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

snake_case__  							=							16
snake_case__  							=							32

def        lowerCamelCase_      (			UpperCAmelCase_							:				Accelerator							,			UpperCAmelCase_							:				int = 16       ):
						lowercase       :    int											=  AutoTokenizer.from_pretrained('''bert-base-cased'''       )
						lowercase       :    Tuple											=  load_dataset('''glue'''							,			'''mrpc'''       )

						def tokenize_function(UpperCAmelCase_							:				int       ):
												# max_length=None => use the model max length (it's actually the default)
												lowercase       :    Dict											=  tokenizer(examples['''sentence1''']							,			examples['''sentence2''']							,			truncation=UpperCAmelCase_							,			max_length=UpperCAmelCase_       )
												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():
												lowercase       :    Optional[int]											=  datasets.map(
												    UpperCAmelCase_							,			batched=UpperCAmelCase_							,			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
						lowercase       :    Union[str, Any]											=  tokenized_datasets.rename_column('''label'''							,			'''labels'''       )

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

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

						# Instantiate dataloaders.
						lowercase       :    Optional[Any]											=  DataLoader(
						    tokenized_datasets['''train''']							,			shuffle=UpperCAmelCase_							,			collate_fn=UpperCAmelCase_							,			batch_size=UpperCAmelCase_       )
						lowercase       :    Dict											=  DataLoader(
						    tokenized_datasets['''validation''']							,			shuffle=UpperCAmelCase_							,			collate_fn=UpperCAmelCase_							,			batch_size=UpperCAmelCase_       )

						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

				snake_case__  							=							mocked_dataloaders  # noqa: F811

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

						set_seed(UpperCAmelCase_       )

						lowercase				,				lowercase       :    str											=  get_dataloaders(UpperCAmelCase_							,			UpperCAmelCase_       )
						lowercase       :    Union[str, Any]											=  evaluate.load('''glue'''							,			'''mrpc'''       )

						# If the batch size is too big we use gradient accumulation
						lowercase       :    int											=  1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												lowercase       :    Any											=  batch_size // MAX_GPU_BATCH_SIZE
												lowercase       :    Union[str, Any]											=  MAX_GPU_BATCH_SIZE

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

						# 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).
						lowercase       :    Union[str, Any]											=  model.to(accelerator.device       )

						# Instantiate optimizer
						lowercase       :    List[str]											=  AdamW(params=model.parameters()							,			lr=UpperCAmelCase_       )

						# Instantiate scheduler
						lowercase       :    int											=  get_linear_schedule_with_warmup(
						    optimizer=UpperCAmelCase_							,			num_warmup_steps=1_00							,			num_training_steps=(len(UpperCAmelCase_       ) * 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.
						lowercase				,				lowercase				,				lowercase				,				lowercase				,				lowercase       :    str											=  accelerator.prepare(
						    UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_       )

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

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

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

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

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

												# 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":
																		lowercase       :    Dict											=  f'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								lowercase       :    Dict											=  os.path.join(args.output_dir							,			UpperCAmelCase_       )
																		accelerator.save_state(UpperCAmelCase_       )

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


if __name__ == "__main__":
				main()




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 							UpperCAmelCase	(   __lowerCamelCase					):
					a__: UNetaDModel
					a__: PNDMScheduler

					def __init__(			self      :					List[str] ,      lowerCAmelCase      :					UNetaDModel ,      lowerCAmelCase      :					PNDMScheduler					):
											super().__init__()

											lowercase       :    int											=  PNDMScheduler.from_config(scheduler.config					)

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




					@torch.no_grad()
					def __call__(			self      :					List[str] ,      lowerCAmelCase      :					int = 1 ,      lowerCAmelCase      :					int = 50 ,      lowerCAmelCase      :					Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowerCAmelCase      :					Optional[str] = "pil" ,      lowerCAmelCase      :					bool = True ,      **lowerCAmelCase      :					int ,      ):
											lowercase       :    Tuple											=  randn_tensor(
											    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,      generator=lowerCAmelCase ,      device=self.device ,      )

											self.scheduler.set_timesteps(lowerCAmelCase					)
											for t in self.progress_bar(self.scheduler.timesteps					):
																	lowercase       :    Dict											=  self.unet(lowerCAmelCase ,      lowerCAmelCase					).sample

																	lowercase       :    Tuple											=  self.scheduler.step(lowerCAmelCase ,      lowerCAmelCase ,      lowerCAmelCase					).prev_sample

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

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=lowerCAmelCase					)


