
"""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,
    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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)


if is_vision_available():
			import PIL
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''pixel_values''']




							def __init__( self    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = PILImageResampling.BICUBIC    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = True    ,				snake_case_ = 1 / 255    ,				snake_case_ = True    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = True    ,				**snake_case_    ,				)       ->  None:
													super().__init__(**snake_case_				)
													__lowerCAmelCase     =  size if size is not None else {"""shortest_edge""": 224}
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_				)
													__lowerCAmelCase     =  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_    ,				param_name="""crop_size"""				)

													__lowerCAmelCase     =  do_resize
													__lowerCAmelCase     =  size
													__lowerCAmelCase     =  resample
													__lowerCAmelCase     =  do_center_crop
													__lowerCAmelCase     =  crop_size
													__lowerCAmelCase     =  do_rescale
													__lowerCAmelCase     =  rescale_factor
													__lowerCAmelCase     =  do_normalize
													__lowerCAmelCase     =  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													__lowerCAmelCase     =  image_std if image_std is not None else OPENAI_CLIP_STD
													__lowerCAmelCase     =  do_convert_rgb




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_ = PILImageResampling.BICUBIC    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  np.ndarray:
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				default_to_square=snake_case_				)
													if "shortest_edge" not in size:
																			raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
													__lowerCAmelCase     =  get_resize_output_image_size(snake_case_    ,				size=size["""shortest_edge"""]    ,				default_to_square=snake_case_				)
													return resize(snake_case_    ,				size=snake_case_    ,				resample=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  np.ndarray:
													__lowerCAmelCase     =  get_size_dict(snake_case_				)
													if "height" not in size or "width" not in size:
																			raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""				)
													return center_crop(snake_case_    ,				size=(size["""height"""], size["""width"""])    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  Union[str, Any]:
													return rescale(snake_case_    ,				scale=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  np.ndarray:
													return normalize(snake_case_    ,				mean=snake_case_    ,				std=snake_case_    ,				data_format=snake_case_    ,				**snake_case_				)




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = ChannelDimension.FIRST    ,				**snake_case_    ,				)       ->  PIL.Image.Image:
													__lowerCAmelCase     =  do_resize if do_resize is not None else self.do_resize
													__lowerCAmelCase     =  size if size is not None else self.size
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				param_name="""size"""    ,				default_to_square=snake_case_				)
													__lowerCAmelCase     =  resample if resample is not None else self.resample
													__lowerCAmelCase     =  do_center_crop if do_center_crop is not None else self.do_center_crop
													__lowerCAmelCase     =  crop_size if crop_size is not None else self.crop_size
													__lowerCAmelCase     =  get_size_dict(snake_case_    ,				param_name="""crop_size"""    ,				default_to_square=snake_case_				)
													__lowerCAmelCase     =  do_rescale if do_rescale is not None else self.do_rescale
													__lowerCAmelCase     =  rescale_factor if rescale_factor is not None else self.rescale_factor
													__lowerCAmelCase     =  do_normalize if do_normalize is not None else self.do_normalize
													__lowerCAmelCase     =  image_mean if image_mean is not None else self.image_mean
													__lowerCAmelCase     =  image_std if image_std is not None else self.image_std
													__lowerCAmelCase     =  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													__lowerCAmelCase     =  make_list_of_images(snake_case_				)

													if not valid_images(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_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:
																			__lowerCAmelCase     =  [convert_to_rgb(snake_case_				) for image in images]

													# All transformations expect numpy arrays.
													__lowerCAmelCase     =  [to_numpy_array(snake_case_				) for image in images]

													if do_resize:
																			__lowerCAmelCase     =  [self.resize(image=snake_case_    ,				size=snake_case_    ,				resample=snake_case_				) for image in images]

													if do_center_crop:
																			__lowerCAmelCase     =  [self.center_crop(image=snake_case_    ,				size=snake_case_				) for image in images]

													if do_rescale:
																			__lowerCAmelCase     =  [self.rescale(image=snake_case_    ,				scale=snake_case_				) for image in images]

													if do_normalize:
																			__lowerCAmelCase     =  [self.normalize(image=snake_case_    ,				mean=snake_case_    ,				std=snake_case_				) for image in images]

													__lowerCAmelCase     =  [to_channel_dimension_format(snake_case_    ,				snake_case_				) for image in images]

													__lowerCAmelCase     =  {"""pixel_values""": images}
													return BatchFeature(data=snake_case_    ,				tensor_type=snake_case_				)


"""simple docstring"""
from typing import List

from .keymap import KEYMAP, get_character

def  lowercase     (_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += [key]
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator

def  lowercase     (*_lowerCAmelCase						):

						def decorator(_lowerCAmelCase						):
												__lowerCAmelCase     =  getattr(_lowerCAmelCase ,    """handle_key""" ,    []						)
												handle += keys
												setattr(_lowerCAmelCase ,    """handle_key""" ,    _lowerCAmelCase						)
												return func

						return decorator
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __new__( cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Any:
													__lowerCAmelCase     =  super().__new__(cls    ,				snake_case_    ,				snake_case_    ,				snake_case_				)
													if not hasattr(snake_case_    ,				"""key_handler"""				):
																			setattr(snake_case_    ,				"""key_handler"""    ,				{}				)
													setattr(snake_case_    ,				"""handle_input"""    ,				KeyHandler.handle_input				)

													for value in attrs.values():
																			__lowerCAmelCase     =  getattr(snake_case_    ,				"""handle_key"""    ,				[]				)
																			for key in handled_keys:
																									__lowerCAmelCase     =  value
													return new_cls




							@staticmethod
							def    A__						( cls				)       ->  Tuple:
													__lowerCAmelCase     =  get_character()
													if char != KEYMAP["undefined"]:
																			__lowerCAmelCase     =  ord(snake_case_				)
													__lowerCAmelCase     =  cls.key_handler.get(snake_case_				)
													if handler:
																			__lowerCAmelCase     =  char
																			return handler(cls				)
													else:
																			return None

def  lowercase     (cls						):
						return KeyHandler(cls.__name__ ,    cls.__bases__ ,    cls.__dict__.copy()						)
