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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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')

__a  			=					list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
__a  			=					tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:Optional[str]         =	field(
	    default='cifar10' ,						metadata={'help': 'Name of a dataset from the datasets package'}							)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}							)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'} ,						)
	a			:Optional[str]         =	field(default=UpperCAmelCase ,						metadata={'help': 'A folder containing the training data.'}							)
	a			:Optional[str]         =	field(default=UpperCAmelCase ,						metadata={'help': 'A folder containing the validation data.'}							)
	a			:Optional[float]         =	field(
	    default=0.15 ,						metadata={'help': 'Percent to split off of train for validation.'}							)
	a			:int         =	field(default=32 ,						metadata={'help': 'The size of the square patches to use for masking.'}							)
	a			:float         =	field(
	    default=0.6 ,						metadata={'help': 'Percentage of patches to mask.'} ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of training examples to this '
	            'value if set.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    } ,						)
	def 						_lowercase  (       self  :			List[str]       )       ->     Tuple:
					lowercase_       =					{}
					if self.train_dir is not None:
									lowercase_       =					self.train_dir
					if self.validation_dir is not None:
									lowercase_       =					self.validation_dir
					lowercase_       =					data_files if data_files else None

@dataclass
class  lowercase__:



	"""simple docstring"""




	a			:str         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
	            'checkpoint identifier on the hub. '
	            'Don\'t set if you want to train a model from scratch.'
	        )
	    } ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(UpperCAmelCase							)} ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}							)
	a			:Optional[str]         =	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'
	        )
	    } ,						)
	a			:Optional[str]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'} ,						)
	a			:str         =	field(
	    default='main' ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,						)
	a			:str         =	field(default=UpperCAmelCase ,						metadata={'help': 'Name or path of preprocessor config.'}							)
	a			:bool         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
	            'with private models).'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={
	        'help': (
	            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
	        )
	    } ,						)
	a			:Optional[int]         =	field(
	    default=UpperCAmelCase ,						metadata={'help': 'Stride to use for the encoder.'} ,						)

class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_9_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=3_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=4       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.6       )       ->     List[str]:
					lowercase_       =					input_size
					lowercase_       =					mask_patch_size
					lowercase_       =					model_patch_size
					lowercase_       =					mask_ratio

					if self.input_size % self.mask_patch_size != 0:
									raise ValueError('''Input size must be divisible by mask patch size'''       )
					if self.mask_patch_size % self.model_patch_size != 0:
									raise ValueError('''Mask patch size must be divisible by model patch size'''       )

					lowercase_       =					self.input_size // self.mask_patch_size
					lowercase_       =					self.mask_patch_size // self.model_patch_size

					lowercase_       =					self.rand_size**2
					lowercase_       =					int(np.ceil(self.token_count * self.mask_ratio       )       )




	def __call__(       self  :			List[str]       )       ->     List[Any]:
					lowercase_       =					np.random.permutation(self.token_count       )[: self.mask_count]
					lowercase_       =					np.zeros(self.token_count       ,							dtype=SCREAMING_SNAKE_CASE_       )
					lowercase_       =					1

					lowercase_       =					mask.reshape((self.rand_size, self.rand_size)       )
					lowercase_       =					mask.repeat(self.scale       ,							axis=0       ).repeat(self.scale       ,							axis=1       )

					return torch.tensor(mask.flatten()       )




def 				a						(							snake_case__:					str					):


				'''simple docstring'''
				lowercase_       =					torch.stack([example['''pixel_values'''] for example in examples]					)
				lowercase_       =					torch.stack([example['''mask'''] for example in examples]					)
				return {"pixel_values": pixel_values, "bool_masked_pos": mask}




def 				a						(							):


				'''simple docstring'''
				# 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.

				lowercase_       =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
				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.
								lowercase_  ,	lowercase_  ,	lowercase_       =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
				else:
								lowercase_  ,	lowercase_  ,	lowercase_       =					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_mim'''						,   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()

				lowercase_       =					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.
				lowercase_       =					None
				if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase_       =					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.
				lowercase_       =					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.
				lowercase_       =					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:
								lowercase_       =					ds['''train'''].train_test_split(data_args.train_val_split					)
								lowercase_       =					split['''train''']
								lowercase_       =					split['''test''']

				# Create config
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				lowercase_       =					{
				    '''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_or_path:
								lowercase_       =					AutoConfig.from_pretrained(model_args.config_name_or_path						,   **snake_case__					)
				elif model_args.model_name_or_path:
								lowercase_       =					AutoConfig.from_pretrained(model_args.model_name_or_path						,   **snake_case__					)
				else:
								lowercase_       =					CONFIG_MAPPING[model_args.model_type]()
								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}'''					)

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
				if hasattr(snake_case__						,   '''decoder_type'''					):
								lowercase_       =					'''simmim'''

				# adapt config
				lowercase_       =					model_args.image_size if model_args.image_size is not None else config.image_size
				lowercase_       =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
				lowercase_       =					(
				    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
				)

				config.update(
				    {
				        '''image_size''': model_args.image_size,
				        '''patch_size''': model_args.patch_size,
				        '''encoder_stride''': model_args.encoder_stride,
				    }					)

				# create image processor
				if model_args.image_processor_name:
								lowercase_       =					AutoImageProcessor.from_pretrained(model_args.image_processor_name						,   **snake_case__					)
				elif model_args.model_name_or_path:
								lowercase_       =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,   **snake_case__					)
				else:
								lowercase_       =					{
								    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
								}
								lowercase_       =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

				# create model
				if model_args.model_name_or_path:
								lowercase_       =					AutoModelForMaskedImageModeling.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'''					)
								lowercase_       =					AutoModelForMaskedImageModeling.from_config(snake_case__					)

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

				if data_args.image_column_name is not None:
								lowercase_       =					data_args.image_column_name
				elif "image" in column_names:
								lowercase_       =					'''image'''
				elif "img" in column_names:
								lowercase_       =					'''img'''
				else:
								lowercase_       =					column_names[0]

				# transformations as done in original SimMIM paper
				# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
				lowercase_       =					Compose(
				    [
				        Lambda(lambda snake_case__					: img.convert('''RGB'''					) if img.mode != "RGB" else img					),
				        RandomResizedCrop(model_args.image_size						,   scale=(0.6_7, 1.0)						,   ratio=(3.0 / 4.0, 4.0 / 3.0)					),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean						,   std=image_processor.image_std					),
				    ]					)

				# create mask generator
				lowercase_       =					MaskGenerator(
				    input_size=model_args.image_size						,   mask_patch_size=data_args.mask_patch_size						,   model_patch_size=model_args.patch_size						,   mask_ratio=data_args.mask_ratio						,   )

				def preprocess_images(snake_case__:					Optional[int]					):
								lowercase_       =					[transforms(snake_case__					) for image in examples[image_column_name]]
								lowercase_       =					[mask_generator() for i in range(len(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:
												lowercase_       =					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:
												lowercase_       =					(
												    ds['''validation'''].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
												)
								# Set the validation transforms
								ds["validation"].set_transform(snake_case__					)

				# Initialize our trainer
				lowercase_       =					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:
								lowercase_       =					None
								if training_args.resume_from_checkpoint is not None:
												lowercase_       =					training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase_       =					last_checkpoint
								lowercase_       =					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:
								lowercase_       =					trainer.evaluate()
								trainer.log_metrics('''eval'''						,   snake_case__					)
								trainer.save_metrics('''eval'''						,   snake_case__					)

				# Write model card and (optionally) push to hub
				lowercase_       =					{
				    '''finetuned_from''': model_args.model_name_or_path,
				    '''tasks''': '''masked-image-modeling''',
				    '''dataset''': data_args.dataset_name,
				    '''tags''': ['''masked-image-modeling'''],
				}
				if training_args.push_to_hub:
								trainer.push_to_hub(**snake_case__					)
				else:
								trainer.create_model_card(**snake_case__					)


if __name__ == "__main__":
						main()








'''simple docstring'''



import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort





class    UpperCAmelCase_  (_UpperCAmelCase					,		unittest.TestCase  ):





    """simple docstring"""






    pass







@nightly
@require_onnxruntime
@require_torch_gpu
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @property
    def       lowercase_				(							self	)				->     int:
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )







    @property
    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	int								=		ort.SessionOptions()
         __lowerCamelCase   :	Optional[Any]								=		False
         return options







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	Any								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Any								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Tuple								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Union[str, Any]								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Union[str, Any]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=10			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	Optional[Any]								=		output.images
         __lowerCamelCase   :	Tuple								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Union[str, Any]								=		np.array([0.2_5_1_4, 0.3_0_0_7, 0.3_5_1_7, 0.1_7_9_0, 0.2_3_8_2, 0.3_1_6_7, 0.1_9_4_4, 0.2_2_7_3, 0.2_4_6_4]	)

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







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	Union[str, Any]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'	)
         __lowerCamelCase   :	List[str]								=		load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'	)
         __lowerCamelCase   :	Dict								=		LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     subfolder='scheduler'			,     revision='onnx'	)
         __lowerCamelCase   :	Tuple								=		OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'			,     revision='onnx'			,     scheduler=SCREAMING_SNAKE_CASE_			,     safety_checker=SCREAMING_SNAKE_CASE_			,     feature_extractor=SCREAMING_SNAKE_CASE_			,     provider=self.gpu_provider			,     sess_options=self.gpu_options			,     )
         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		'A red cat sitting on a park bench'

         __lowerCamelCase   :	Tuple								=		np.random.RandomState(0	)
         __lowerCamelCase   :	Optional[int]								=		pipe(
             prompt=SCREAMING_SNAKE_CASE_			,     image=SCREAMING_SNAKE_CASE_			,     mask_image=SCREAMING_SNAKE_CASE_			,     guidance_scale=7.5			,     num_inference_steps=20			,     generator=SCREAMING_SNAKE_CASE_			,     output_type='np'			,     )
         __lowerCamelCase   :	List[str]								=		output.images
         __lowerCamelCase   :	Dict								=		images[0, 2_55:2_58, 2_55:2_58, -1]

         assert images.shape == (1, 5_12, 5_12, 3)
         __lowerCamelCase   :	Tuple								=		np.array([0.0_0_8_6, 0.0_0_7_7, 0.0_0_8_3, 0.0_0_9_3, 0.0_1_0_7, 0.0_1_3_9, 0.0_0_9_4, 0.0_0_9_7, 0.0_1_2_5]	)

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






