






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
#
########################################################################

SCREAMING_SNAKE_CASE								=  16
SCREAMING_SNAKE_CASE								=  32

def        _lowerCamelCase		(  __A      :							Accelerator						,						__A      :							int = 16	) ->   List[Any]:
    _UpperCAmelCase			: Tuple     					=			AutoTokenizer.from_pretrained('''bert-base-cased'''	)
    _UpperCAmelCase			: Union[str, Any]     					=			load_dataset('''glue'''						,						'''mrpc'''	)

    def tokenize_function(__A      :							Union[str, Any]	):
        # max_length=None => use the model max length (it's actually the default)
        _UpperCAmelCase			: 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
    # starting with the main process first:
    with accelerator.main_process_first():
        _UpperCAmelCase			: Union[str, Any]     					=			datasets.map(
            __A						,						batched=__A						,						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			: Tuple     					=			tokenized_datasets.rename_column('''label'''						,						'''labels'''	)

    def collate_fn(__A      :							List[Any]	):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        _UpperCAmelCase			: Any     					=			128 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			: Optional[int]     					=			16
        elif accelerator.mixed_precision != "no":
            _UpperCAmelCase			: Union[str, Any]     					=			8
        else:
            _UpperCAmelCase			: Union[str, Any]     					=			None

        return tokenizer.pad(
            __A						,						padding='''longest'''						,						max_length=__A						,						pad_to_multiple_of=__A						,						return_tensors='''pt'''						,						)

    # Instantiate dataloaders.
    _UpperCAmelCase			: Any     					=			DataLoader(
        tokenized_datasets['''train''']						,						shuffle=__A						,						collate_fn=__A						,						batch_size=__A	)
    _UpperCAmelCase			: Optional[int]     					=			DataLoader(
        tokenized_datasets['''validation''']						,						shuffle=__A						,						collate_fn=__A						,						batch_size=__A	)

    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

  SCREAMING_SNAKE_CASE								=  mocked_dataloaders  # noqa: F811

def        _lowerCamelCase		(  __A      :							Optional[int]						,						__A      :							Any	) ->   Union[str, Any]:
    # For testing only
    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''						,						__A	) == "1":
        _UpperCAmelCase			: List[Any]     					=			2
    # Initialize accelerator
    _UpperCAmelCase			: Optional[int]     					=			Accelerator(cpu=args.cpu						,						mixed_precision=args.mixed_precision	)
    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    _UpperCAmelCase			: Optional[Any]     					=			config['''lr''']
    _UpperCAmelCase			: Optional[Any]     					=			int(config['''num_epochs''']	)
    _UpperCAmelCase			: Any     					=			int(config['''seed''']	)
    _UpperCAmelCase			: int     					=			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			: Union[str, Any]     					=			args.checkpointing_steps
        elif args.checkpointing_steps.isdigit():
            _UpperCAmelCase			: Optional[Any]     					=			int(args.checkpointing_steps	)
        else:
            raise ValueError(
                f'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''	)
    else:
        _UpperCAmelCase			: List[str]     					=			None

    set_seed(__A	)

    _UpperCAmelCase					,	_UpperCAmelCase			: Tuple     					=			get_dataloaders(__A						,						__A	)
    _UpperCAmelCase			: Tuple     					=			evaluate.load('''glue'''						,						'''mrpc'''	)

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

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    _UpperCAmelCase			: int     					=			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''						,						return_dict=__A	)

    # 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			: Optional[int]     					=			model.to(accelerator.device	)

    # Instantiate optimizer
    _UpperCAmelCase			: str     					=			AdamW(params=model.parameters()						,						lr=__A	)

    # Instantiate scheduler
    _UpperCAmelCase			: List[Any]     					=			get_linear_schedule_with_warmup(
        optimizer=__A						,						num_warmup_steps=100						,						num_training_steps=(len(__A	) * 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			: int     					=			accelerator.prepare(
        __A						,						__A						,						__A						,						__A						,						__A	)

    # New Code #
    # We need to keep track of how many total steps we have iterated over
    _UpperCAmelCase			: int     					=			0
    # We also need to keep track of the stating epoch so files are named properly
    _UpperCAmelCase			: Union[str, Any]     					=			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			: Union[str, Any]     					=			os.path.basename(args.resume_from_checkpoint	)
        else:
            # Get the most recent checkpoint
            _UpperCAmelCase			: Dict     					=			[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
            dirs.sort(key=os.path.getctime	)
            _UpperCAmelCase			: Optional[Any]     					=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
        # Extract `epoch_{i}` or `step_{i}`
        _UpperCAmelCase			: int     					=			os.path.splitext(__A	)[0]

        if "epoch" in training_difference:
            _UpperCAmelCase			: List[Any]     					=			int(training_difference.replace('''epoch_'''						,						''''''	)	) + 1
            _UpperCAmelCase			: Optional[Any]     					=			None
        else:
            _UpperCAmelCase			: Optional[int]     					=			int(training_difference.replace('''step_'''						,						''''''	)	)
            _UpperCAmelCase			: Optional[int]     					=			resume_step // len(__A	)
            resume_step -= starting_epoch * len(__A	)

    # Now we train the model
    for epoch in range(__A						,						__A	):
        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			: int     					=			accelerator.skip_first_batches(__A						,						__A	)
            overall_step += resume_step
        else:
            # After the first iteration though, we need to go back to the original dataloader
            _UpperCAmelCase			: List[str]     					=			train_dataloader
        for step, batch in enumerate(__A	):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device	)
            _UpperCAmelCase			: Optional[int]     					=			model(**__A	)
            _UpperCAmelCase			: Dict     					=			outputs.loss
            _UpperCAmelCase			: Union[str, Any]     					=			loss / gradient_accumulation_steps
            accelerator.backward(__A	)
            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(__A						,						__A	):
                _UpperCAmelCase			: str     					=			f'''step_{overall_step}'''
                if overall_step % checkpointing_steps == 0:
                    if args.output_dir is not None:
                        _UpperCAmelCase			: Optional[int]     					=			os.path.join(args.output_dir						,						__A	)
                    accelerator.save_state(__A	)

        model.eval()
        for step, batch in enumerate(__A	):
            # 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			: Any     					=			model(**__A	)
            _UpperCAmelCase			: Tuple     					=			outputs.logits.argmax(dim=-1	)
            _UpperCAmelCase					,	_UpperCAmelCase			: Optional[int]     					=			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
            metric.add_batch(
                predictions=__A						,						references=__A						,						)

        _UpperCAmelCase			: Dict     					=			metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'''epoch {epoch}:'''						,						__A	)

        # 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			: Any     					=			f'''epoch_{epoch}'''
            if args.output_dir is not None:
                _UpperCAmelCase			: Tuple     					=			os.path.join(args.output_dir						,						__A	)
            accelerator.save_state(__A	)

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Optional[int]     					=			argparse.ArgumentParser(description='''Simple example of training script.'''	)
    parser.add_argument(
        '''--mixed_precision'''						,						type=__A						,						default=__A						,						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=__A						,						default=__A						,						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=__A						,						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=__A						,						default=__A						,						help='''If the training should continue from a checkpoint folder.'''						,						)
    _UpperCAmelCase			: Union[str, Any]     					=			parser.parse_args()
    _UpperCAmelCase			: Any     					=			{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(__A						,						__A	)


if __name__ == "__main__":
  main()












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     super().__init__()
     self.register_modules(unet=_A							,					scheduler=_A)


 @torch.no_grad()
 def __call__(		self							,					_A = 1							,					_A = None							,					_A = 1000							,					_A = "pil"							,					_A = True							,					)	->						Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _UpperCAmelCase			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			randn_tensor(_A							,					generator=_A							,					device=self.device)

     # set step values
     self.scheduler.set_timesteps(_A)

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _UpperCAmelCase			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




