
'''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,
    get_resize_output_image_size,
    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


_lowercase			:  Dict   							=	logging.get_logger(__name__)






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					['''pixel_values''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = 1 / 2_55    ,							__SCREAMING_SNAKE_CASE = True    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	size if size is not None else {'''shortest_edge''': 3_84}
											lowercase_     :					List[str]													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	do_resize
											lowercase_     :					List[Any]													=	size
											# Default value set here for backwards compatibility where the value in config is None
											lowercase_     :					Optional[Any]													=	crop_pct if crop_pct is not None else 2_24 / 2_56
											lowercase_     :					Tuple													=	resample
											lowercase_     :					Tuple													=	do_rescale
											lowercase_     :					Dict													=	rescale_factor
											lowercase_     :					Tuple													=	do_normalize
											lowercase_     :					Optional[int]													=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowercase_     :					Union[str, Any]													=	image_std if image_std is not None else IMAGENET_STANDARD_STD






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE		)
											if "shortest_edge" not in size:
																	raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''		)
											lowercase_     :					Optional[Any]													=	size['''shortest_edge''']

											if shortest_edge < 3_84:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	lowercase_     :					List[Any]													=	int(shortest_edge / crop_pct		)
																	lowercase_     :					Tuple													=	get_resize_output_image_size(__SCREAMING_SNAKE_CASE    ,							size=__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					Optional[Any]													=	resize(image=__SCREAMING_SNAKE_CASE    ,							size=__SCREAMING_SNAKE_CASE    ,							resample=__SCREAMING_SNAKE_CASE    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=__SCREAMING_SNAKE_CASE    ,							size=(shortest_edge, shortest_edge)    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    __SCREAMING_SNAKE_CASE    ,							size=(shortest_edge, shortest_edge)    ,							resample=__SCREAMING_SNAKE_CASE    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											return rescale(__SCREAMING_SNAKE_CASE    ,							scale=__SCREAMING_SNAKE_CASE    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											return normalize(__SCREAMING_SNAKE_CASE    ,							mean=__SCREAMING_SNAKE_CASE    ,							std=__SCREAMING_SNAKE_CASE    ,							data_format=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       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 = None    ,							__SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Any													=	do_resize if do_resize is not None else self.do_resize
											lowercase_     :					Optional[int]													=	crop_pct if crop_pct is not None else self.crop_pct
											lowercase_     :					Union[str, Any]													=	resample if resample is not None else self.resample
											lowercase_     :					Tuple													=	do_rescale if do_rescale is not None else self.do_rescale
											lowercase_     :					str													=	rescale_factor if rescale_factor is not None else self.rescale_factor
											lowercase_     :					List[str]													=	do_normalize if do_normalize is not None else self.do_normalize
											lowercase_     :					Any													=	image_mean if image_mean is not None else self.image_mean
											lowercase_     :					int													=	image_std if image_std is not None else self.image_std

											lowercase_     :					Optional[int]													=	size if size is not None else self.size
											lowercase_     :					List[str]													=	get_size_dict(__SCREAMING_SNAKE_CASE    ,							default_to_square=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					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 or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''		)

											if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
																	raise ValueError('''crop_pct must be specified if size < 384.'''		)

											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.
											lowercase_     :					Optional[Any]													=	[to_numpy_array(__SCREAMING_SNAKE_CASE		) for image in images]

											if do_resize:
																	lowercase_     :					Optional[Any]													=	[self.resize(image=__SCREAMING_SNAKE_CASE    ,							size=__SCREAMING_SNAKE_CASE    ,							crop_pct=__SCREAMING_SNAKE_CASE    ,							resample=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_rescale:
																	lowercase_     :					Dict													=	[self.rescale(image=__SCREAMING_SNAKE_CASE    ,							scale=__SCREAMING_SNAKE_CASE		) for image in images]

											if do_normalize:
																	lowercase_     :					Any													=	[self.normalize(image=__SCREAMING_SNAKE_CASE    ,							mean=__SCREAMING_SNAKE_CASE    ,							std=__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_     :					Any													=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) for image in images]

											lowercase_     :					Dict													=	{'''pixel_values''': images}
											return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,							tensor_type=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						return [
						    txt[:a] + txt[a].upper() + txt[a + 1 :]
						    for a in range(len(__SCREAMING_SNAKE_CASE							)							)
						    if txt[a].isalpha()
						]


if __name__ == "__main__":
							__import__("doctest").testmod()



