





'''simple docstring'''

import argparse
import os

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, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################

__lowerCAmelCase							:			Union[str, Any]				=16
__lowerCAmelCase							:			int				=32





def 		UpperCamelCase       (							_lowerCamelCase      :      str		, _lowerCamelCase      :      Optional[int] = 16 ):
    A__       =    AutoTokenizer.from_pretrained("bert-base-cased" )
    A__       =    load_dataset("glue"		, "mrpc" )

    def tokenize_function(_lowerCamelCase      :      int ):
        # max_length=None => use the model max length (it's actually the default)
        A__       =    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():
        A__       =    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
    A__       =    tokenized_datasets.rename_column("label"		, "labels" )

    def collate_fn(_lowerCamelCase      :      List[str] ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        A__       =    1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            A__       =    16
        elif accelerator.mixed_precision != "no":
            A__       =    8
        else:
            A__       =    None

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

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

 __lowerCAmelCase							:			Any				=mocked_dataloaders  # noqa: F811





def 		UpperCamelCase       (							_lowerCamelCase      :      List[str]		, _lowerCamelCase      :      int ):
    # For testing only
    if os.environ.get("TESTING_MOCKED_DATALOADERS"		, _lowerCamelCase ) == "1":
        A__       =    2
    # Initialize accelerator
    A__       =    Accelerator(cpu=args.cpu		, mixed_precision=args.mixed_precision )
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A__       =    config["lr"]
    A__       =    int(config["num_epochs"] )
    A__       =    int(config["seed"] )
    A__       =    int(config["batch_size"] )

    # New Code #
    # Parse out whether we are saving every epoch or after a certain number of batches
    if hasattr(args.checkpointing_steps		, "isdigit" ):
        if args.checkpointing_steps == "epoch":
            A__       =    args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            A__       =    int(args.checkpointing_steps )
        else:
            raise ValueError(
                F"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed." )
    else:
        A__       =    None

    set_seed(_lowerCamelCase )

    A__,			A__       =    get_dataloaders(_lowerCamelCase		, _lowerCamelCase )
    A__       =    evaluate.load("glue"		, "mrpc" )

    # If the batch size is too big we use gradient accumulation
    A__       =    1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        A__       =    batch_size // MAX_GPU_BATCH_SIZE
        A__       =    MAX_GPU_BATCH_SIZE

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A__       =    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).
    A__       =    model.to(accelerator.device )

    # Instantiate optimizer
    A__       =    AdamW(params=model.parameters()		, lr=_lowerCamelCase )

    # Instantiate scheduler
    A__       =    get_linear_schedule_with_warmup(
        optimizer=_lowerCamelCase		, num_warmup_steps=1_00		, num_training_steps=(len(_lowerCamelCase ) * num_epochs) // gradient_accumulation_steps		, )

    # 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.
    A__,			A__,			A__,			A__,			A__       =    accelerator.prepare(
        _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase		, _lowerCamelCase )

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

    # We need to load the checkpoint back in before training here with `load_state`
    # The total number of epochs is adjusted based on where the state is being loaded from,
    # as we assume continuation of the same training script
    if args.resume_from_checkpoint:
        if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
            accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}" )
            accelerator.load_state(args.resume_from_checkpoint )
            A__       =    os.path.basename(args.resume_from_checkpoint )
        else:
            # Get the most recent checkpoint
            A__       =    [f.name for f in os.scandir(os.getcwd() ) if f.is_dir()]
            dirs.sort(key=os.path.getctime )
            A__       =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        A__       =    os.path.splitext(_lowerCamelCase )[0]

        if "epoch" in training_difference:
            A__       =    int(training_difference.replace("epoch_"		, "" ) ) + 1
            A__       =    None
        else:
            A__       =    int(training_difference.replace("step_"		, "" ) )
            A__       =    resume_step // len(_lowerCamelCase )
            resume_step -= starting_epoch * len(_lowerCamelCase )

    # Now we train the model
    for epoch in range(_lowerCamelCase		, _lowerCamelCase ):
        model.train()
        # New Code #
        if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
            # We need to skip steps until we reach the resumed step
            A__       =    accelerator.skip_first_batches(_lowerCamelCase		, _lowerCamelCase )
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            A__       =    train_dataloader
        for step, batch in enumerate(_lowerCamelCase ):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device )
            A__       =    model(**_lowerCamelCase )
            A__       =    outputs.loss
            A__       =    loss / gradient_accumulation_steps
            accelerator.backward(_lowerCamelCase )
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()
            # New Code #
            overall_step += 1

            # New Code #
            # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
            # These are saved to folders named `step_{overall_step}`
            # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
            # If mixed precision was used, will also save a "scalar.bin" file
            if isinstance(_lowerCamelCase		, _lowerCamelCase ):
                A__       =    F"step_{overall_step}"
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        A__       =    os.path.join(args.output_dir		, _lowerCamelCase )
                    accelerator.save_state(_lowerCamelCase )

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

        A__       =    metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(F"epoch {epoch}:"		, _lowerCamelCase )

        # New Code #
        # We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
        # These are saved to folders named `epoch_{epoch}`
        # Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
        # If mixed precision was used, will also save a "scalar.bin" file
        if checkpointing_steps == "epoch":
            A__       =    F"epoch_{epoch}"
            if args.output_dir is not None:
                A__       =    os.path.join(args.output_dir		, _lowerCamelCase )
            accelerator.save_state(_lowerCamelCase )





def 		UpperCamelCase       (							):
    A__       =    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." )
    parser.add_argument(
        "--checkpointing_steps"		, type=_lowerCamelCase		, default=_lowerCamelCase		, help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		, )
    parser.add_argument(
        "--output_dir"		, type=_lowerCamelCase		, default="."		, help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		, )
    parser.add_argument(
        "--resume_from_checkpoint"		, type=_lowerCamelCase		, default=_lowerCamelCase		, help="If the training should continue from a checkpoint folder."		, )
    A__       =    parser.parse_args()
    A__       =    {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(_lowerCamelCase		, _lowerCamelCase )


if __name__ == "__main__":
 main()










'''simple docstring'''

import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
  import torch


__lowerCAmelCase							:			Optional[int]				=logging.get_logger(__name__)






class      UpperCAmelCase			:
 def __init__(       self   :Optional[int]		, lowercase_   :Any		, lowercase_   :Any=21		, lowercase_   :Any=[]		, lowercase_   :Dict=None							)->							Tuple:
     A__       =    model
     A__       =    trace_batch_nums
     A__       =    abort_after_batch_num

     # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
     A__       =    collections.deque([]		, lowercase_							)
     A__       =    []
     A__       =    0
     A__       =    0
     A__       =    False
     A__       =    "                 "

     self.analyse_model()

     self.register_forward_hook()
 def   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any=None							)->							Optional[Any]:
     if frame is not None:
         self.expand_frame(lowercase_							)
     self.frames.append("\n".join(self.frame							)							)
     A__       =    []  # start a new frame
 def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :Dict							)->							List[str]:
     self.frame.append(lowercase_							)
 def   UpperCAmelCase_							(       self   :int							)->							Dict:
     print("\n".join(self.frames							)							)
     A__       =    []
 def   UpperCAmelCase_							(       self   :str							)->							Union[str, Any]:
     A__       =    []
 def   UpperCAmelCase_							(       self   :List[str]							)->							Optional[Any]:
     print(F"\nDetected inf/nan during batch_number={self.batch_number}"							)
     print(F"Last {len(self.frames							)} forward frames:"							)
     print(F"{'abs min':8} {'abs max':8} metadata"							)
     print("\n".join(self.frames							)							)
     print("\n\n"							)
     A__       =    []
 def   UpperCAmelCase_							(       self   :Any							)->							Optional[Any]:
     # extract the fully qualified module names, to be able to report at run time. e.g.:
     # encoder.block.2.layer.0.SelfAttention.o
     #
     # for shared weights only the first shared module name will be registered
     A__       =    {m: name for name, m in self.model.named_modules()}
     # self.longest_module_name = max(len(v) for v in self.module_names.values())
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Dict		, lowercase_   :Any							)->							str:
     if torch.is_tensor(lowercase_							):
         self.expand_frame(get_abs_min_max(lowercase_		, lowercase_							)							)
         if detect_overflow(lowercase_		, lowercase_							):
             A__       =    True
     elif var is None:
         self.expand_frame(F"{'None':>17} {ctx}"							)
     else:
         self.expand_frame(F"{'not a tensor':>17} {ctx}"							)
 def   UpperCAmelCase_							(       self   :Union[str, Any]							)->							str:
     self.expand_frame(F"\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"							)
     self.expand_frame(F"{'abs min':8} {'abs max':8} metadata"							)
 def   UpperCAmelCase_							(       self   :int							)->							int:
     self.expand_frame(F"{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"							)
 def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :Dict		, lowercase_   :Dict		, lowercase_   :str							)->							int:
     self.expand_frame(F"{self.prefix} {self.module_names[module]} {module.__class__.__name__}"							)

     # params
     for name, p in module.named_parameters(recurse=lowercase_							):
         self.analyse_variable(lowercase_		, lowercase_							)

     # inputs
     if isinstance(lowercase_		, lowercase_							):
         for i, x in enumerate(lowercase_							):
             self.analyse_variable(lowercase_		, F"input[{i}]"							)
     else:
         self.analyse_variable(lowercase_		, "input"							)

     # outputs
     if isinstance(lowercase_		, lowercase_							):
         for i, x in enumerate(lowercase_							):
             # possibly a tuple of tuples
             if isinstance(lowercase_		, lowercase_							):
                 for j, y in enumerate(lowercase_							):
                     self.analyse_variable(lowercase_		, F"output[{i}][{j}]"							)
             else:
                 self.analyse_variable(lowercase_		, F"output[{i}]"							)
     else:
         self.analyse_variable(lowercase_		, "output"							)

     self.save_frame()
 def   UpperCAmelCase_							(       self   :Any							)->							int:
     self.model.apply(self._register_forward_hook							)
 def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Optional[int]							)->							Tuple:
     module.register_forward_hook(self.forward_hook							)



 def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :int		, lowercase_   :Optional[Any]		, lowercase_   :Dict							)->							Union[str, Any]:
     # - input is a tuple of packed inputs (could be non-Tensors)
     # - output could be a Tensor or a tuple of Tensors and non-Tensors

     A__       =    False

     A__       =    True if self.batch_number in self.trace_batch_nums else False
     if trace_mode:
         self.reset_saved_frames()

     if self.total_calls == 0:
         self.batch_start_frame()
     self.total_calls += 1

     # count batch numbers - the very first forward hook of the batch will be called when the
     # batch completes - i.e. it gets called very last - we know this batch has finished
     if module == self.model:
         self.batch_number += 1
         A__       =    True

     self.create_frame(lowercase_		, lowercase_		, lowercase_							)

     # if last_frame_of_batch:
     #     self.batch_end_frame()

     if trace_mode:
         self.trace_frames()

     if last_frame_of_batch:
         self.batch_start_frame()

     if self.detected_overflow and not trace_mode:
         self.dump_saved_frames()

         # now we can abort, as it's pointless to continue running
         raise ValueError(
             "DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. "
             "Please scroll up above this traceback to see the activation values prior to this event."							)

     # abort after certain batch if requested to do so
     if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
         raise ValueError(
             F"DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"
             F" `abort_after_batch_num={self.abort_after_batch_num}` arg"							)





def 		UpperCamelCase       (							_lowerCamelCase      :      Dict		, _lowerCamelCase      :      int ):
    A__       =    var.abs()
    return F"{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"





def 		UpperCamelCase       (							_lowerCamelCase      :      Tuple		, _lowerCamelCase      :      Optional[int] ):
    A__       =    False
    if torch.isnan(_lowerCamelCase ).any().item():
        A__       =    True
        print(F"{ctx} has nans" )
    if torch.isinf(_lowerCamelCase ).any().item():
        A__       =    True
        print(F"{ctx} has infs" )

    # if needed to monitor large elements can enable the following
    if 0:  # and detected:
        A__       =    var[torch.ge(var.abs()		, 1_00 )]
        if naaa.numel() > 0:
            print(F"{ctx}:  n100={naaa.numel()}" )
        A__       =    var[torch.ge(var.abs()		, 10_00 )]
        if naaaa.numel() > 0:
            print(F"{ctx}: n1000={naaaa.numel()}" )
        A__       =    var[torch.ge(var.abs()		, 1_00_00 )]
        if naaaaa.numel() > 0:
            print(F"{ctx}: n10000={naaaaa.numel()}" )

    if 0:
        print(F"min={var.min():9.2e} max={var.max():9.2e}" )

    if 0:
        print(F"min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})" )

    return detected






class      UpperCAmelCase			( UpperCamelCase__       ):
 __lowercase     	=       """underflow_overflow"""
 __lowercase     	=       """tpu_metrics_debug"""



