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 (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  __lowerCamelCase								=					logging.get_logger(__name__)
  
  
  if is_vision_available():
      import PIL
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			['''pixel_values''']
   def __init__(						self	,    a = True	,    a = None	,    a = PILImageResampling.BICUBIC	,    a = True	,    a = None	,    a = True	,    a = 1 / 2_5_5	,    a = True	,    a = None	,    a = None	,    a = True	,    **a	,    )	->							None:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__(**a					)
     _A           =    size if size is not None else {'''shortest_edge''': 2_2_4}
     _A           =    get_size_dict(a	,    default_to_square=a					)
     _A           =    crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
     _A           =    get_size_dict(a					)
  
     _A           =    do_resize
     _A           =    size
     _A           =    resample
     _A           =    do_center_crop
     _A           =    crop_size
     _A           =    do_rescale
     _A           =    rescale_factor
     _A           =    do_normalize
     _A           =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     _A           =    image_std if image_std is not None else OPENAI_CLIP_STD
     _A           =    do_convert_rgb
   def lowercase_		(						self	,    a	,    a	,    a = PILImageResampling.BICUBIC	,    a = None	,    **a	,    )	->							np.ndarray:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    get_size_dict(a	,    default_to_square=a					)
     _A           =    get_resize_output_image_size(
         a	,    size=(size['''height'''], size['''width'''])	,    default_to_square=a					)
     return resize(a	,    size=a	,    resample=a	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a = None	,    **a	,    )	->							np.ndarray:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    get_size_dict(a					)
     return center_crop(a	,    size=(size['''height'''], size['''width'''])	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a = None	,    **a	,    )	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return rescale(a	,    scale=a	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a	,    a = None	,    **a	,    )	->							np.ndarray:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return normalize(a	,    mean=a	,    std=a	,    data_format=a	,    **a					)
  
  
  
   def lowercase_		(						self	,    a	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = ChannelDimension.FIRST	,    **a	,    )	->							PIL.Image.Image:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    do_resize if do_resize is not None else self.do_resize
     _A           =    size if size is not None else self.size
     _A           =    get_size_dict(a	,    default_to_square=a					)
     _A           =    resample if resample is not None else self.resample
     _A           =    do_center_crop if do_center_crop is not None else self.do_center_crop
     _A           =    crop_size if crop_size is not None else self.crop_size
     _A           =    get_size_dict(a					)
     _A           =    do_rescale if do_rescale is not None else self.do_rescale
     _A           =    rescale_factor if rescale_factor is not None else self.rescale_factor
     _A           =    do_normalize if do_normalize is not None else self.do_normalize
     _A           =    image_mean if image_mean is not None else self.image_mean
     _A           =    image_std if image_std is not None else self.image_std
     _A           =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
     _A           =    make_list_of_images(a					)
  
     if not valid_images(a					):
       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_center_crop and crop_size is None:
       raise ValueError('''Crop size must be specified if do_center_crop is True.'''					)
  
     if do_rescale and rescale_factor is None:
       raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)
  
     if do_normalize and (image_mean is None or image_std is None):
       raise ValueError('''Image mean and std must be specified if do_normalize is True.'''					)
  
     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
       _A           =    [convert_to_rgb(a					) for image in images]
  
     # All transformations expect numpy arrays.
     _A           =    [to_numpy_array(a					) for image in images]
  
     if do_resize:
       _A           =    [self.resize(image=a	,    size=a	,    resample=a					) for image in images]
  
     if do_center_crop:
       _A           =    [self.center_crop(image=a	,    size=a					) for image in images]
  
     if do_rescale:
       _A           =    [self.rescale(image=a	,    scale=a					) for image in images]
  
     if do_normalize:
       _A           =    [self.normalize(image=a	,    mean=a	,    std=a					) for image in images]
  
     _A           =    [to_channel_dimension_format(a	,    a					) for image in images]
  
     _A           =    {'''pixel_values''': images}
     return BatchFeature(data=a	,    tensor_type=a					)
style_context: 
  
  
  
  import platform
  from argparse import ArgumentParser
  
  import huggingface_hub
  import pandas
  import pyarrow
  
  from datasets import __version__ as version
  from datasets.commands import BaseDatasetsCLICommand
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case							)   -> Optional[Any]:
    return EnvironmentCommand()
  
  
  
  class 		_snake_case      (			lowerCamelCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parser.add_parser('''env'''	,    help='''Print relevant system environment info.'''					)
     download_parser.set_defaults(func=a					)
   def lowercase_		(						self					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    {
         '''`datasets` version''': version,
         '''Platform''': platform.platform(),
         '''Python version''': platform.python_version(),
         '''Huggingface_hub version''': huggingface_hub.__version__,
         '''PyArrow version''': pyarrow.__version__,
         '''Pandas version''': pandas.__version__,
     }
  
     print('''\nCopy-and-paste the text below in your GitHub issue.\n'''					)
     print(self.format_dict(a					)					)
  
     return info
  
  
  
   @staticmethod
   def lowercase_		(						a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]					) + "\n"