code: 
  
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  
  
  __snake_case							      =						logging.get_logger(__name__)
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        UpperCamelCase_       :      List[str]     =['pixel_values']
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = 1 / 255					,       SCREAMING_SNAKE_CASE_ = True					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   None:
            super().__init__(**SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :List[str]										=     size if size is not None else {'''height''': 224, '''width''': 224}
            UpperCamelCase :List[Any]										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
            UpperCamelCase :Optional[Any]										=     do_resize
            UpperCamelCase :Dict										=     do_rescale
            UpperCamelCase :Optional[int]										=     do_normalize
            UpperCamelCase :Any										=     size
            UpperCamelCase :Tuple										=     resample
            UpperCamelCase :Union[str, Any]										=     rescale_factor
            UpperCamelCase :List[str]										=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            UpperCamelCase :str										=     image_std if image_std is not None else IMAGENET_STANDARD_STD
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = PILImageResampling.BILINEAR					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
            UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
            if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''				)
            return resize(
                SCREAMING_SNAKE_CASE_					,       size=(size['''height'''], size['''width'''])					,       resample=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_				)       ->   np.ndarray:
            return rescale(SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       **SCREAMING_SNAKE_CASE_					,       )       ->   np.ndarray:
            return normalize(SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_					,       data_format=SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)
  
  
  
  
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = None					,       SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST					,       **SCREAMING_SNAKE_CASE_					,       )       ->   int:
            UpperCamelCase :Any										=     do_resize if do_resize is not None else self.do_resize
            UpperCamelCase :Tuple										=     do_rescale if do_rescale is not None else self.do_rescale
            UpperCamelCase :List[Any]										=     do_normalize if do_normalize is not None else self.do_normalize
            UpperCamelCase :Union[str, Any]										=     resample if resample is not None else self.resample
            UpperCamelCase :List[str]										=     rescale_factor if rescale_factor is not None else self.rescale_factor
            UpperCamelCase :Tuple										=     image_mean if image_mean is not None else self.image_mean
            UpperCamelCase :Any										=     image_std if image_std is not None else self.image_std
  
            UpperCamelCase :int										=     size if size is not None else self.size
            UpperCamelCase :str										=     get_size_dict(SCREAMING_SNAKE_CASE_				)
  
            UpperCamelCase :Union[str, Any]										=     make_list_of_images(SCREAMING_SNAKE_CASE_				)
  
            if not valid_images(SCREAMING_SNAKE_CASE_				):
                raise ValueError(
                    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)
  
            if do_resize and size is None:
                raise ValueError('''Size must be specified if do_resize is True.'''				)
  
            if do_rescale and rescale_factor is None:
                raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)
  
            # All transformations expect numpy arrays.
            UpperCamelCase :Tuple										=     [to_numpy_array(SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_resize:
                UpperCamelCase :Optional[int]										=     [self.resize(image=SCREAMING_SNAKE_CASE_					,       size=SCREAMING_SNAKE_CASE_					,       resample=SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_rescale:
                UpperCamelCase :int										=     [self.rescale(image=SCREAMING_SNAKE_CASE_					,       scale=SCREAMING_SNAKE_CASE_				) for image in images]
  
            if do_normalize:
                UpperCamelCase :Optional[Any]										=     [self.normalize(image=SCREAMING_SNAKE_CASE_					,       mean=SCREAMING_SNAKE_CASE_					,       std=SCREAMING_SNAKE_CASE_				) for image in images]
  
            UpperCamelCase :List[str]										=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				) for image in images]
  
            UpperCamelCase :Dict										=     {'''pixel_values''': images}
            return BatchFeature(data=SCREAMING_SNAKE_CASE_					,       tensor_type=SCREAMING_SNAKE_CASE_				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  from typing import Generic, TypeVar
  
  __snake_case							      =						TypeVar("""T""")
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        pass
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      Generic[T]				):
  
  
  
        """simple docstring"""
        def __init__(       self					,       SCREAMING_SNAKE_CASE_ = 10				)       ->   List[str]:
            UpperCamelCase :list[T]										=     []
            UpperCamelCase :Any										=     limit
        def __bool__(       self				)       ->   bool:
            return bool(self.stack				)
        def __str__(       self				)       ->   str:
            return str(self.stack				)
        def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_				)       ->   None:
            if len(self.stack				) >= self.limit:
                raise StackOverflowError
            self.stack.append(SCREAMING_SNAKE_CASE_				)
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack.pop()
        def 					UpperCAmelCase		(       self				)       ->   T:
            if not self.stack:
                raise StackUnderflowError
            return self.stack[-1]
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return not bool(self.stack				)
        def 					UpperCAmelCase		(       self				)       ->   bool:
            return self.size() == self.limit
        def 					UpperCAmelCase		(       self				)       ->   int:
            return len(self.stack				)
  
  
  
  
        def __contains__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   bool:
            return item in self.stack
  
  
  
  
  
  
  
  def 						_A     (       ):
      UpperCamelCase :Stack[int]										=     Stack(10 )
      assert bool(SCREAMING_SNAKE_CASE__ ) is False
      assert stack.is_empty() is True
      assert stack.is_full() is False
      assert str(SCREAMING_SNAKE_CASE__ ) == "[]"
  
      try:
          UpperCamelCase :Optional[int]										=     stack.pop()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      try:
          UpperCamelCase :Union[str, Any]										=     stack.peek()
          raise AssertionError  # This should not happen
      except StackUnderflowError:
          assert True  # This should happen
  
      for i in range(10 ):
          assert stack.size() == i
          stack.push(SCREAMING_SNAKE_CASE__ )
  
      assert bool(SCREAMING_SNAKE_CASE__ )
      assert not stack.is_empty()
      assert stack.is_full()
      assert str(SCREAMING_SNAKE_CASE__ ) == str(list(range(10 ) ) )
      assert stack.pop() == 9
      assert stack.peek() == 8
  
      stack.push(100 )
      assert str(SCREAMING_SNAKE_CASE__ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100] )
  
      try:
          stack.push(200 )
          raise AssertionError  # This should not happen
      except StackOverflowError:
          assert True  # This should happen
  
      assert not stack.is_empty()
      assert stack.size() == 10
  
      assert 5 in stack
      assert 55 not in stack
  
  
  if __name__ == "__main__":
   test_stack()
  
  
  
  
  
  