






"""simple docstring"""







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
lowerCAmelCase_	:    Tuple            =      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''')

lowerCAmelCase_	:    int            =      list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCAmelCase_	:    str            =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





@dataclass
class   UpperCamelCase_      :
  _A       :					Optional[str]				=	field(
      default='cifar10'   ,					metadata={'help': 'Name of a dataset from the datasets package'}  )
  _A       :					Optional[str]				=	field(
      default=a_   ,					metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}  )
  _A       :					Optional[str]				=	field(
      default=a_   ,					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=a_   ,					metadata={'help': 'A folder containing the training data.'}  )
  _A       :					Optional[str]				=	field(default=a_   ,					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=a_   ,					metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of training examples to this '
              'value if set.'
          )
      }   ,					)
  _A       :					Optional[int]				=	field(
      default=a_   ,					metadata={
          'help': (
              'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
              'value if set.'
          )
      }   ,					)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			{}
         if self.train_dir is not None:
                UpperCAmelCase	    =			self.train_dir
         if self.validation_dir is not None:
                UpperCAmelCase	    =			self.validation_dir
         UpperCAmelCase	    =			data_files if data_files else None





@dataclass
class   UpperCamelCase_      :
  _A       :					str				=	field(
      default=a_   ,					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=a_   ,					metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(a_  )}   ,					)
  _A       :					Optional[str]				=	field(
      default=a_   ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'}  )
  _A       :					Optional[str]				=	field(
      default=a_   ,					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=a_   ,					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=a_   ,					metadata={'help': 'Name or path of preprocessor config.'}  )
  _A       :					bool				=	field(
      default=a_   ,					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=a_   ,					metadata={
          'help': (
              'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
          )
      }   ,					)
  _A       :					Optional[int]				=	field(
      default=a_   ,					metadata={
          'help': (
              'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
          )
      }   ,					)
  _A       :					Optional[int]				=	field(
      default=a_   ,					metadata={'help': 'Stride to use for the encoder.'}   ,					)





class   UpperCamelCase_      :



  def __init__(      self ,		snake_case__=1_92 ,		snake_case__=32 ,		snake_case__=4 ,		snake_case__=0.6				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			input_size
         UpperCAmelCase	    =			mask_patch_size
         UpperCAmelCase	    =			model_patch_size
         UpperCAmelCase	    =			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"""				)

         UpperCAmelCase	    =			self.input_size // self.mask_patch_size
         UpperCAmelCase	    =			self.mask_patch_size // self.model_patch_size

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



  def __call__(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			np.random.permutation(self.token_count				)[: self.mask_count]
         UpperCAmelCase	    =			np.zeros(self.token_count ,		dtype=snake_case__				)
         UpperCAmelCase	    =			1

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

         return torch.tensor(mask.flatten()				)






def       _lowerCAmelCase       (      lowerCAmelCase		):







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






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			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.
              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]		)		)
       else:
              UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase	    =			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"""	,      lowerCAmelCase	,      lowerCAmelCase		)

       # 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()

       UpperCAmelCase	    =			training_args.get_process_log_level()
       logger.setLevel(lowerCAmelCase		)
       transformers.utils.logging.set_verbosity(lowerCAmelCase		)
       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.
       UpperCAmelCase	    =			None
       if os.path.isdir(training_args.output_dir		) and training_args.do_train and not training_args.overwrite_output_dir:
              UpperCAmelCase	    =			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.
       UpperCAmelCase	    =			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.
       UpperCAmelCase	    =			None if """validation""" in ds.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split	,      lowerCAmelCase		) and data_args.train_val_split > 0.0:
              UpperCAmelCase	    =			ds["""train"""].train_test_split(data_args.train_val_split		)
              UpperCAmelCase	    =			split["""train"""]
              UpperCAmelCase	    =			split["""test"""]

       # Create config
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       UpperCAmelCase	    =			{
           """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:
              UpperCAmelCase	    =			AutoConfig.from_pretrained(model_args.config_name_or_path	,      **lowerCAmelCase		)
       elif model_args.model_name_or_path:
              UpperCAmelCase	    =			AutoConfig.from_pretrained(model_args.model_name_or_path	,      **lowerCAmelCase		)
       else:
              UpperCAmelCase	    =			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(lowerCAmelCase	,      """decoder_type"""		):
              UpperCAmelCase	    =			"""simmim"""

       # adapt config
       UpperCAmelCase	    =			model_args.image_size if model_args.image_size is not None else config.image_size
       UpperCAmelCase	    =			model_args.patch_size if model_args.patch_size is not None else config.patch_size
       UpperCAmelCase	    =			(
           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:
              UpperCAmelCase	    =			AutoImageProcessor.from_pretrained(model_args.image_processor_name	,      **lowerCAmelCase		)
       elif model_args.model_name_or_path:
              UpperCAmelCase	    =			AutoImageProcessor.from_pretrained(model_args.model_name_or_path	,      **lowerCAmelCase		)
       else:
              UpperCAmelCase	    =			{
                  conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
              }
              UpperCAmelCase	    =			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

       # create model
       if model_args.model_name_or_path:
              UpperCAmelCase	    =			AutoModelForMaskedImageModeling.from_pretrained(
                  model_args.model_name_or_path	,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path		)	,      config=lowerCAmelCase	,      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"""		)
              UpperCAmelCase	    =			AutoModelForMaskedImageModeling.from_config(lowerCAmelCase		)

       if training_args.do_train:
              UpperCAmelCase	    =			ds["""train"""].column_names
       else:
              UpperCAmelCase	    =			ds["""validation"""].column_names

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

       # transformations as done in original SimMIM paper
       # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
       UpperCAmelCase	    =			Compose(
           [
               Lambda(lambda lowerCAmelCase		: img.convert("""RGB"""		) if img.mode != "RGB" else img		),
               RandomResizedCrop(model_args.image_size	,      scale=(0.67, 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
       UpperCAmelCase	    =			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(lowerCAmelCase		):
              UpperCAmelCase	    =			[transforms(lowerCAmelCase		) for image in examples[image_column_name]]
              UpperCAmelCase	    =			[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:
                     UpperCAmelCase	    =			ds["""train"""].shuffle(seed=training_args.seed		).select(range(data_args.max_train_samples		)		)
              # Set the training transforms
              ds["train"].set_transform(lowerCAmelCase		)

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

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

       # Training
       if training_args.do_train:
              UpperCAmelCase	    =			None
              if training_args.resume_from_checkpoint is not None:
                     UpperCAmelCase	    =			training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     UpperCAmelCase	    =			last_checkpoint
              UpperCAmelCase	    =			trainer.train(resume_from_checkpoint=lowerCAmelCase		)
              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:
              UpperCAmelCase	    =			trainer.evaluate()
              trainer.log_metrics("""eval"""	,      lowerCAmelCase		)
              trainer.save_metrics("""eval"""	,      lowerCAmelCase		)

       # Write model card and (optionally) push to hub
       UpperCAmelCase	    =			{
           """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(**lowerCAmelCase		)
       else:
              trainer.create_model_card(**lowerCAmelCase		)


if __name__ == "__main__":
 main()








"""simple docstring"""







import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor





class   UpperCamelCase_ (       unittest.TestCase  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 32 ,		snake_case__ = True ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = [0.48_145_466, 0.4_578_275, 0.40_821_073] ,		snake_case__ = [0.26_862_954, 0.26_130_258, 0.27_577_711] ,		snake_case__ = True ,		snake_case__=7 ,		snake_case__=30 ,		snake_case__=4_00 ,		snake_case__=3 ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			parent
         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_88}
         UpperCAmelCase	    =			size_divisor
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			image_mean
         UpperCAmelCase	    =			image_std
         UpperCAmelCase	    =			do_pad
         UpperCAmelCase	    =			batch_size
         UpperCAmelCase	    =			num_channels
         UpperCAmelCase	    =			min_resolution
         UpperCAmelCase	    =			max_resolution



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=False				)			->			Optional[int]:



         """simple docstring"""





         if not batched:
                UpperCAmelCase	    =			self.size["""shortest_edge"""]
                UpperCAmelCase	    =			image_inputs[0]
                if isinstance(snake_case__ ,		Image.Image				):
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.size
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			image.shape[1], image.shape[2]
                UpperCAmelCase	    =			size / min(snake_case__ ,		snake_case__				)
                if h < w:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			size, scale * w
                else:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			scale * h, size

                UpperCAmelCase	    =			int((13_33 / 8_00) * size				)
                if max(snake_case__ ,		snake_case__				) > max_size:
                       UpperCAmelCase	    =			max_size / max(snake_case__ ,		snake_case__				)
                       UpperCAmelCase	    =			newh * scale
                       UpperCAmelCase	    =			neww * scale

                UpperCAmelCase    ,       UpperCAmelCase	    =			int(newh + 0.5				), int(neww + 0.5				)
                UpperCAmelCase    ,       UpperCAmelCase	    =			(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

         else:
                UpperCAmelCase	    =			[]
                for image in image_inputs:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_expected_values([image]				)
                       expected_values.append((expected_height, expected_width)				)
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[0]				)[0]
                UpperCAmelCase	    =			max(snake_case__ ,		key=lambda snake_case__				: item[1]				)[1]

         return expected_height, expected_width





@require_torch
@require_vision
class   UpperCamelCase_ (       a_   ,					unittest.TestCase  ):
  _A       :					int				=	BridgeTowerImageProcessor if is_vision_available() else None



  def 		UpperCamelCase_				(      self				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			BridgeTowerImageProcessingTester(self				)



  @property
  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()



  def 		UpperCamelCase_				(      self				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_mean"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""image_std"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_normalize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""do_resize"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size"""				)				)
         self.assertTrue(hasattr(snake_case__ ,		"""size_divisor"""				)				)



  def 		UpperCamelCase_				(      self				)			->			Union[str, Any]:



         """simple docstring"""





         pass



  def 		UpperCamelCase_				(      self				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		Image.Image				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			int:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		numpify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		np.ndarray				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)



  def 		UpperCamelCase_				(      self				)			->			Dict:



         """simple docstring"""





         UpperCAmelCase	    =			self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         UpperCAmelCase	    =			prepare_image_inputs(self.image_processor_tester ,		equal_resolution=snake_case__ ,		torchify=snake_case__				)
         for image in image_inputs:
                self.assertIsInstance(snake_case__ ,		torch.Tensor				)

         # Test not batched input
         UpperCAmelCase	    =			image_processing(image_inputs[0] ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,		)

         # Test batched
         UpperCAmelCase	    =			image_processing(snake_case__ ,		return_tensors="""pt"""				).pixel_values

         UpperCAmelCase    ,       UpperCAmelCase	    =			self.image_processor_tester.get_expected_values(snake_case__ ,		batched=snake_case__				)
         self.assertEqual(
             encoded_images.shape ,		(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             ) ,		)
