






"""simple docstring"""



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
A_    =							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 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							field(
							    default='cifar10'       ,		metadata={'help': 'Name of a dataset from the datasets package'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'The column name of the images in the files.'}		)
							snake_case_     =							field(default=UpperCamelCase       ,		metadata={'help': 'A folder containing the training data.'}		)
							snake_case_     =							field(default=UpperCamelCase       ,		metadata={'help': 'A folder containing the validation data.'}		)
							snake_case_     =							field(
							    default=0.1_5       ,		metadata={'help': 'Percent to split off of train for validation.'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of training examples to this '
							            'value if set.'
							        )
							    }       ,		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={
							        'help': (
							            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
							            'value if set.'
							        )
							    }       ,		)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													A__						:  str		  =  {}
													if self.train_dir is not None:
																			A__						:  Optional[Any]		  =  self.train_dir
													if self.validation_dir is not None:
																			A__						:  Optional[int]		  =  self.validation_dir
													A__						:  List[Any]		  =  data_files if data_files else None






@dataclass
class 							__SCREAMING_SNAKE_CASE						:
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={
							        'help': (
							            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
							        )
							    }       ,		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		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'
							        )
							    }       ,		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}		)
							snake_case_     =							field(
							    default='main'       ,		metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}       ,		)
							snake_case_     =							field(default=UpperCamelCase       ,		metadata={'help': 'Name or path of preprocessor config.'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={
							        'help': (
							            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
							            'with private models).'
							        )
							    }       ,		)
							snake_case_     =							field(
							    default=0.7_5       ,		metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}		)
							snake_case_     =							field(
							    default=UpperCamelCase       ,		metadata={'help': 'Whether or not to train with normalized pixel values as target.'}		)






@dataclass
class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							field(
							    default=1E-3       ,		metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}		)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str      )		->str:
						A__						:  int		  =  torch.stack([example["""pixel_values"""] for example in examples]      )
						return {"pixel_values": pixel_values}




def      _lowerCAmelCase		(							)		->str:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						A__						:  Optional[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.
												A__  ,  A__  ,  A__						:  Union[str, Any]		  =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
						else:
												A__  ,  A__  ,  A__						:  Optional[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""",						UpperCAmelCase__,						UpperCAmelCase__      )

						# 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()

						A__						:  Optional[Any]		  =  training_args.get_process_log_level()
						logger.setLevel(UpperCAmelCase__      )
						transformers.utils.logging.set_verbosity(UpperCAmelCase__      )
						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.
						A__						:  int		  =  None
						if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
												A__						:  Union[str, 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.
						A__						:  str		  =  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.
						A__						:  Tuple		  =  None if """validation""" in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split,						UpperCAmelCase__      ) and data_args.train_val_split > 0.0:
												A__						:  Tuple		  =  ds["""train"""].train_test_split(data_args.train_val_split      )
												A__						:  Optional[Any]		  =  split["""train"""]
												A__						:  List[str]		  =  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.
						A__						:  Dict		  =  {
						    """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:
												A__						:  Dict		  =  ViTMAEConfig.from_pretrained(model_args.config_name,						**UpperCAmelCase__      )
						elif model_args.model_name_or_path:
												A__						:  Optional[Any]		  =  ViTMAEConfig.from_pretrained(model_args.model_name_or_path,						**UpperCAmelCase__      )
						else:
												A__						:  Optional[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:
												A__						:  List[str]		  =  ViTImageProcessor.from_pretrained(model_args.image_processor_name,						**UpperCAmelCase__      )
						elif model_args.model_name_or_path:
												A__						:  List[Any]		  =  ViTImageProcessor.from_pretrained(model_args.model_name_or_path,						**UpperCAmelCase__      )
						else:
												A__						:  Optional[Any]		  =  ViTImageProcessor()

						# create model
						if model_args.model_name_or_path:
												A__						:  List[str]		  =  ViTMAEForPreTraining.from_pretrained(
												    model_args.model_name_or_path,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path      ),						config=UpperCAmelCase__,						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"""      )
												A__						:  Optional[Any]		  =  ViTMAEForPreTraining(UpperCAmelCase__      )

						if training_args.do_train:
												A__						:  Dict		  =  ds["""train"""].column_names
						else:
												A__						:  Optional[int]		  =  ds["""validation"""].column_names

						if data_args.image_column_name is not None:
												A__						:  Tuple		  =  data_args.image_column_name
						elif "image" in column_names:
												A__						:  Optional[int]		  =  """image"""
						elif "img" in column_names:
												A__						:  Optional[int]		  =  """img"""
						else:
												A__						:  Tuple		  =  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:
												A__						:  List[str]		  =  image_processor.size["""shortest_edge"""]
						else:
												A__						:  List[Any]		  =  (image_processor.size["""height"""], image_processor.size["""width"""])
						A__						:  Tuple		  =  Compose(
						    [
						        Lambda(lambda UpperCAmelCase__      : img.convert("""RGB"""      ) if img.mode != "RGB" else img      ),
						        RandomResizedCrop(UpperCAmelCase__,						scale=(0.2, 1.0),						interpolation=InterpolationMode.BICUBIC      ),
						        RandomHorizontalFlip(),
						        ToTensor(),
						        Normalize(mean=image_processor.image_mean,						std=image_processor.image_std      ),
						    ]      )

						def preprocess_images(UpperCAmelCase__       :					List[Any]      ):
												A__						:  Optional[int]		  =  [transforms(UpperCAmelCase__      ) 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:
																		A__						:  List[Any]		  =  ds["""train"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
												# Set the training transforms
												ds["train"].set_transform(UpperCAmelCase__      )

						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:
																		A__						:  Optional[int]		  =  (
																		    ds["""validation"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
																		)
												# Set the validation transforms
												ds["validation"].set_transform(UpperCAmelCase__      )

						# Compute absolute learning rate
						A__						:  str		  =  (
						    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
						)
						if training_args.base_learning_rate is not None:
												A__						:  Union[str, Any]		  =  training_args.base_learning_rate * total_train_batch_size / 2_5_6

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

						# Training
						if training_args.do_train:
												A__						:  int		  =  None
												if training_args.resume_from_checkpoint is not None:
																		A__						:  Tuple		  =  training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A__						:  List[str]		  =  last_checkpoint
												A__						:  Any		  =  trainer.train(resume_from_checkpoint=UpperCAmelCase__      )
												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:
												A__						:  List[Any]		  =  trainer.evaluate()
												trainer.log_metrics("""eval""",						UpperCAmelCase__      )
												trainer.save_metrics("""eval""",						UpperCAmelCase__      )

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any      )		->str:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
						main()














"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							42
							snake_case_     =							42


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						UNetaDModel			,    snake_case      :						KarrasVeScheduler							):
													'''simple docstring'''





													super().__init__()
													self.register_modules(unet=snake_case			,    scheduler=snake_case							)


							@torch.no_grad()
							def __call__(			self      :						Any			,    snake_case      :						int = 1			,    snake_case      :						int = 50			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[int]		  =  self.unet.config.sample_size
													A__						:  Dict		  =  (batch_size, 3, img_size, img_size)

													A__						:  Tuple		  =  self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													A__						:  Union[str, Any]		  =  randn_tensor(snake_case			,    generator=snake_case			,    device=self.device							) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(snake_case							)

													for t in self.progress_bar(self.scheduler.timesteps							):
																			# here sigma_t == t_i from the paper
																			A__						:  Optional[Any]		  =  self.scheduler.schedule[t]
																			A__						:  Union[str, Any]		  =  self.scheduler.schedule[t - 1] if t > 0 else 0

																			# 1. Select temporarily increased noise level sigma_hat
																			# 2. Add new noise to move from sample_i to sample_hat
																			A__  ,  A__						:  Dict		  =  self.scheduler.add_noise_to_input(snake_case			,    snake_case			,    generator=snake_case							)

																			# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																			# The model inputs and output are adjusted by following eq. (213) in [1].
																			A__						:  int		  =  (sigma_hat / 2) * model((sample_hat + 1) / 2			,    sigma_hat / 2							).sample

																			# 4. Evaluate dx/dt at sigma_hat
																			# 5. Take Euler step from sigma to sigma_prev
																			A__						:  Tuple		  =  self.scheduler.step(snake_case			,    snake_case			,    snake_case			,    snake_case							)

																			if sigma_prev != 0:
																									# 6. Apply 2nd order correction
																									# The model inputs and output are adjusted by following eq. (213) in [1].
																									A__						:  Dict		  =  (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2			,    sigma_prev / 2							).sample
																									A__						:  Tuple		  =  self.scheduler.step_correct(
																									    snake_case			,    snake_case			,    snake_case			,    snake_case			,    step_output.prev_sample			,    step_output["""derivative"""]			,    )
																			A__						:  Tuple		  =  step_output.prev_sample

													A__						:  Dict		  =  (sample / 2 + 0.5).clamp(0			,    1							)
													A__						:  Dict		  =  sample.cpu().permute(0			,    2			,    3			,    1							).numpy()
													if output_type == "pil":
																			A__						:  Optional[Any]		  =  self.numpy_to_pil(snake_case							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=snake_case							)






