






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 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       pass







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





       """simple docstring"""







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





       @property
       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							ort.SessionOptions()
         UpperCamelCase_:   int        =							False
         return options





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

         UpperCamelCase_:   Optional[int]        =							'A red cat sitting on a park bench'

         UpperCamelCase_:   Any        =							np.random.RandomState(0				)
         UpperCamelCase_:   Tuple        =							pipe(
             prompt=_lowerCamelCase    ,     image=_lowerCamelCase    ,     mask_image=_lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=1_0    ,     generator=_lowerCamelCase    ,     output_type='np'    ,     )
         UpperCamelCase_:   List[Any]        =							output.images
         UpperCamelCase_:   List[str]        =							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:   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      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'				)
         UpperCamelCase_:   List[Any]        =							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'				)
         UpperCamelCase_:   Tuple        =							LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'    ,     subfolder='scheduler'    ,     revision='onnx'				)
         UpperCamelCase_:   str        =							OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'    ,     revision='onnx'    ,     scheduler=_lowerCamelCase    ,     safety_checker=_lowerCamelCase    ,     feature_extractor=_lowerCamelCase    ,     provider=self.gpu_provider    ,     sess_options=self.gpu_options    ,     )
         pipe.set_progress_bar_config(disable=_lowerCamelCase				)

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

         UpperCamelCase_:   Optional[int]        =							np.random.RandomState(0				)
         UpperCamelCase_:   str        =							pipe(
             prompt=_lowerCamelCase    ,     image=_lowerCamelCase    ,     mask_image=_lowerCamelCase    ,     guidance_scale=7.5    ,     num_inference_steps=2_0    ,     generator=_lowerCamelCase    ,     output_type='np'    ,     )
         UpperCamelCase_:   Optional[Any]        =							output.images
         UpperCamelCase_:   int        =							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         UpperCamelCase_:   Union[str, Any]        =							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






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__      :     Dict         =		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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The column name of the images in the files."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the training data."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		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=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }     ,		)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       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 UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		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=_SCREAMING_SNAKE_CASE     ,		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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Name or path of preprocessor config."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )





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




def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				List[str]:
     '''simple docstring'''


     A_          =       torch.stack([example['''pixel_values'''] for example in examples]				)
     return {"pixel_values": pixel_values}

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     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'''  ,						_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_          =       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_          =       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  ,						_UpperCAmelCase				) 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  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     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  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     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=_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_          =       ViTMAEForPreTraining(_UpperCAmelCase				)

     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 _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							:Optional[Any]				):
          A_          =       [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_          =       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_          =       (
                   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_          =       (
         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=_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_          =       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=_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_          =       trainer.evaluate()
          trainer.log_metrics('''eval'''  ,						_UpperCAmelCase				)
          trainer.save_metrics('''eval'''  ,						_UpperCAmelCase				)

     # 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(**_UpperCAmelCase				)
     else:
          trainer.create_model_card(**_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()



