


'''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
#
########################################################################


lowerCamelCase  						=     16
lowerCamelCase  						=     32

def _SCREAMING_SNAKE_CASE				(A  ,			A	)	->	Optional[int]:






       """simple docstring"""







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

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

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

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

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

       # If the batch size is too big we use gradient accumulation
       lowercase__				      =			1
       if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
              lowercase__				      =			batch_size // MAX_GPU_BATCH_SIZE
              lowercase__				      =			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.
              lowercase__				      =			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":
                     lowercase__				      =			16
              elif accelerator.mixed_precision != "no":
                     lowercase__				      =			8
              else:
                     lowercase__				      =			None

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

       # Instantiate dataloaders.
       lowercase__				      =			DataLoader(
           tokenized_datasets['''train''']  ,			shuffle=A  ,			collate_fn=A  ,			batch_size=A	)
       lowercase__				      =			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)
       lowercase__				      =			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).
       lowercase__				      =			model.to(accelerator.device	)

       # Instantiate optimizer
       lowercase__				      =			AdamW(params=model.parameters()  ,			lr=A	)

       # Instantiate scheduler
       lowercase__				      =			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.
       lowercase__      ,lowercase__      ,lowercase__      ,lowercase__      ,lowercase__				      =			accelerator.prepare(
           A  ,			A  ,			A  ,			A  ,			A	)

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

              if "epoch" in training_difference:
                     lowercase__				      =			int(training_difference.replace('''epoch_'''  ,			''''''	)	) + 1
                     lowercase__				      =			None
              else:
                     lowercase__				      =			int(training_difference.replace('''step_'''  ,			''''''	)	)
                     lowercase__				      =			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:
                     lowercase__				      =			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
                     lowercase__				      =			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
                     lowercase__				      =			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	)
                     lowercase__				      =			model(**A	)
                     lowercase__				      =			outputs.loss
                     lowercase__				      =			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	):
                            lowercase__				      =			f"step_{overall_step}"
                            if overall_step % checkpointing_steps == 0:
                                   if args.output_dir is not None:
                                          lowercase__				      =			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():
                            lowercase__				      =			model(**A	)
                     lowercase__				      =			outputs.logits.argmax(dim=-1	)
                     lowercase__      ,lowercase__				      =			accelerator.gather_for_metrics((predictions, batch['''labels'''])	)
                     metric.add_batch(
                         predictions=A  ,			references=A  ,			)

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

       if args.with_tracking:
              accelerator.end_training()

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






       """simple docstring"""







       lowercase__				      =			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'''  ,			)
       lowercase__				      =			parser.parse_args()
       lowercase__				      =			{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(A  ,			A	)


if __name__ == "__main__":
 main()








'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class   __lowerCAmelCase						(lowercase_  ,					lowercase_  ,					unittest.TestCase					):





      '''simple docstring'''






      lowerCAmelCase__ :					Dict   =							IFImgaImgPipeline
      lowerCAmelCase__ :					int   =							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      lowerCAmelCase__ :					Optional[int]   =							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
      lowerCAmelCase__ :					str   =							PipelineTesterMixin.required_optional_params - {"""latents"""}



      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             return self._get_dummy_components()



      def 				UpperCamelCase__		(self   :   Optional[Any]       ,						UpperCamelCase   :   List[Any]       ,						UpperCamelCase   :   Optional[Any]=0			):

             '''simple docstring'''







             if str(UpperCamelCase			).startswith('''mps'''			):
                    lowercase__				      =			torch.manual_seed(UpperCamelCase			)
             else:
                    lowercase__				      =			torch.Generator(device=UpperCamelCase			).manual_seed(UpperCamelCase			)

             lowercase__				      =			floats_tensor((1, 3, 32, 32)       ,						rng=random.Random(UpperCamelCase			)			).to(UpperCamelCase			)

             lowercase__				      =			{
                 '''prompt''': '''A painting of a squirrel eating a burger''',
                 '''image''': image,
                 '''generator''': generator,
                 '''num_inference_steps''': 2,
                 '''output_type''': '''numpy''',
             }

             return inputs



      def 				UpperCamelCase__		(self   :   int			):

             '''simple docstring'''







             self._test_save_load_optional_components()



      @unittest.skipIf(
          torch_device != '''cuda''' or not is_xformers_available()       ,						reason='''XFormers attention is only available with CUDA and `xformers` installed'''       ,						)
      def 				UpperCamelCase__		(self   :   Optional[Any]			):

             '''simple docstring'''







             self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Any			):

             '''simple docstring'''







             super().test_save_load_floataa(expected_max_diff=1E-1			)



      @unittest.skipIf(torch_device != '''cuda'''       ,						reason='''float16 requires CUDA'''			)
      def 				UpperCamelCase__		(self   :   Dict			):

             '''simple docstring'''







             super().test_floataa_inference(expected_max_diff=1E-1			)



      def 				UpperCamelCase__		(self   :   Optional[int]			):

             '''simple docstring'''







             self._test_attention_slicing_forward_pass(expected_max_diff=1E-2			)



      def 				UpperCamelCase__		(self   :   str			):

             '''simple docstring'''







             self._test_save_load_local()






      def 				UpperCamelCase__		(self   :   List[Any]			):

             '''simple docstring'''







             self._test_inference_batch_single_identical(
                 expected_max_diff=1E-2       ,						)




