code: 
  
  
  
  import os
  
  from apache_beam.io.filesystems import FileSystems
  from apache_beam.pipeline import Pipeline
  
  from .logging import get_logger
  
  
  __UpperCAmelCase      = 2 << 20  # 2mb
  __UpperCAmelCase      = get_logger(__name__)
  
  
  
  class        lowerCAmelCase_	(						a__							):
         def       snake_case_ (    self )  ->	Tuple:
            UpperCamelCase					:						Union[str, Any]					=			self._options.get_all_options().get('runner' )
            return runner in [None, "DirectRunner", "PortableRunner"]
  
  
  
  
  
  
  def 	UpperCamelCase			(     snake_case__ :					Optional[Any] ,  snake_case__ :					List[str] ,  snake_case__ :					Dict=False			)			->  str:
     UpperCamelCase					:						Optional[Any]					=			FileSystems
     if fs.exists(snake_case__			):
        if force_upload:
           logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""			)
        else:
           logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""			)
           return
     with fs.create(snake_case__			) as remote_file:
        with open(snake_case__ ,  'rb'			) as local_file:
           UpperCamelCase					:						int					=			local_file.read(snake_case__			)
           while chunk:
              remote_file.write(snake_case__			)
              UpperCamelCase					:						Any					=			local_file.read(snake_case__			)
  
  
  def 	UpperCamelCase			(     snake_case__ :					List[str] ,  snake_case__ :					Tuple ,  snake_case__ :					Tuple=False			)			->  Optional[Any]:
     UpperCamelCase					:						List[Any]					=			FileSystems
     if os.path.exists(snake_case__			):
        if force_download:
           logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""			)
        else:
           logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""			)
           return
     with fs.open(snake_case__			) as remote_file:
        with open(snake_case__ ,  'wb'			) as local_file:
           UpperCamelCase					:						str					=			remote_file.read(snake_case__			)
           while chunk:
              local_file.write(snake_case__			)
              UpperCamelCase					:						Optional[Any]					=			remote_file.read(snake_case__			)
  
  
  
  
style_context: 
  
  
  
  import unittest
  
  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 ViltImageProcessor
  
  
  
  class        lowerCAmelCase_	(						unittest.TestCase							):
         def __init__(    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=7,    SCREAMING_SNAKE_CASE_=3,    SCREAMING_SNAKE_CASE_=18,    SCREAMING_SNAKE_CASE_=30,    SCREAMING_SNAKE_CASE_=400,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=None,    SCREAMING_SNAKE_CASE_=2,    SCREAMING_SNAKE_CASE_=True,    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],    )  ->	List[Any]:
            UpperCamelCase					:						Optional[int]					=			size if size is not None else {'shortest_edge': 30}
            UpperCamelCase					:						Dict					=			parent
            UpperCamelCase					:						str					=			batch_size
            UpperCamelCase					:						str					=			num_channels
            UpperCamelCase					:						Optional[int]					=			image_size
            UpperCamelCase					:						List[Any]					=			min_resolution
            UpperCamelCase					:						Tuple					=			max_resolution
            UpperCamelCase					:						Tuple					=			do_resize
            UpperCamelCase					:						Any					=			size
            UpperCamelCase					:						int					=			size_divisor
            UpperCamelCase					:						str					=			do_normalize
            UpperCamelCase					:						Optional[int]					=			image_mean
            UpperCamelCase					:						List[str]					=			image_std
         def       snake_case_ (    self )  ->	List[Any]:
            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       snake_case_ (    self,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_=False )  ->	Union[str, Any]:
            if not batched:
               UpperCamelCase					:						Optional[Any]					=			self.size['shortest_edge']
               UpperCamelCase					:						int					=			image_inputs[0]
               if isinstance(SCREAMING_SNAKE_CASE_,    Image.Image ):
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.size
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			image.shape[1], image.shape[2]
               UpperCamelCase					:						Any					=			size / min(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
               if h < w:
                  UpperCamelCase			,  UpperCamelCase					:						Optional[int]					=			size, scale * w
               else:
                  UpperCamelCase			,  UpperCamelCase					:						List[str]					=			scale * h, size
  
               UpperCamelCase					:						int					=			int((1333 / 800) * size )
               if max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ ) > max_size:
                  UpperCamelCase					:						Any					=			max_size / max(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_ )
                  UpperCamelCase					:						List[Any]					=			newh * scale
                  UpperCamelCase					:						Optional[int]					=			neww * scale
  
               UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			int(newh + 0.5 ), int(neww + 0.5 )
               UpperCamelCase			,  UpperCamelCase					:						int					=			(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )
  
            else:
               UpperCamelCase					:						Dict					=			[]
               for image in image_inputs:
                  UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.get_expected_values([image] )
                  expected_values.append((expected_height, expected_width) )
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[0] )[0]
               UpperCamelCase					:						List[str]					=			max(SCREAMING_SNAKE_CASE_,    key=lambda SCREAMING_SNAKE_CASE_ : item[1] )[1]
  
            return expected_height, expected_width
  
  
  
  
  
  
  @require_torch
  @require_vision
  class        lowerCAmelCase_	(						a__ ,					unittest.TestCase							):
         UpperCAmelCase__       :      List[Any]       =						ViltImageProcessor if is_vision_available() else None
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						Dict					=			ViltImageProcessingTester(self )
         @property
         def       snake_case_ (    self )  ->	Dict:
            return self.image_processor_tester.prepare_image_processor_dict()
         def       snake_case_ (    self )  ->	Union[str, Any]:
            UpperCamelCase					:						List[str]					=			self.image_processing_class(**self.image_processor_dict )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_mean' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'image_std' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_normalize' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'do_resize' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size' ) )
            self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,    'size_divisor' ) )
         def       snake_case_ (    self )  ->	List[Any]:
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict )
            self.assertEqual(image_processor.size,    {'shortest_edge': 30} )
  
            UpperCamelCase					:						Union[str, Any]					=			self.image_processing_class.from_dict(self.image_processor_dict,    size=42 )
            self.assertEqual(image_processor.size,    {'shortest_edge': 42} )
         def       snake_case_ (    self )  ->	Dict:
            pass
         def       snake_case_ (    self )  ->	str:
            # Initialize image_processing
            UpperCamelCase					:						int					=			self.image_processing_class(**self.image_processor_dict )
            # create random PIL images
            UpperCamelCase					:						Any					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_ )
            for image in image_inputs:
               self.assertIsInstance(SCREAMING_SNAKE_CASE_,    Image.Image )
  
            # Test not batched input
            UpperCamelCase					:						int					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						str					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Tuple					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
         def       snake_case_ (    self )  ->	List[str]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random numpy tensors
            UpperCamelCase					:						Optional[Any]					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    numpify=SCREAMING_SNAKE_CASE_ )
            for image in image_inputs:
               self.assertIsInstance(SCREAMING_SNAKE_CASE_,    np.ndarray )
  
            # Test not batched input
            UpperCamelCase					:						Optional[Any]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						str					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						Dict					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  
  
  
         def       snake_case_ (    self )  ->	Optional[Any]:
            # Initialize image_processing
            UpperCamelCase					:						Optional[int]					=			self.image_processing_class(**self.image_processor_dict )
            # create random PyTorch tensors
            UpperCamelCase					:						Dict					=			prepare_image_inputs(self.image_processor_tester,    equal_resolution=SCREAMING_SNAKE_CASE_,    torchify=SCREAMING_SNAKE_CASE_ )
            for image in image_inputs:
               self.assertIsInstance(SCREAMING_SNAKE_CASE_,    torch.Tensor )
  
            # Test not batched input
            UpperCamelCase					:						List[str]					=			image_processing(image_inputs[0],    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (1, self.image_processor_tester.num_channels, expected_height, expected_width),    )
  
            # Test batched
            UpperCamelCase					:						List[Any]					=			image_processing(SCREAMING_SNAKE_CASE_,    return_tensors='pt' ).pixel_values
  
            UpperCamelCase			,  UpperCamelCase					:						Dict					=			self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,    batched=SCREAMING_SNAKE_CASE_ )
            self.assertEqual(
                encoded_images.shape,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                ),    )
  
  
  
  