
"""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
#
# 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
#
########################################################################


A_						       =				16
A_						       =				32






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			# Initialize accelerator
			if args.with_tracking:
						lowerCamelCase_ 		=     Accelerator(
						    cpu=args.cpu  ,mixed_precision=args.mixed_precision  ,log_with='''all'''  ,project_dir=args.project_dir	)
			else:
						lowerCamelCase_ 		=     Accelerator(cpu=args.cpu  ,mixed_precision=args.mixed_precision	)

			if hasattr(args.checkpointing_steps  ,'''isdigit'''	):
						if args.checkpointing_steps == "epoch":
									lowerCamelCase_ 		=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase_ 		=     int(args.checkpointing_steps	)
						else:
									raise ValueError(
									    f"Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."	)
			else:
						lowerCamelCase_ 		=     None
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase_ 		=     config['''lr''']
			lowerCamelCase_ 		=     int(config['''num_epochs''']	)
			lowerCamelCase_ 		=     int(config['''seed''']	)
			lowerCamelCase_ 		=     int(config['''batch_size''']	)

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

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

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

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

			def collate_fn(lowerCAmelCase__	):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						lowerCamelCase_ 		=     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":
									lowerCamelCase_ 		=     16
						elif accelerator.mixed_precision != "no":
									lowerCamelCase_ 		=     8
						else:
									lowerCamelCase_ 		=     None

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

			# Instantiate dataloaders.
			lowerCamelCase_ 		=     DataLoader(
			    tokenized_datasets['''train''']  ,shuffle=lowerCAmelCase__  ,collate_fn=lowerCAmelCase__  ,batch_size=lowerCAmelCase__	)
			lowerCamelCase_ 		=     DataLoader(
			    tokenized_datasets['''validation''']  ,shuffle=lowerCAmelCase__  ,collate_fn=lowerCAmelCase__  ,batch_size=lowerCAmelCase__	)

			set_seed(lowerCAmelCase__	)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase_ 		=     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).
			lowerCamelCase_ 		=     model.to(accelerator.device	)

			# Instantiate optimizer
			lowerCamelCase_ 		=     AdamW(params=model.parameters()  ,lr=lowerCAmelCase__	)

			# Instantiate scheduler
			lowerCamelCase_ 		=     get_linear_schedule_with_warmup(
			    optimizer=lowerCAmelCase__  ,num_warmup_steps=100  ,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.
			lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.prepare(
			    lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	)

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

						if "epoch" in training_difference:
									lowerCamelCase_ 		=     int(training_difference.replace('''epoch_'''  ,''''''	)	) + 1
									lowerCamelCase_ 		=     None
						else:
									lowerCamelCase_ 		=     int(training_difference.replace('''step_'''  ,''''''	)	)
									lowerCamelCase_ 		=     resume_step // len(lowerCAmelCase__	)
									resume_step -= starting_epoch * len(lowerCAmelCase__	)

    # Now we train the model
			for epoch in range(lowerCAmelCase__  ,lowerCAmelCase__	):
						model.train()
						if args.with_tracking:
									lowerCamelCase_ 		=     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
									lowerCamelCase_ 		=     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
									lowerCamelCase_ 		=     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	)
									lowerCamelCase_ 		=     model(**lowerCAmelCase__	)
									lowerCamelCase_ 		=     outputs.loss
									lowerCamelCase_ 		=     loss / gradient_accumulation_steps
									# We keep track of the loss at each epoch
									if args.with_tracking:
												total_loss += loss.detach().float()
									accelerator.backward(lowerCAmelCase__	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

									if isinstance(lowerCAmelCase__  ,lowerCAmelCase__	):
												lowerCamelCase_ 		=     f"step_{overall_step}"
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase_ 		=     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`.
									batch.to(accelerator.device	)
									with torch.no_grad():
												lowerCamelCase_ 		=     model(**lowerCAmelCase__	)
									lowerCamelCase_ 		=     outputs.logits.argmax(dim=-1	)
									lowerCamelCase_		,   lowerCamelCase_ 		=     accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
									metric.add_batch(
									    predictions=lowerCAmelCase__  ,references=lowerCAmelCase__  ,)

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

						if checkpointing_steps == "epoch":
									lowerCamelCase_ 		=     f"epoch_{epoch}"
									if args.output_dir is not None:
												lowerCamelCase_ 		=     os.path.join(args.output_dir  ,lowerCAmelCase__	)
									accelerator.save_state(lowerCAmelCase__	)

			if args.with_tracking:
						accelerator.end_training()






def    lowercase	(  ):
			lowerCamelCase_ 		=     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(
			    '''--resume_from_checkpoint'''  ,type=lowerCAmelCase__  ,default=lowerCAmelCase__  ,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=lowerCAmelCase__  ,default='''.'''  ,help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,)
			parser.add_argument(
			    '''--project_dir'''  ,type=lowerCAmelCase__  ,default='''logs'''  ,help='''Location on where to store experiment tracking logs` and relevent project information'''  ,)
			lowerCamelCase_ 		=     parser.parse_args()
			lowerCamelCase_ 		=     {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
			training_function(lowerCAmelCase__  ,lowerCAmelCase__	)


if __name__ == "__main__":
						main()





"""simple docstring"""


import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class    __lowerCamelCase     (   unittest.TestCase ):
				a__:			Union[str, Any]									=						'hf-internal-testing/tiny-stable-diffusion-pipe'
				a__:			Tuple									=						1
				a__:			int									=						77
				a__:			List[str]									=						32
				a__:			List[Any]									=						4
				a__:			str									=						64
				a__:			Optional[Any]									=						torch.manual_seed(0 )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase=0.9_9_9_9      ):
							lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(self.model_id       ,	subfolder='''unet'''      )
							lowerCamelCase_ 		=     unet.to(UpperCAmelCase      )
							lowerCamelCase_ 		=     EMAModel(unet.parameters()       ,	decay=UpperCAmelCase       ,	model_cls=UpperCAmelCase       ,	model_config=unet.config      )
							return unet, ema_unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     torch.randn(
							    self.batch_size       ,	self.num_in_channels       ,	self.latent_height       ,	self.latent_width       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     torch.randint(0       ,	1000       ,	size=(self.batch_size,)       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     torch.randn(
							    self.batch_size       ,	self.prompt_length       ,	self.text_encoder_hidden_dim       ,	generator=self.generator      ).to(UpperCAmelCase      )
							return noisy_latents, timesteps, encoder_hidden_states




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     {}
							for k, param in unet.state_dict().items():
										lowerCamelCase_ 		=     torch.randn_like(UpperCAmelCase      ) + (param * torch.randn_like(UpperCAmelCase      ))
										updated_state_dict.update({k: updated_param}      )
							unet.load_state_dict(UpperCAmelCase      )
							return unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()      )
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2      ):
										ema_unet.step(unet.parameters()      )
							assert ema_unet.optimization_step == 3




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()      )
							lowerCamelCase_ 		=     list(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4      ):
										ema_unet.step(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

							# Now the EMA'd parameters won't be equal to the original model parameters.
							lowerCamelCase_ 		=     list(unet_pseudo_updated_step_one.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4      ):
										ema_unet.step(unet.parameters()      )
							for s_param, param in zip(ema_unet.shadow_params       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()

							# First backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							# Second backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							for step_one, step_two in zip(UpperCAmelCase       ,	UpperCAmelCase      ):
										assert ~torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models(decay=0.0      )
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							for step_one, step_two in zip(UpperCAmelCase       ,	UpperCAmelCase      ):
										assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase      )





				@skip_mps
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(UpperCAmelCase      )
										lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(UpperCAmelCase       ,	model_cls=UpperCAmelCase      )
										lowerCamelCase_ 		=     loaded_unet.to(unet.device      )

							# Since no EMA step has been performed the outputs should match.
							lowerCamelCase_ 		=     unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample
							lowerCamelCase_ 		=     loaded_unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample

							assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase       ,	atol=1e-4      )



