



'''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
#
########################################################################

lowercase__     :  Any    					=			16
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_	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        UpperCamelCase__      =			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":
            UpperCamelCase__      =			16
        elif accelerator.mixed_precision != "no":
            UpperCamelCase__      =			8
        else:
            UpperCamelCase__      =			None

        return tokenizer.pad(
            lowerCAmelCase_					,  padding="longest"					,  max_length=lowerCAmelCase_					,  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"]	)

    # 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":
            UpperCamelCase__      =			args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            UpperCamelCase__      =			int(args.checkpointing_steps	)
        else:
            raise ValueError(
                F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'	)
    else:
        UpperCamelCase__      =			None

    set_seed(lowerCAmelCase_	)

    UpperCamelCase__    ,  UpperCamelCase__      =			get_dataloaders(lowerCAmelCase_					,  lowerCAmelCase_	)
    UpperCamelCase__      =			evaluate.load("glue"					,  "mrpc"	)

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

    # 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_	)

    # Instantiate scheduler
    UpperCamelCase__      =			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.
    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			accelerator.prepare(
        lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

    # New Code #
    # 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

    # 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	)
            UpperCamelCase__      =			os.path.basename(args.resume_from_checkpoint	)
        else:
            # Get the most recent checkpoint
            UpperCamelCase__      =			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
            dirs.sort(key=os.path.getctime	)
            UpperCamelCase__      =			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        UpperCamelCase__      =			os.path.splitext(lowerCAmelCase_	)[0]

        if "epoch" in training_difference:
            UpperCamelCase__      =			int(training_difference.replace("epoch_"					,  ""	)	) + 1
            UpperCamelCase__      =			None
        else:
            UpperCamelCase__      =			int(training_difference.replace("step_"					,  ""	)	)
            UpperCamelCase__      =			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
            UpperCamelCase__      =			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
            UpperCamelCase__      =			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	)
            UpperCamelCase__      =			model(**lowerCAmelCase_	)
            UpperCamelCase__      =			outputs.loss
            UpperCamelCase__      =			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_	):
                UpperCamelCase__      =			F'step_{overall_step}'
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        UpperCamelCase__      =			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():
                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 #
        # 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":
            UpperCamelCase__      =			F'epoch_{epoch}'
            if args.output_dir is not None:
                UpperCamelCase__      =			os.path.join(args.output_dir					,  lowerCAmelCase_	)
            accelerator.save_state(lowerCAmelCase_	)





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."	)
    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."					,  )
    UpperCamelCase__      =			parser.parse_args()
    UpperCamelCase__      =			{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
    training_function(lowerCAmelCase_					,  lowerCAmelCase_	)


if __name__ == "__main__":
      main()









'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       lowerCamelCase_							(	lowerCAmelCase_  ):






   '''simple docstring'''

   A :     UNetaDModel
   A :     PNDMScheduler


   def __init__(  self     ,						__UpperCamelCase     ,						__UpperCamelCase ):
       super().__init__()

       UpperCamelCase__      =			PNDMScheduler.from_config(scheduler.config )

       self.register_modules(unet=__UpperCamelCase     ,						scheduler=__UpperCamelCase )







   @torch.no_grad()
   def __call__(  self     ,						__UpperCamelCase = 1     ,						__UpperCamelCase = 50     ,						__UpperCamelCase = None     ,						__UpperCamelCase = "pil"     ,						__UpperCamelCase = True     ,						**__UpperCamelCase     ,						):
       UpperCamelCase__      =			randn_tensor(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,						generator=__UpperCamelCase     ,						device=self.device     ,						)

       self.scheduler.set_timesteps(__UpperCamelCase )
       for t in self.progress_bar(self.scheduler.timesteps ):
           UpperCamelCase__      =			self.unet(__UpperCamelCase     ,						__UpperCamelCase ).sample

           UpperCamelCase__      =			self.scheduler.step(__UpperCamelCase     ,						__UpperCamelCase     ,						__UpperCamelCase ).prev_sample

       UpperCamelCase__      =			(image / 2 + 0.5).clamp(0     ,						1 )
       UpperCamelCase__      =			image.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       if output_type == "pil":
           UpperCamelCase__      =			self.numpy_to_pil(__UpperCamelCase )

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase )




