



'''simple docstring'''




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase							:int				    =					1_6
lowerCamelCase							:Optional[int]				    =					3_2



def 	a			( lowerCamelCase__						,      lowerCamelCase__ = 16						,      lowerCamelCase__ = "bert-base-cased"    ):
			'''simple docstring'''





			A_	:						Optional[int]	=     AutoTokenizer.from_pretrained(lowerCamelCase__    )
			A_	:						List[str]	=     load_dataset("""glue"""						,      """mrpc"""    )

			def tokenize_function(lowerCamelCase__    ):
						# max_length=None => use the model max length (it's actually the default)
						A_	:						Dict	=     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
			A_	:						Optional[Any]	=     datasets.map(
			    lowerCamelCase__						,      batched=lowerCamelCase__						,      remove_columns=["""idx""", """sentence1""", """sentence2"""]						,      load_from_cache_file=lowerCamelCase__    )

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			A_	:						Optional[Any]	=     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.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(lowerCamelCase__						,      padding="""max_length"""						,      max_length=1_28						,      return_tensors="""pt"""    )
						return tokenizer.pad(lowerCamelCase__						,      padding="""longest"""						,      return_tensors="""pt"""    )

			# Instantiate dataloaders.
			A_	:						Tuple	=     DataLoader(
			    tokenized_datasets["""train"""]						,      shuffle=lowerCamelCase__						,      collate_fn=lowerCamelCase__						,      batch_size=lowerCamelCase__    )
			A_	:						Optional[Any]	=     DataLoader(
			    tokenized_datasets["""validation"""]						,      shuffle=lowerCamelCase__						,      collate_fn=lowerCamelCase__						,      batch_size=lowerCamelCase__    )

			return train_dataloader, eval_dataloader



def 	a			( lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			model.eval()
			A_	:						Optional[int]	=     0
			for step, batch in enumerate(lowerCamelCase__    ):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device    )
						with torch.no_grad():
									A_	:						Union[str, Any]	=     model(**lowerCamelCase__    )
						A_	:						str	=     outputs.logits.argmax(dim=-1    )
						# It is slightly faster to call this once, than multiple times
						A_,    A_	:						Dict	=     accelerator.gather(
						    (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
									if step == len(lowerCamelCase__    ) - 1:
												A_	:						Tuple	=     predictions[: len(eval_dataloader.dataset    ) - samples_seen]
												A_	:						List[Any]	=     references[: len(eval_dataloader.dataset    ) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=lowerCamelCase__						,      references=lowerCamelCase__						,      )

			A_	:						str	=     metric.compute()
			return eval_metric["accuracy"]



def 	a			( lowerCamelCase__						,      lowerCamelCase__    ):
			'''simple docstring'''





			A_	:						Dict	=     Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			A_	:						str	=     config["""lr"""]
			A_	:						Optional[int]	=     int(config["""num_epochs"""]    )
			A_	:						Dict	=     int(config["""seed"""]    )
			A_	:						Union[str, Any]	=     int(config["""batch_size"""]    )
			A_	:						Dict	=     args.model_name_or_path

			set_seed(lowerCamelCase__    )
			A_,    A_	:						Optional[Any]	=     get_dataloaders(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			A_	:						Optional[int]	=     AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__						,      return_dict=lowerCamelCase__    )

			# Instantiate optimizer
			A_	:						List[str]	=     (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			A_	:						List[Any]	=     optimizer_cls(params=model.parameters()						,      lr=lowerCamelCase__    )

			if accelerator.state.deepspeed_plugin is not None:
						A_	:						List[str]	=     accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						A_	:						List[str]	=     1
			A_	:						Tuple	=     (len(lowerCamelCase__    ) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						A_	:						Optional[int]	=     get_linear_schedule_with_warmup(
						    optimizer=lowerCamelCase__						,      num_warmup_steps=0						,      num_training_steps=lowerCamelCase__						,      )
			else:
						A_	:						List[Any]	=     DummyScheduler(lowerCamelCase__						,      total_num_steps=lowerCamelCase__						,      warmup_num_steps=0    )

			# 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_	:						Union[str, Any]	=     accelerator.prepare(
			    lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )

			# We need to keep track of how many total steps we have iterated over
			A_	:						Dict	=     0
			# We also need to keep track of the stating epoch so files are named properly
			A_	:						Union[str, Any]	=     0
			A_	:						List[Any]	=     evaluate.load("""glue"""						,      """mrpc"""    )
			A_	:						int	=     num_epochs

			if args.partial_train_epoch is not None:
						A_	:						Optional[int]	=     args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint    )
						A_	:						int	=     args.resume_from_checkpoint.split("""epoch_"""    )[1]
						A_	:						int	=     """"""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						A_	:						List[Any]	=     int(lowerCamelCase__    ) + 1
						A_	:						int	=     evaluation_loop(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
						accelerator.print("""resumed checkpoint performance:"""						,      lowerCamelCase__    )
						accelerator.print("""resumed checkpoint's scheduler's lr:"""						,      lr_scheduler.get_lr()[0]    )
						accelerator.print("""resumed optimizers's lr:"""						,      optimizer.param_groups[0]["""lr"""]    )
						with open(os.path.join(args.output_dir						,      f'state_{starting_epoch-1}.json'    )						,      """r"""    ) as f:
									A_	:						str	=     json.load(lowerCamelCase__    )
									assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["lr"] == lr_scheduler.get_lr()[0]
									), "Scheduler learning rate mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
									), "Optimizer learning rate mismatch, loading from checkpoint failed"
									assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
									return

    # Now we train the model
			A_	:						Optional[Any]	=     {}
			for epoch in range(lowerCamelCase__						,      lowerCamelCase__    ):
						model.train()
						for step, batch in enumerate(lowerCamelCase__    ):
									A_	:						str	=     model(**lowerCamelCase__    )
									A_	:						str	=     outputs.loss
									A_	:						int	=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__    )
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						A_	:						str	=     f'epoch_{epoch}'
						A_	:						Any	=     os.path.join(args.output_dir						,      lowerCamelCase__    )
						accelerator.save_state(lowerCamelCase__    )
						A_	:						List[str]	=     evaluation_loop(lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__    )
						A_	:						Union[str, Any]	=     accuracy
						A_	:						Optional[Any]	=     lr_scheduler.get_lr()[0]
						A_	:						Tuple	=     optimizer.param_groups[0]["""lr"""]
						A_	:						Optional[int]	=     epoch
						A_	:						Union[str, Any]	=     overall_step
						accelerator.print(f'epoch {epoch}:'						,      lowerCamelCase__    )

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									with open(os.path.join(args.output_dir						,      f'state_{epoch}.json'    )						,      """w"""    ) as f:
												json.dump(lowerCamelCase__						,      lowerCamelCase__    )



def 	a			( ):
			'''simple docstring'''





			A_	:						Union[str, Any]	=     argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
			parser.add_argument(
			    """--model_name_or_path"""						,      type=lowerCamelCase__						,      default="""bert-base-cased"""						,      help="""Path to pretrained model or model identifier from huggingface.co/models."""						,      required=lowerCamelCase__						,      )
			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."""						,      )
			parser.add_argument(
			    """--partial_train_epoch"""						,      type=lowerCamelCase__						,      default=lowerCamelCase__						,      help="""If passed, the training will stop after this number of epochs."""						,      )
			parser.add_argument(
			    """--num_epochs"""						,      type=lowerCamelCase__						,      default=2						,      help="""Number of train epochs."""						,      )
			A_	:						Any	=     parser.parse_args()
			A_	:						Any	=     {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

			training_function(lowerCamelCase__						,      lowerCamelCase__    )


if __name__ == "__main__":
						main()




'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')