelCase__	     =      argparse.ArgumentParser(description="Simple example of training script."						)
    parser.add_argument(
        "--mixed_precision"       ,	type=lowerCAmelCase_       ,	default=lowerCAmelCase_       ,	choices=["no", "fp16", "bf16", "fp8"]       ,	help="Whether to use mixed precision. Choose"
        "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
        "and an Nvidia Ampere GPU."       ,	)
    parser.add_argument("--cpu"       ,	action="store_true"       ,	help="If passed, will train on the CPU."						)
    UpperCamelCase__	     =      parser.parse_args()
    # New Code #
    # We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
    UpperCamelCase__	     =      {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 2_56}
    training_function(lowerCAmelCase_       ,	lowerCAmelCase_						)


if __name__ == "__main__":
      main()




