



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        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						if args.with_tracking:
												snake_case_			:List[str]						=      Accelerator(
												    cpu=args.cpu, mixed_precision=args.mixed_precision, log_with="""all""", project_dir=args.project_dir     )
						else:
												snake_case_			:int						=      Accelerator(cpu=args.cpu, mixed_precision=args.mixed_precision     )

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

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

						snake_case_			:Any						=      AutoTokenizer.from_pretrained("""bert-base-cased"""     )
						snake_case_			:Optional[int]						=      load_dataset("""glue""", """mrpc"""     )
						snake_case_			:Dict						=      evaluate.load("""glue""", """mrpc"""     )

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

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

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

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

						# Instantiate dataloaders.
						snake_case_			:List[str]						=      DataLoader(
						    tokenized_datasets["""train"""], shuffle=SCREAMING_SNAKE_CASE__, collate_fn=SCREAMING_SNAKE_CASE__, batch_size=SCREAMING_SNAKE_CASE__     )
						snake_case_			:Tuple						=      DataLoader(
						    tokenized_datasets["""validation"""], shuffle=SCREAMING_SNAKE_CASE__, collate_fn=SCREAMING_SNAKE_CASE__, batch_size=SCREAMING_SNAKE_CASE__     )

						set_seed(SCREAMING_SNAKE_CASE__     )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						snake_case_			:Optional[Any]						=      AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""", return_dict=SCREAMING_SNAKE_CASE__     )

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

						# Instantiate optimizer
						snake_case_			:Optional[Any]						=      AdamW(params=model.parameters(), lr=SCREAMING_SNAKE_CASE__     )

						# Instantiate scheduler
						snake_case_			:Optional[int]						=      get_linear_schedule_with_warmup(
						    optimizer=SCREAMING_SNAKE_CASE__, num_warmup_steps=100, num_training_steps=(len(SCREAMING_SNAKE_CASE__     ) * 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.
						snake_case_			:Dict						=      accelerator.prepare(
						    SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__     )

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

												if "epoch" in training_difference:
																		snake_case_			:Union[str, Any]						=      int(training_difference.replace("""epoch_""", """"""     )     ) + 1
																		snake_case_			:List[Any]						=      None
												else:
																		snake_case_			:List[Any]						=      int(training_difference.replace("""step_""", """"""     )     )
																		snake_case_			:Dict						=      resume_step // len(SCREAMING_SNAKE_CASE__     )
																		resume_step -= starting_epoch * len(SCREAMING_SNAKE_CASE__     )

    # Now we train the model
						for epoch in range(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__     ):
												model.train()
												if args.with_tracking:
																		snake_case_			:List[str]						=      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
																		snake_case_			:Optional[int]						=      accelerator.skip_first_batches(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__     )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		snake_case_			:Optional[Any]						=      train_dataloader
												for step, batch in enumerate(SCREAMING_SNAKE_CASE__     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		snake_case_			:Tuple						=      model(**SCREAMING_SNAKE_CASE__     )
																		snake_case_			:Union[str, Any]						=      outputs.loss
																		snake_case_			:Optional[Any]						=      loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(SCREAMING_SNAKE_CASE__     )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__     ):
																								snake_case_			:Optional[int]						=      f"""step_{overall_step}"""
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				snake_case_			:Union[str, Any]						=      os.path.join(args.output_dir, SCREAMING_SNAKE_CASE__     )
																														accelerator.save_state(SCREAMING_SNAKE_CASE__     )

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

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

												if checkpointing_steps == "epoch":
																		snake_case_			:int						=      f"""epoch_{epoch}"""
																		if args.output_dir is not None:
																								snake_case_			:Optional[Any]						=      os.path.join(args.output_dir, SCREAMING_SNAKE_CASE__     )
																		accelerator.save_state(SCREAMING_SNAKE_CASE__     )

						if args.with_tracking:
												accelerator.end_training()
def        A_  (      ):







						'''simple docstring'''


						snake_case_			:Optional[Any]						=      argparse.ArgumentParser(description="""Simple example of training script."""     )
						parser.add_argument(
						    """--mixed_precision""", type=SCREAMING_SNAKE_CASE__, default=SCREAMING_SNAKE_CASE__, 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=SCREAMING_SNAKE_CASE__, default=SCREAMING_SNAKE_CASE__, 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=SCREAMING_SNAKE_CASE__, default=SCREAMING_SNAKE_CASE__, 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=SCREAMING_SNAKE_CASE__, default=""".""", help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""", )
						parser.add_argument(
						    """--project_dir""", type=SCREAMING_SNAKE_CASE__, default="""logs""", help="""Location on where to store experiment tracking logs` and relevent project information""", )
						snake_case_			:Optional[int]						=      parser.parse_args()
						snake_case_			:Dict						=      {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
						main()





"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					RePaintPipeline
					_A		:      List[Any]						=					IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
					_A		:      Union[str, Any]						=					PipelineTesterMixin.required_optional_params - {
					    """latents""",
					    """num_images_per_prompt""",
					    """callback""",
					    """callback_steps""",
					}
					_A		:      Tuple						=					IMAGE_INPAINTING_BATCH_PARAMS
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[str]:
											torch.manual_seed(0						)
											torch.manual_seed(0						)
											snake_case_			:Union[str, Any]						=      UNetaDModel(
											    block_out_channels=(32, 64)						,					layers_per_block=2						,					sample_size=32						,					in_channels=3						,					out_channels=3						,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,					)
											snake_case_			:str						=      RePaintScheduler()
											snake_case_			:Any						=      {"""unet""": unet, """scheduler""": scheduler}
											return components






					def        lowerCAmelCase_    (	self:							Dict						,					snake_case:							Dict						,					snake_case:							List[Any]=0						)       ->     Tuple:
											if str(snake_case						).startswith("""mps"""						):
																	snake_case_			:int						=      torch.manual_seed(snake_case						)
											else:
																	snake_case_			:Optional[Any]						=      torch.Generator(device=snake_case						).manual_seed(snake_case						)
											snake_case_			:Dict						=      np.random.RandomState(snake_case						).standard_normal((1, 3, 32, 32)						)
											snake_case_			:int						=      torch.from_numpy(snake_case						).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Dict						=      (image > 0).to(device=snake_case						,					dtype=torch.floataa						)
											snake_case_			:Union[str, Any]						=      {
											    """image""": image,
											    """mask_image""": mask,
											    """generator""": generator,
											    """num_inference_steps""": 5,
											    """eta""": 0.0,
											    """jump_length""": 2,
											    """jump_n_sample""": 2,
											    """output_type""": """numpy""",
											}
											return inputs






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											snake_case_			:int						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
											snake_case_			:List[str]						=      self.get_dummy_components()
											snake_case_			:Union[str, Any]						=      RePaintPipeline(**snake_case						)
											snake_case_			:Tuple						=      sd_pipe.to(snake_case						)
											sd_pipe.set_progress_bar_config(disable=snake_case						)

											snake_case_			:Tuple						=      self.get_dummy_inputs(snake_case						)
											snake_case_			:Dict						=      sd_pipe(**snake_case						).images
											snake_case_			:List[Any]						=      image[0, -3:, -3:, -1]

											assert image.shape == (1, 32, 32, 3)
											snake_case_			:Optional[int]						=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]						)

											assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3






					@skip_mps
					def        lowerCAmelCase_    (	self:							Dict						)       ->     int:
											return super().test_save_load_local()






					@unittest.skip("""non-deterministic pipeline"""						)
					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     List[Any]:
											return super().test_inference_batch_single_identical()






					@skip_mps
					def        lowerCAmelCase_    (	self:							int						)       ->     Optional[Any]:
											return super().test_dict_tuple_outputs_equivalent()






					@skip_mps
					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Optional[Any]:
											return super().test_save_load_optional_components()






					@skip_mps
					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											return super().test_attention_slicing_forward_pass()

@nightly
@require_torch_gpu
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     str:
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Tuple:
											snake_case_			:Any						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256.png"""						)
											snake_case_			:Union[str, Any]						=      load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"""						)
											snake_case_			:str						=      load_numpy(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"""
											    """repaint/celeba_hq_256_result.npy"""						)

											snake_case_			:List[str]						=      """google/ddpm-ema-celebahq-256"""
											snake_case_			:Optional[int]						=      UNetaDModel.from_pretrained(snake_case						)
											snake_case_			:List[Any]						=      RePaintScheduler.from_pretrained(snake_case						)

											snake_case_			:str						=      RePaintPipeline(unet=snake_case						,					scheduler=snake_case						).to(snake_case						)
											repaint.set_progress_bar_config(disable=snake_case						)
											repaint.enable_attention_slicing()

											snake_case_			:int						=      torch.manual_seed(0						)
											snake_case_			:str						=      repaint(
											    snake_case						,					snake_case						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=snake_case						,					output_type="""np"""						,					)
											snake_case_			:List[str]						=      output.images[0]

											assert image.shape == (256, 256, 3)
											assert np.abs(expected_image - image						).mean() < 1E-2
