
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
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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							:Dict											=       16
SCREAMING_SNAKE_CASE							:Optional[int]											=       32


def 		UpperCAmelCase    (			a_	,     a_		)	->			Union[str, Any]:







				"""simple docstring"""



				if args.with_tracking:
								__A         =				Accelerator(
								    cpu=args.cpu	,     mixed_precision=args.mixed_precision	,     log_with="all"	,     project_dir=args.project_dir		)
				else:
								__A         =				Accelerator(cpu=args.cpu	,     mixed_precision=args.mixed_precision		)

				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
				# 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"]		)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								__A         =				os.path.split(a_		)[-1].split("."		)[0]
								accelerator.init_trackers(a_	,     a_		)

				__A         =				AutoTokenizer.from_pretrained("bert-base-cased"		)
				__A         =				load_dataset("glue"	,     "mrpc"		)
				__A         =				evaluate.load("glue"	,     "mrpc"		)

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

				# 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

				def collate_fn(a_		):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__A         =				1_2_8 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         =				1_6
								elif accelerator.mixed_precision != "no":
												__A         =				8
								else:
												__A         =				None

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

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

				set_seed(a_		)

				# 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=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).
				__A         =				model.to(accelerator.device		)

				# Instantiate optimizer
				__A         =				AdamW(params=model.parameters()	,     lr=a_		)

				# Instantiate scheduler
				__A         =				get_linear_schedule_with_warmup(
				    optimizer=a_	,     num_warmup_steps=1_0_0	,     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.
				__A    ,							__A    ,							__A    ,							__A    ,							__A         =				accelerator.prepare(
				    a_	,     a_	,     a_	,     a_	,     a_		)

				# 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

				# Potentially load in the weights and states from a previous save
				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(a_		)[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(a_		)
												resume_step -= starting_epoch * len(a_		)

    # Now we train the model
				for epoch in range(a_	,     a_		):
								model.train()
								if args.with_tracking:
												__A         =				0
								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(a_	,     a_		)
												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(a_		):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device		)
												__A         =				model(**a_		)
												__A         =				outputs.loss
												__A         =				loss / gradient_accumulation_steps
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(a_		)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

												if isinstance(a_	,     a_		):
																__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	,     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`.
												batch.to(accelerator.device		)
												with torch.no_grad():
																__A         =				model(**a_		)
												__A         =				outputs.logits.argmax(dim=-1		)
												__A    ,							__A         =				accelerator.gather_for_metrics((predictions, batch["labels"])		)
												metric.add_batch(
												    predictions=a_	,     references=a_	,     )

								__A         =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''	,     a_		)
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": eval_metric["accuracy"],
												        "f1": eval_metric["f1"],
												        "train_loss": total_loss.item() / len(a_		),
												        "epoch": epoch,
												    }	,     step=a_	,     )

								if checkpointing_steps == "epoch":
												__A         =				F'''epoch_{epoch}'''
												if args.output_dir is not None:
																__A         =				os.path.join(args.output_dir	,     a_		)
												accelerator.save_state(a_		)

				if args.with_tracking:
								accelerator.end_training()


def 		UpperCAmelCase    (			)	->			Dict:







				"""simple docstring"""



				__A         =				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(
				    "--resume_from_checkpoint"	,     type=a_	,     default=a_	,     help="If the training should continue from a checkpoint folder."	,     )
				parser.add_argument(
				    "--with_tracking"	,     action="store_true"	,     help="Whether to load in all available experiment trackers from the environment and use them for logging."	,     )
				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(
				    "--project_dir"	,     type=a_	,     default="logs"	,     help="Location on where to store experiment tracking logs` and relevent project information"	,     )
				__A         =				parser.parse_args()
				__A         =				{"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
				training_function(a_	,     a_		)


if __name__ == "__main__":
					main()


from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
					from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
