

"""simple docstring"""






import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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,
# specifically showcasing how to perform Cross Validation,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


snake_case_ : Any   =							1_6
snake_case_ : int   =							3_2



def 	lowercase_						(     _lowercase							:		Accelerator				,   _lowercase							:		DatasetDict				,   _lowercase							:		List[int]				,   _lowercase							:		List[int]				,   _lowercase							:		int = 16						):




					'''simple docstring'''






					UpperCAmelCase      : Optional[Any]   =					AutoTokenizer.from_pretrained("bert-base-cased"						)
					UpperCAmelCase      : Optional[int]   =					DatasetDict(
					    {
					        "train": dataset["train"].select(UpperCAmelCase__						),
					        "validation": dataset["train"].select(UpperCAmelCase__						),
					        "test": dataset["validation"],
					    }						)

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

					def collate_fn(_lowercase							:		Tuple						):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										UpperCAmelCase      : List[str]   =					1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															UpperCAmelCase      : List[str]   =					16
										elif accelerator.mixed_precision != "no":
															UpperCAmelCase      : List[Any]   =					8
										else:
															UpperCAmelCase      : Optional[Any]   =					None

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

					# Instantiate dataloaders.
					UpperCAmelCase      : List[str]   =					DataLoader(
					    tokenized_datasets["train"]				,   shuffle=UpperCAmelCase__				,   collate_fn=UpperCAmelCase__				,   batch_size=UpperCAmelCase__						)
					UpperCAmelCase      : Union[str, Any]   =					DataLoader(
					    tokenized_datasets["validation"]				,   shuffle=UpperCAmelCase__				,   collate_fn=UpperCAmelCase__				,   batch_size=UpperCAmelCase__						)

					UpperCAmelCase      : Any   =					DataLoader(
					    tokenized_datasets["test"]				,   shuffle=UpperCAmelCase__				,   collate_fn=UpperCAmelCase__				,   batch_size=UpperCAmelCase__						)

					return train_dataloader, eval_dataloader, test_dataloader



def 	lowercase_						(     _lowercase							:		int				,   _lowercase							:		Optional[Any]						):




					'''simple docstring'''






					UpperCAmelCase      : Any   =					[]
					# Download the dataset
					UpperCAmelCase      : Any   =					load_dataset("glue"				,   "mrpc"						)
					# Create our splits
					UpperCAmelCase      : Dict   =					StratifiedKFold(n_splits=int(args.num_folds						)						)
					# Initialize accelerator
					UpperCAmelCase      : Union[str, Any]   =					Accelerator(cpu=args.cpu				,   mixed_precision=args.mixed_precision						)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					UpperCAmelCase      : Union[str, Any]   =					config["lr"]
					UpperCAmelCase      : int   =					int(config["num_epochs"]						)
					UpperCAmelCase      : int   =					int(config["seed"]						)
					UpperCAmelCase      : List[str]   =					int(config["batch_size"]						)

					UpperCAmelCase      : Tuple   =					evaluate.load("glue"				,   "mrpc"						)

					# If the batch size is too big we use gradient accumulation
					UpperCAmelCase      : int   =					1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										UpperCAmelCase      : Optional[Any]   =					batch_size // MAX_GPU_BATCH_SIZE
										UpperCAmelCase      : int   =					MAX_GPU_BATCH_SIZE

					set_seed(UpperCAmelCase__						)

					# New Code #
					# Create our folds:
					UpperCAmelCase      : str   =					kfold.split(np.zeros(datasets["train"].num_rows						)				,   datasets["train"]["label"]						)
					UpperCAmelCase      : List[Any]   =					[]
					# Iterate over them
					for i, (train_idxs, valid_idxs) in enumerate(UpperCAmelCase__						):
										UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase      : Optional[int]   =					get_fold_dataloaders(
										    UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   )
										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										UpperCAmelCase      : Optional[int]   =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased"				,   return_dict=UpperCAmelCase__						)

										# 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).
										UpperCAmelCase      : Any   =					model.to(accelerator.device						)

										# Instantiate optimizer
										UpperCAmelCase      : str   =					AdamW(params=model.parameters()				,   lr=UpperCAmelCase__						)

										# Instantiate scheduler
										UpperCAmelCase      : Union[str, Any]   =					get_linear_schedule_with_warmup(
										    optimizer=UpperCAmelCase__				,   num_warmup_steps=1_00				,   num_training_steps=(len(UpperCAmelCase__						) * 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.
										UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase   ,				UpperCAmelCase      : Dict   =					accelerator.prepare(
										    UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__				,   UpperCAmelCase__						)

										# Now we train the model
										for epoch in range(UpperCAmelCase__						):
															model.train()
															for step, batch in enumerate(UpperCAmelCase__						):
																				# We could avoid this line since we set the accelerator with `device_placement=True`.
																				batch.to(accelerator.device						)
																				UpperCAmelCase      : List[Any]   =					model(**UpperCAmelCase__						)
																				UpperCAmelCase      : Union[str, Any]   =					outputs.loss
																				UpperCAmelCase      : List[Any]   =					loss / gradient_accumulation_steps
																				accelerator.backward(UpperCAmelCase__						)
																				if step % gradient_accumulation_steps == 0:
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

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

															UpperCAmelCase      : List[Any]   =					metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:"""				,   UpperCAmelCase__						)

										# New Code #
										# We also run predictions on the test set at the very end
										UpperCAmelCase      : List[str]   =					[]
										for step, batch in enumerate(UpperCAmelCase__						):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device						)
															with torch.no_grad():
																				UpperCAmelCase      : Any   =					model(**UpperCAmelCase__						)
															UpperCAmelCase      : Union[str, Any]   =					outputs.logits
															UpperCAmelCase   ,				UpperCAmelCase      : Tuple   =					accelerator.gather_for_metrics((predictions, batch["labels"])						)
															fold_predictions.append(predictions.cpu()						)
															if i == 0:
																				# We need all of the test predictions
																				test_references.append(references.cpu()						)
        # Use accelerator.print to print only on the main process.
										test_predictions.append(torch.cat(UpperCAmelCase__				,   dim=0						)						)
										# We now need to release all our memory and get rid of the current model, optimizer, etc
										accelerator.free_memory()
					# New Code #
					# Finally we check the accuracy of our folded results:
					UpperCAmelCase      : Any   =					torch.cat(UpperCAmelCase__				,   dim=0						)
					UpperCAmelCase      : Optional[int]   =					torch.stack(UpperCAmelCase__				,   dim=0						).sum(dim=0						).div(int(args.num_folds						)						).argmax(dim=-1						)
					UpperCAmelCase      : Tuple   =					metric.compute(predictions=UpperCAmelCase__				,   references=UpperCAmelCase__						)
					accelerator.print("Average test metrics from all folds:"				,   UpperCAmelCase__						)



def 	lowercase_						(     ):




					'''simple docstring'''






					UpperCAmelCase      : str   =					argparse.ArgumentParser(description="Simple example of training script."						)
					parser.add_argument(
					    "--mixed_precision"				,   type=UpperCAmelCase__				,   default=UpperCAmelCase__				,   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."						)
					# New Code #
					parser.add_argument("--num_folds"				,   type=UpperCAmelCase__				,   default=3				,   help="The number of splits to perform across the dataset"						)
					UpperCAmelCase      : Tuple   =					parser.parse_args()
					UpperCAmelCase      : str   =					{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
					training_function(UpperCAmelCase__				,   UpperCAmelCase__						)


if __name__ == "__main__":
	main()




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase   =	logging.get_logger(__name__)  # pylint: disable=invalid-name



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     int:
							warnings.warn(
							    """The preprocess method is deprecated and will be removed in a future version. Please"""
							    """ use VaeImageProcessor.preprocess instead"""      ,					UpperCAmelCase__      ,					)
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return image
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [image]

							if isinstance(image[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       image[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														lowerCamelCase_					=       [np.array(i.resize((w, h)      ,					resample=PIL_INTERPOLATION["""lanczos"""]							)							)[None, :] for i in image]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       np.array(UpperCAmelCase__							).astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       image.transpose(0      ,					3      ,					1      ,					2							)
														lowerCamelCase_					=       2.0 * image - 1.0
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(image[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return image



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Union[List, PIL.Image.Image, torch.Tensor]							)	->     Any:
							if isinstance(UpperCAmelCase__      ,					torch.Tensor							):
														return mask
							elif isinstance(UpperCAmelCase__      ,					PIL.Image.Image							):
														lowerCamelCase_					=       [mask]

							if isinstance(mask[0]      ,					PIL.Image.Image							):
														lowerCamelCase_	, lowerCamelCase_					=       mask[0].size
														lowerCamelCase_	, lowerCamelCase_					=       (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
														lowerCamelCase_					=       [np.array(m.convert("""L"""							).resize((w, h)      ,					resample=PIL_INTERPOLATION["""nearest"""]							)							)[None, :] for m in mask]
														lowerCamelCase_					=       np.concatenate(UpperCAmelCase__      ,					axis=0							)
														lowerCamelCase_					=       mask.astype(np.floataa							) / 2_5_5.0
														lowerCamelCase_					=       0
														lowerCamelCase_					=       1
														lowerCamelCase_					=       torch.from_numpy(UpperCAmelCase__							)
							elif isinstance(mask[0]      ,					torch.Tensor							):
														lowerCamelCase_					=       torch.cat(UpperCAmelCase__      ,					dim=0							)
							return mask





class    __A(					UpperCAmelCase							):
					SCREAMING_SNAKE_CASE           =     42
					SCREAMING_SNAKE_CASE           =     42

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ):
												super().__init__()
												self.register_modules(unet=__UpperCamelCase ,       scheduler=__UpperCamelCase )



					@torch.no_grad()
					def __call__(						self       :  List[str] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  Union[torch.Tensor, PIL.Image.Image] ,       __UpperCamelCase       :  int = 2_5_0 ,       __UpperCamelCase       :  float = 0.0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  int = 1_0 ,       __UpperCamelCase       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       __UpperCamelCase       :  Optional[str] = "pil" ,       __UpperCamelCase       :  bool = True ,       ):
												lowerCamelCase_					=       image

												lowerCamelCase_					=       _preprocess_image(__UpperCamelCase )
												lowerCamelCase_					=       original_image.to(device=self.device ,       dtype=self.unet.dtype )
												lowerCamelCase_					=       _preprocess_mask(__UpperCamelCase )
												lowerCamelCase_					=       mask_image.to(device=self.device ,       dtype=self.unet.dtype )

												lowerCamelCase_					=       original_image.shape[0]

												# sample gaussian noise to begin the loop
												if isinstance(__UpperCamelCase ,       __UpperCamelCase ) and len(__UpperCamelCase ) != batch_size:
																			raise ValueError(
																			    F'''You have passed a list of generators of length {len(__UpperCamelCase )}, but requested an effective batch'''
																			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

												lowerCamelCase_					=       original_image.shape
												lowerCamelCase_					=       randn_tensor(__UpperCamelCase ,       generator=__UpperCamelCase ,       device=self.device ,       dtype=self.unet.dtype )

												# set step values
												self.scheduler.set_timesteps(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       self.device )
												lowerCamelCase_					=       eta

												lowerCamelCase_					=       self.scheduler.timesteps[0] + 1
												lowerCamelCase_					=       generator[0] if isinstance(__UpperCamelCase ,       __UpperCamelCase ) else generator
												for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
																			if t < t_last:
																										# predict the noise residual
																										lowerCamelCase_					=       self.unet(__UpperCamelCase ,       __UpperCamelCase ).sample
																										# compute previous image: x_t -> x_t-1
																										lowerCamelCase_					=       self.scheduler.step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ).prev_sample

																			else:
																										# compute the reverse: x_t-1 -> x_t
																										lowerCamelCase_					=       self.scheduler.undo_step(__UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
																			lowerCamelCase_					=       t

												lowerCamelCase_					=       (image / 2 + 0.5).clamp(0 ,       1 )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 ).numpy()
												if output_type == "pil":
																			lowerCamelCase_					=       self.numpy_to_pil(__UpperCamelCase )

												if not return_dict:
																			return (image,)

												return ImagePipelineOutput(images=__UpperCamelCase )





