


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
SCREAMING_SNAKE_CASE__     					=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")



@dataclass
class 						_UpperCAmelCase      :
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default="""cifar10"""		,		metadata={"""help""": """Name of a dataset from the datasets package"""}							)
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default=lowercase		,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}							)
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default=lowercase		,		metadata={"""help""": """The column name of the images in the files."""}							)
		lowerCamelCase_	:			Optional[str]       			=  field(default=lowercase		,		metadata={"""help""": """A folder containing the training data."""}							)
		lowerCamelCase_	:			Optional[str]       			=  field(default=lowercase		,		metadata={"""help""": """A folder containing the validation data."""}							)
		lowerCamelCase_	:			Optional[float]       			=  field(
		    default=0.15		,		metadata={"""help""": """Percent to split off of train for validation."""}							)
		lowerCamelCase_	:			Optional[int]       			=  field(
		    default=lowercase		,		metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of training examples to this """
		            """value if set."""
		        )
		    }		,		)
		lowerCamelCase_	:			Optional[int]       			=  field(
		    default=lowercase		,		metadata={
		        """help""": (
		            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
		            """value if set."""
		        )
		    }		,		)






		def _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						{}
				if self.train_dir is not None:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						self.train_dir
				if self.validation_dir is not None:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.validation_dir
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						data_files if data_files else None




@dataclass
class 						_UpperCAmelCase      :
		lowerCamelCase_	:			str       			=  field(
		    default=lowercase		,		metadata={
		        """help""": (
		            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
		        )
		    }		,		)
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default=lowercase		,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}							)
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default=lowercase		,		metadata={
		        """help""": (
		            """Override some existing default config settings when a model is trained from scratch. Example: """
		            """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
		        )
		    }		,		)
		lowerCamelCase_	:			Optional[str]       			=  field(
		    default=lowercase		,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}							)
		lowerCamelCase_	:			str       			=  field(
		    default="""main"""		,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}		,		)
		lowerCamelCase_	:			str       			=  field(default=lowercase		,		metadata={"""help""": """Name or path of preprocessor config."""}							)
		lowerCamelCase_	:			bool       			=  field(
		    default=lowercase		,		metadata={
		        """help""": (
		            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
		            """with private models)."""
		        )
		    }		,		)
		lowerCamelCase_	:			float       			=  field(
		    default=0.75		,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}							)
		lowerCamelCase_	:			bool       			=  field(
		    default=lowercase		,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}							)




@dataclass
class 						_UpperCAmelCase      (     lowercase							):
		lowerCamelCase_	:			float       			=  field(
		    default=1E-3		,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""}							)




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						torch.stack([example["pixel_values"] for example in examples] )
		return {"pixel_values": pixel_values}


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
		if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
		else:
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[Any]				     =						parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("run_mae"     , a     , a )

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     , datefmt="%m/%d/%Y %H:%M:%S"     , handlers=[logging.StreamHandler(sys.stdout )]     , )

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		SCREAMING_SNAKE_CASE_				:Tuple				     =						training_args.get_process_log_level()
		logger.setLevel(a )
		transformers.utils.logging.set_verbosity(a )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    F"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
		    + F"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
		logger.info(F"Training/evaluation parameters {training_args}" )

		# Detecting last checkpoint.
		SCREAMING_SNAKE_CASE_				:Any				     =						None
		if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						get_last_checkpoint(training_args.output_dir )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
						raise ValueError(
						    F"Output directory ({training_args.output_dir}) already exists and is not empty. "
						    "Use --overwrite_output_dir to overcome." )
				elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
						logger.info(
						    F"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
						    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Initialize our dataset.
		SCREAMING_SNAKE_CASE_				:int				     =						load_dataset(
		    data_args.dataset_name     , data_args.dataset_config_name     , data_files=data_args.data_files     , cache_dir=model_args.cache_dir     , use_auth_token=True if model_args.use_auth_token else None     , )

		# If we don't have a validation split, split off a percentage of train as validation.
		SCREAMING_SNAKE_CASE_				:Tuple				     =						None if "validation" in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split     , a ) and data_args.train_val_split > 0.0:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ds["train"].train_test_split(data_args.train_val_split )
				SCREAMING_SNAKE_CASE_				:Tuple				     =						split["train"]
				SCREAMING_SNAKE_CASE_				:Any				     =						split["test"]

		# Load pretrained model and image processor
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		SCREAMING_SNAKE_CASE_				:int				     =						{
		    "cache_dir": model_args.cache_dir,
		    "revision": model_args.model_revision,
		    "use_auth_token": True if model_args.use_auth_token else None,
		}
		if model_args.config_name:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						ViTMAEConfig.from_pretrained(model_args.config_name     , **a )
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						ViTMAEConfig.from_pretrained(model_args.model_name_or_path     , **a )
		else:
				SCREAMING_SNAKE_CASE_				:int				     =						ViTMAEConfig()
				logger.warning("You are instantiating a new config instance from scratch." )
				if model_args.config_overrides is not None:
						logger.info(F"Overriding config: {model_args.config_overrides}" )
						config.update_from_string(model_args.config_overrides )
						logger.info(F"New config: {config}" )

    # adapt config
		config.update(
		    {
		        "mask_ratio": model_args.mask_ratio,
		        "norm_pix_loss": model_args.norm_pix_loss,
		    } )

		# create image processor
		if model_args.image_processor_name:
				SCREAMING_SNAKE_CASE_				:Tuple				     =						ViTImageProcessor.from_pretrained(model_args.image_processor_name     , **a )
		elif model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_				:int				     =						ViTImageProcessor.from_pretrained(model_args.model_name_or_path     , **a )
		else:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						ViTImageProcessor()

		# create model
		if model_args.model_name_or_path:
				SCREAMING_SNAKE_CASE_				:List[str]				     =						ViTMAEForPreTraining.from_pretrained(
				    model_args.model_name_or_path     , from_tf=bool(".ckpt" in model_args.model_name_or_path )     , config=a     , cache_dir=model_args.cache_dir     , revision=model_args.model_revision     , use_auth_token=True if model_args.use_auth_token else None     , )
		else:
				logger.info("Training new model from scratch" )
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						ViTMAEForPreTraining(a )

		if training_args.do_train:
				SCREAMING_SNAKE_CASE_				:Any				     =						ds["train"].column_names
		else:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ds["validation"].column_names

		if data_args.image_column_name is not None:
				SCREAMING_SNAKE_CASE_				:Any				     =						data_args.image_column_name
		elif "image" in column_names:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"image"
		elif "img" in column_names:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"img"
		else:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						column_names[0]

		# transformations as done in original MAE paper
		# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
		if "shortest_edge" in image_processor.size:
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						image_processor.size["shortest_edge"]
		else:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(image_processor.size["height"], image_processor.size["width"])
		SCREAMING_SNAKE_CASE_				:List[str]				     =						Compose(
		    [
		        Lambda(lambda a : img.convert("RGB" ) if img.mode != "RGB" else img ),
		        RandomResizedCrop(a     , scale=(0.2, 1.0)     , interpolation=InterpolationMode.BICUBIC ),
		        RandomHorizontalFlip(),
		        ToTensor(),
		        Normalize(mean=image_processor.image_mean     , std=image_processor.image_std ),
		    ] )

		def preprocess_images(a ):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[transforms(a ) for image in examples[image_column_name]]
				return examples

		if training_args.do_train:
				if "train" not in ds:
						raise ValueError("--do_train requires a train dataset" )
				if data_args.max_train_samples is not None:
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
				# Set the training transforms
				ds["train"].set_transform(a )

		if training_args.do_eval:
				if "validation" not in ds:
						raise ValueError("--do_eval requires a validation dataset" )
				if data_args.max_eval_samples is not None:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
						)
				# Set the validation transforms
				ds["validation"].set_transform(a )

		# Compute absolute learning rate
		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						(
		    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
		)
		if training_args.base_learning_rate is not None:
				SCREAMING_SNAKE_CASE_				:Dict				     =						training_args.base_learning_rate * total_train_batch_size / 256

		# Initialize our trainer
		SCREAMING_SNAKE_CASE_				:str				     =						Trainer(
		    model=a     , args=a     , train_dataset=ds["train"] if training_args.do_train else None     , eval_dataset=ds["validation"] if training_args.do_eval else None     , tokenizer=a     , data_collator=a     , )

		# Training
		if training_args.do_train:
				SCREAMING_SNAKE_CASE_				:Dict				     =						None
				if training_args.resume_from_checkpoint is not None:
						SCREAMING_SNAKE_CASE_				:str				     =						training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						SCREAMING_SNAKE_CASE_				:Any				     =						last_checkpoint
				SCREAMING_SNAKE_CASE_				:Dict				     =						trainer.train(resume_from_checkpoint=a )
				trainer.save_model()
				trainer.log_metrics("train"     , train_result.metrics )
				trainer.save_metrics("train"     , train_result.metrics )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				SCREAMING_SNAKE_CASE_				:Dict				     =						trainer.evaluate()
				trainer.log_metrics("eval"     , a )
				trainer.save_metrics("eval"     , a )

		# Write model card and (optionally) push to hub
		SCREAMING_SNAKE_CASE_				:Any				     =						{
		    "tasks": "masked-auto-encoding",
		    "dataset": data_args.dataset_name,
		    "tags": ["masked-auto-encoding"],
		}
		if training_args.push_to_hub:
				trainer.push_to_hub(**a )
		else:
				trainer.create_model_card(**a )


def     lowercase	(    a ):



		'''simple docstring'''




		main()


if __name__ == "__main__":
		main()










import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


SCREAMING_SNAKE_CASE__     					=			False



class 						_UpperCAmelCase      (     unittest.TestCase							):






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Dict=32):
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Dict				     =						UNetaDModel(sample_size=UpperCAmelCase   ,  in_channels=3   ,  out_channels=3)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.optim.SGD(model.parameters()   ,  lr=0.0001)
				return model, optimizer







		@slow
		def _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				SCREAMING_SNAKE_CASE_				:Any				     =						DDPMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:str				     =						DDIMScheduler(
				    num_train_timesteps=10_00   ,  beta_start=0.0001   ,  beta_end=0.02   ,  beta_schedule="linear"   ,  clip_sample=UpperCAmelCase   ,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0)
				SCREAMING_SNAKE_CASE_				:Any				     =						[torch.randn((4, 3, 32, 32)).clip(-1   ,  1).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:str				     =						[torch.randn((4, 3, 32, 32)).to(UpperCAmelCase) for _ in range(4)]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[torch.randint(0   ,  10_00   ,  (4,)).long().to(UpperCAmelCase) for _ in range(4)]

				# train with a DDPM scheduler
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						ddpm_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.get_model_optimizer(resolution=32)
				model.train().to(UpperCAmelCase)
				for i in range(4):
						optimizer.zero_grad()
						SCREAMING_SNAKE_CASE_				:List[str]				     =						ddim_scheduler.add_noise(clean_images[i]   ,  noise[i]   ,  timesteps[i])
						SCREAMING_SNAKE_CASE_				:Tuple				     =						model(UpperCAmelCase   ,  timesteps[i]).sample
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.nn.functional.mse_loss(UpperCAmelCase   ,  noise[i])
						loss.backward()
						optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))
				self.assertTrue(torch.allclose(UpperCAmelCase   ,  UpperCAmelCase   ,  atol=1E-5))






