code: 
  """simple docstring"""
  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, 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, is_vision_available, logging
  
  
  if is_vision_available():
     import PIL
  
  
  __magic_name__		       =				logging.get_logger(__name__)
  
  
  class    _lowerCAmelCase						(						lowerCamelCase__						):
         lowercase_     :     List[Any]       =							['pixel_values']
  
  
  
         def __init__(      self ,						a_ = True ,						a_ = None ,						a_ = PIL.Image.NEAREST ,						a_ = False ,						a_ = None ,						a_ = 1 / 255 ,						a_ = False ,						a_ = True ,						a_ = True ,						a_ = None ,						a_ = None ,						a_ = True ,						**a_ ,						)							->							None:
          super().__init__(**lowercase_   )
          _UpperCAmelCase =						size if size is not None else {"height": 346, "width": 346}
          _UpperCAmelCase =						get_size_dict(lowercase_   )
          _UpperCAmelCase =						crop_size if crop_size is not None else {"height": 289, "width": 289}
          _UpperCAmelCase =						get_size_dict(lowercase_ ,						param_name="crop_size"   )
  
          _UpperCAmelCase =						do_resize
          _UpperCAmelCase =						size
          _UpperCAmelCase =						resample
          _UpperCAmelCase =						do_center_crop
          _UpperCAmelCase =						crop_size
          _UpperCAmelCase =						do_rescale
          _UpperCAmelCase =						rescale_factor
          _UpperCAmelCase =						rescale_offset
          _UpperCAmelCase =						do_normalize
          _UpperCAmelCase =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _UpperCAmelCase =						image_std if image_std is not None else IMAGENET_STANDARD_STD
          _UpperCAmelCase =						include_top
  
  
  
         def       _a   (      self ,						a_ ,						a_ ,						a_ = PIL.Image.NEAREST ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
          _UpperCAmelCase =						get_size_dict(lowercase_   )
          if "height" not in size or "width" not in size:
           raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
          return resize(
              lowercase_ ,						size=(size["height"], size["width"]) ,						resample=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )
  
  
  
         def       _a   (      self ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
          _UpperCAmelCase =						get_size_dict(lowercase_   )
          if "height" not in size or "width" not in size:
           raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"   )
          return center_crop(lowercase_ ,						size=(size["height"], size["width"]) ,						data_format=lowercase_ ,						**lowercase_   )
  
  
  
         def       _a   (      self ,						a_ ,						a_ ,						a_ = True ,						a_ = None ,						**a_ ,						)							->							Union[str, Any]:
  
  
  
  
  
  
          if offset:
           _UpperCAmelCase =						(image - 127.5) * scale
           if data_format is not None:
            _UpperCAmelCase =						to_channel_dimension_format(lowercase_ ,						lowercase_   )
           _UpperCAmelCase =						rescaled_image.astype(np.floataa   )
          else:
           _UpperCAmelCase =						rescale(lowercase_ ,						scale=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )
          return rescaled_image
  
  
  
         def       _a   (      self ,						a_ ,						a_ ,						a_ ,						a_ = None ,						**a_ ,						)							->							np.ndarray:
          return normalize(lowercase_ ,						mean=lowercase_ ,						std=lowercase_ ,						data_format=lowercase_ ,						**lowercase_   )
  
  
  
         def       _a   (      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_ = None ,						a_ = ChannelDimension.FIRST ,						**a_ ,						)							->							PIL.Image.Image:
          _UpperCAmelCase =						do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase =						resample if resample is not None else self.resample
          _UpperCAmelCase =						do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase =						do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase =						rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase =						rescale_offset if rescale_offset is not None else self.rescale_offset
          _UpperCAmelCase =						do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase =						image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase =						image_std if image_std is not None else self.image_std
          _UpperCAmelCase =						include_top if include_top is not None else self.include_top
  
          _UpperCAmelCase =						size if size is not None else self.size
          _UpperCAmelCase =						get_size_dict(lowercase_   )
          _UpperCAmelCase =						crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase =						get_size_dict(lowercase_ ,						param_name="crop_size"   )
  
          _UpperCAmelCase =						make_list_of_images(lowercase_   )
  
          if not valid_images(lowercase_   ):
           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 or resample is None:
           raise ValueError("Size and resample 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."   )
  
          # All transformations expect numpy arrays.
          _UpperCAmelCase =						[to_numpy_array(lowercase_   ) for image in images]
  
          if do_resize:
           _UpperCAmelCase =						[self.resize(image=lowercase_ ,						size=lowercase_ ,						resample=lowercase_   ) for image in images]
  
          if do_center_crop:
           _UpperCAmelCase =						[self.center_crop(image=lowercase_ ,						size=lowercase_   ) for image in images]
  
          if do_rescale:
           _UpperCAmelCase =						[self.rescale(image=lowercase_ ,						scale=lowercase_ ,						offset=lowercase_   ) for image in images]
  
          if do_normalize:
           _UpperCAmelCase =						[self.normalize(image=lowercase_ ,						mean=lowercase_ ,						std=lowercase_   ) for image in images]
  
          if include_top:
           _UpperCAmelCase =						[self.normalize(image=lowercase_ ,						mean=[0, 0, 0] ,						std=lowercase_   ) for image in images]
  
          _UpperCAmelCase =						[to_channel_dimension_format(lowercase_ ,						lowercase_   ) for image in images]
  
          _UpperCAmelCase =						{"pixel_values": images}
          return BatchFeature(data=lowercase_ ,						tensor_type=lowercase_   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __magic_name__		       =				{}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizer''']
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizerFast''']
  
  
  if TYPE_CHECKING:
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm import CpmTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm_fast import CpmTokenizerFast
  
  else:
  			import sys
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  