"""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


lowerCAmelCase__						:						Union[str, Any]		  =			logging.get_logger(__name__)
class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				["pixel_values"]







					def __init__(       self     :			Union[str, Any]					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			Union[int, float] = 1 / 255					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,**lowerCamelCase__     :			Any					,):
									super().__init__(**lowerCamelCase__    )
									UpperCAmelCase__    =							size if size is not None else {'height': 256, 'width': 256}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else {'height': 224, 'width': 224}
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,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__    =							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







					def 	__lowerCAmelCase		(       self     :			Any					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			PILImageResampling = PIL.Image.BICUBIC					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			str					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									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(
									    lowerCamelCase__					,size=(size['height'], size['width'])					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Dict[str, int]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Dict					,):
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									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(lowerCamelCase__					,size=(size['height'], size['width'])					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Union[int, float]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Tuple					,):
									return rescale(lowerCamelCase__					,scale=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			np.ndarray					,lowerCamelCase__     :			Union[float, List[float]]					,lowerCamelCase__     :			Union[float, List[float]]					,lowerCamelCase__     :			Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase__     :			Union[str, Any]					,):
									return normalize(lowerCamelCase__					,mean=lowerCamelCase__					,std=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			ImageInput					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			Union[str, Any]=None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Dict[str, int] = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			float = None					,lowerCamelCase__     :			bool = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[float, List[float]]] = None					,lowerCamelCase__     :			Optional[Union[str, TensorType]] = None					,lowerCamelCase__     :			ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__     :			List[Any]					,):
									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__    =							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__    =							size if size is not None else self.size
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__    )
									UpperCAmelCase__    =							crop_size if crop_size is not None else self.crop_size
									UpperCAmelCase__    =							get_size_dict(lowerCamelCase__					,param_name='crop_size'    )

									UpperCAmelCase__    =							make_list_of_images(lowerCamelCase__    )

									if not valid_images(lowerCamelCase__    ):
													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(lowerCamelCase__    ) for image in images]

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

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

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

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

									UpperCAmelCase__    =							[to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__    ) for image in images]

									UpperCAmelCase__    =							{'pixel_values': images}
									return BatchFeature(data=lowerCamelCase__					,tensor_type=lowerCamelCase__    )




"""simple docstring"""






import os
import sys
from contextlib import contextmanager


# Windows only
if os.name == "nt":
						import ctypes
						import msvcrt  # noqa
						class        snake_case (				ctypes.Structure       ):




											"""simple docstring"""

											snake_case__       					=				[("size", ctypes.c_int), ("visible", ctypes.c_byte)]



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							False
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25l' )
								sys.stdout.flush()



def 						a_		(      ):
				if os.name == "nt":
								UpperCAmelCase__    =							CursorInfo()
								UpperCAmelCase__    =							ctypes.windll.kernelaa.GetStdHandle(-1_1 )
								ctypes.windll.kernelaa.GetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
								UpperCAmelCase__    =							True
								ctypes.windll.kernelaa.SetConsoleCursorInfo(lowerCamelCase      ,    ctypes.byref(lowerCamelCase ) )
				elif os.name == "posix":
								sys.stdout.write('\033[?25h' )
								sys.stdout.flush()







@contextmanager
def 						a_		(      ):
				try:
								hide_cursor()
								yield
				finally:
								show_cursor()



