
"""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
_lowercase							   =				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 				lowerCAmelCase_				:



						'''simple docstring'''


						_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 						_SCREAMING_SNAKE_CASE      ( self     :      int      )     ->   Dict:
												A						   =   {}
												if self.train_dir is not None:
																		A						   =   self.train_dir
												if self.validation_dir is not None:
																		A						   =   self.validation_dir
												A						   =   data_files if data_files else None







@dataclass
class 				lowerCAmelCase_				:



						'''simple docstring'''


						_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 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    float      =    field(
						    default=1E-3		,					metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}    )






def       _snake_case				(		snake_case__   :     Union[str, Any]     ):
						A						   =   torch.stack([example['pixel_values'] for example in examples]     )
						return {"pixel_values": pixel_values}






def       _snake_case				(		):
						# 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						   =   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						   =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
						else:
												A   ,      A   ,      A						   =   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'	,      snake_case__	,      snake_case__     )

						# 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						   =   training_args.get_process_log_level()
						logger.setLevel(snake_case__     )
						transformers.utils.logging.set_verbosity(snake_case__     )
						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						   =   None
						if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
												A						   =   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						   =   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						   =   None if 'validation' in ds.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split	,      snake_case__     ) and data_args.train_val_split > 0.0:
												A						   =   ds['train'].train_test_split(data_args.train_val_split     )
												A						   =   split['train']
												A						   =   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						   =   {
						    '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						   =   ViTMAEConfig.from_pretrained(model_args.config_name	,      **snake_case__     )
						elif model_args.model_name_or_path:
												A						   =   ViTMAEConfig.from_pretrained(model_args.model_name_or_path	,      **snake_case__     )
						else:
												A						   =   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						   =   ViTImageProcessor.from_pretrained(model_args.image_processor_name	,      **snake_case__     )
						elif model_args.model_name_or_path:
												A						   =   ViTImageProcessor.from_pretrained(model_args.model_name_or_path	,      **snake_case__     )
						else:
												A						   =   ViTImageProcessor()

						# create model
						if model_args.model_name_or_path:
												A						   =   ViTMAEForPreTraining.from_pretrained(
												    model_args.model_name_or_path	,      from_tf=bool('.ckpt' in model_args.model_name_or_path     )	,      config=snake_case__	,      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						   =   ViTMAEForPreTraining(snake_case__     )

						if training_args.do_train:
												A						   =   ds['train'].column_names
						else:
												A						   =   ds['validation'].column_names

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

						def preprocess_images(snake_case__   :     Dict     ):
												A						   =   [transforms(snake_case__     ) 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						   =   ds['train'].shuffle(seed=training_args.seed     ).select(range(data_args.max_train_samples     )     )
												# Set the training transforms
												ds["train"].set_transform(snake_case__     )

						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						   =   (
																		    ds['validation'].shuffle(seed=training_args.seed     ).select(range(data_args.max_eval_samples     )     )
																		)
												# Set the validation transforms
												ds["validation"].set_transform(snake_case__     )

						# Compute absolute learning rate
						A						   =   (
						    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
						)
						if training_args.base_learning_rate is not None:
												A						   =   training_args.base_learning_rate * total_train_batch_size / 256

						# Initialize our trainer
						A						   =   Trainer(
						    model=snake_case__	,      args=snake_case__	,      train_dataset=ds['train'] if training_args.do_train else None	,      eval_dataset=ds['validation'] if training_args.do_eval else None	,      tokenizer=snake_case__	,      data_collator=snake_case__	,      )

						# Training
						if training_args.do_train:
												A						   =   None
												if training_args.resume_from_checkpoint is not None:
																		A						   =   training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		A						   =   last_checkpoint
												A						   =   trainer.train(resume_from_checkpoint=snake_case__     )
												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						   =   trainer.evaluate()
												trainer.log_metrics('eval'	,      snake_case__     )
												trainer.save_metrics('eval'	,      snake_case__     )

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






def       _snake_case				(		snake_case__   :     Optional[int]     ):
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
		main()

"""simple docstring"""


from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


_lowercase							   =				8






def       _snake_case				(		snake_case__   :     Tuple	,      snake_case__   :     Optional[int]=BITS     ):
	A						   =   x.device

	A						   =   (x * 255).int().clamp(0	,      255     )

	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__     )
	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b c h w -> b c 1 h w'     )

	A						   =   ((x & mask) != 0).float()
	A						   =   rearrange(snake_case__	,      'b c d h w -> b (c d) h w'     )
	A						   =   bits * 2 - 1
	return bits






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Any=BITS     ):
	A						   =   x.device

	A						   =   (x > 0).int()
	A						   =   2 ** torch.arange(bits - 1	,      -1	,      -1	,      device=snake_case__	,      dtype=torch.intaa     )

	A						   =   rearrange(snake_case__	,      'd -> d 1 1'     )
	A						   =   rearrange(snake_case__	,      'b (c d) h w -> b c d h w'	,      d=8     )
	A						   =   reduce(x * mask	,      'b c d h w -> b c h w'	,      'sum'     )
	return (dec / 255).clamp(0.0	,      1.0     )






def       _snake_case				(		self   :     Optional[int]	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     float = 0.0	,      snake_case__   :     bool = True	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	if self.num_inference_steps is None:
		raise ValueError(
		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'     )

	# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
	# Ideally, read DDIM paper in-detail understanding

	# Notation (<variable name> -> <name in paper>
	# - pred_noise_t -> e_theta(x_t, t)
	# - pred_original_sample -> f_theta(x_t, t) or x_0
	# - std_dev_t -> sigma_t
	# - eta -> η
	# - pred_sample_direction -> "direction pointing to x_t"
	# - pred_prev_sample -> "x_t-1"

	# 1. get previous step value (=t-1)
	A						   =   timestep - self.config.num_train_timesteps // self.num_inference_steps

	# 2. compute alphas, betas
	A						   =   self.alphas_cumprod[timestep]
	A						   =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

	A						   =   1 - alpha_prod_t

	# 3. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

	# 4. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 5. compute variance: "sigma_t(η)" -> see formula (16)
	# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
	A						   =   self._get_variance(snake_case__	,      snake_case__     )
	A						   =   eta * variance ** 0.5

	if use_clipped_model_output:
		# the model_output is always re-derived from the clipped x_0 in Glide
		A						   =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

	# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

	# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
	A						   =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

	if eta > 0:
		# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
		A						   =   model_output.device if torch.is_tensor(snake_case__     ) else 'cpu'
		A						   =   torch.randn(model_output.shape	,      dtype=model_output.dtype	,      generator=snake_case__     ).to(snake_case__     )
		A						   =   self._get_variance(snake_case__	,      snake_case__     ) ** 0.5 * eta * noise

		A						   =   prev_sample + variance

	if not return_dict:
		return (prev_sample,)

	return DDIMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )






def       _snake_case				(		self   :     Dict	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     int	,      snake_case__   :     torch.FloatTensor	,      snake_case__   :     Tuple="epsilon"	,      snake_case__   :     List[str]=None	,      snake_case__   :     bool = True	,      ):
	A						   =   timestep

	if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
		A   ,      A						   =   torch.split(snake_case__	,      sample.shape[1]	,      dim=1     )
	else:
		A						   =   None

	# 1. compute alphas, betas
	A						   =   self.alphas_cumprod[t]
	A						   =   self.alphas_cumprod[t - 1] if t > 0 else self.one
	A						   =   1 - alpha_prod_t
	A						   =   1 - alpha_prod_t_prev

	# 2. compute predicted original sample from predicted noise also called
	# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
	if prediction_type == "epsilon":
		A						   =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
	elif prediction_type == "sample":
		A						   =   model_output
	else:
		raise ValueError(F'Unsupported prediction_type {prediction_type}.'     )

	# 3. Clip "predicted x_0"
	A						   =   self.bit_scale
	if self.config.clip_sample:
		A						   =   torch.clamp(snake_case__	,      -scale	,      snake_case__     )

	# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
	A						   =   self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

	# 5. Compute predicted previous sample µ_t
	# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
	A						   =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

	# 6. Add noise
	A						   =   0
	if t > 0:
		A						   =   torch.randn(
		    model_output.size()	,      dtype=model_output.dtype	,      layout=model_output.layout	,      generator=snake_case__     ).to(model_output.device     )
		A						   =   (self._get_variance(snake_case__	,      predicted_variance=snake_case__     ) ** 0.5) * noise

	A						   =   pred_prev_sample + variance

	if not return_dict:
		return (pred_prev_sample,)

	return DDPMSchedulerOutput(prev_sample=snake_case__	,      pred_original_sample=snake_case__     )







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						def __init__( self     :      Union[str, Any]  ,A_     :      UNetaDConditionModel  ,A_     :      Union[DDIMScheduler, DDPMScheduler]  ,A_     :      Optional[float] = 1.0  ,)     ->   Optional[int]:
							super().__init__()
							A						   =   bit_scale
							A						   =   (
							    ddim_bit_scheduler_step if isinstance(A_  ,A_      ) else ddpm_bit_scheduler_step
							)

							self.register_modules(unet=A_  ,scheduler=A_      )




						@torch.no_grad()
						def __call__( self     :      Tuple  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 256  ,A_     :      Optional[int] = 50  ,A_     :      Optional[torch.Generator] = None  ,A_     :      Optional[int] = 1  ,A_     :      Optional[str] = "pil"  ,A_     :      bool = True  ,**A_     :      Optional[Any]  ,)     ->   Union[Tuple, ImagePipelineOutput]:
							A						   =   torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)  ,generator=A_  ,)
							A						   =   decimal_to_bits(A_      ) * self.bit_scale
							A						   =   latents.to(self.device      )

							self.scheduler.set_timesteps(A_      )

							for t in self.progress_bar(self.scheduler.timesteps      ):
								# predict the noise residual
								A						   =   self.unet(A_  ,A_      ).sample

								# compute the previous noisy sample x_t -> x_t-1
								A						   =   self.scheduler.step(A_  ,A_  ,A_      ).prev_sample

							A						   =   bits_to_decimal(A_      )

							if output_type == "pil":
								A						   =   self.numpy_to_pil(A_      )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_      )