





'''simple docstring'''







import pytest

from datasets.utils.version import Version





@pytest.mark.parametrize(
    '''other, expected_equality''',     [
        (Version('''1.0.0'''				), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''				), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ],     )
def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     List[str],     SCREAMING_SNAKE_CASE__ :     List[str]				)	->		Any:
						UpperCAmelCase_     : List[Any]         =					Version('''1.0.0'''				)
						assert (version == other) is expected_equality
						assert (version != other) is not expected_equality
						assert (hash(SCREAMING_SNAKE_CASE__				) == hash(SCREAMING_SNAKE_CASE__				)) is expected_equality







'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


snake_case_       :    int          =					logging.get_logger(__name__)


class    __a (lowerCamelCase       ):
							__a   :				Optional[Any]       =    ["pixel_values"]

							def __init__(							self							:       str    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Dict[str, int] = None    ,       __magic_name__							:       PILImageResampling = PILImageResampling.BICUBIC    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Union[int, float] = 1 / 2_55    ,       __magic_name__							:       bool = True    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       **__magic_name__							:       List[Any]    ,       )   ->    None:







													"""simple docstring"""
													super().__init__(**__magic_name__				)
													UpperCAmelCase_     : Tuple         =					crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)
													UpperCAmelCase_     : List[Any]         =					size if size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_     : int         =					get_size_dict(__magic_name__				)

													UpperCAmelCase_     : Optional[int]         =					do_center_crop
													UpperCAmelCase_     : List[str]         =					crop_size
													UpperCAmelCase_     : Dict         =					do_resize
													UpperCAmelCase_     : str         =					size
													UpperCAmelCase_     : int         =					resample
													UpperCAmelCase_     : Optional[int]         =					do_rescale
													UpperCAmelCase_     : List[str]         =					rescale_factor
													UpperCAmelCase_     : int         =					do_normalize
													UpperCAmelCase_     : Any         =					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
													UpperCAmelCase_     : str         =					image_std if image_std is not None else IMAGENET_DEFAULT_STD

							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Dict[str, int]    ,       __magic_name__							:       Optional[int] = None    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Union[str, Any]    ,       )   ->    np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : List[Any]         =					self.size if size is None else size
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													UpperCAmelCase_     : Union[str, Any]         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)

													UpperCAmelCase_  ,    UpperCAmelCase_     : Any         =					get_image_size(__magic_name__				)
													UpperCAmelCase_     : List[str]         =					min(__magic_name__    ,       __magic_name__				)
													UpperCAmelCase_     : Dict         =					(size['''height'''] / crop_size['''height''']) * min_dim
													UpperCAmelCase_     : List[str]         =					(size['''width'''] / crop_size['''width''']) * min_dim
													return center_crop(__magic_name__    ,       size=(cropped_height, cropped_width)    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Dict[str, int]    ,       __magic_name__							:       PILImageResampling = PIL.Image.BICUBIC    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       str    ,       )   ->    np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_     : str         =					get_size_dict(__magic_name__				)
													if "height" not in size or "width" not in size:
																			raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""				)
													return resize(
													    __magic_name__    ,       size=(size['''height'''], size['''width'''])    ,       resample=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Tuple    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Union[int, float]    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Tuple    ,       )   ->    str:







													"""simple docstring"""
													return rescale(__magic_name__    ,       scale=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)

							def       UpperCAmelCase__		(							self							:       Optional[int]    ,       __magic_name__							:       np.ndarray    ,       __magic_name__							:       Union[float, List[float]]    ,       __magic_name__							:       Union[float, List[float]]    ,       __magic_name__							:       Optional[Union[str, ChannelDimension]] = None    ,       **__magic_name__							:       Dict    ,       )   ->    np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__    ,       mean=__magic_name__    ,       std=__magic_name__    ,       data_format=__magic_name__    ,       **__magic_name__				)



							def       UpperCAmelCase__		(							self							:       str    ,       __magic_name__							:       ImageInput    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Dict[str, int]] = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Dict[str, int]] = None    ,       __magic_name__							:       PILImageResampling = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[float] = None    ,       __magic_name__							:       Optional[bool] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[float, List[float]]] = None    ,       __magic_name__							:       Optional[Union[str, TensorType]] = None    ,       __magic_name__							:       ChannelDimension = ChannelDimension.FIRST    ,       **__magic_name__							:       Optional[Any]    ,       )   ->    PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_     : str         =					crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_     : int         =					get_size_dict(__magic_name__    ,       param_name='''crop_size'''				)
													UpperCAmelCase_     : Dict         =					do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_     : Optional[Any]         =					size if size is not None else self.size
													UpperCAmelCase_     : Tuple         =					get_size_dict(__magic_name__				)
													UpperCAmelCase_     : Any         =					resample if resample is not None else self.resample
													UpperCAmelCase_     : int         =					do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_     : str         =					rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_     : Optional[Any]         =					do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_     : Tuple         =					image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_     : Tuple         =					image_std if image_std is not None else self.image_std

													UpperCAmelCase_     : Optional[Any]         =					make_list_of_images(__magic_name__				)

													if not valid_images(__magic_name__				):
																			raise ValueError(
																			    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																			    '''torch.Tensor, tf.Tensor or jax.ndarray.'''				)

													if do_center_crop and crop_size is None:
																			raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''				)

													if do_resize and size is None:
																			raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''				)

													# All transformations expect numpy arrays.
													UpperCAmelCase_     : Tuple         =					[to_numpy_array(__magic_name__				) for image in images]

													if do_center_crop:
																			UpperCAmelCase_     : int         =					[self.center_crop(__magic_name__    ,       __magic_name__    ,       size=__magic_name__				) for image in images]

													if do_resize:
																			UpperCAmelCase_     : Dict         =					[self.resize(image=__magic_name__    ,       size=__magic_name__    ,       resample=__magic_name__				) for image in images]

													if do_rescale:
																			UpperCAmelCase_     : Union[str, Any]         =					[self.rescale(image=__magic_name__    ,       scale=__magic_name__				) for image in images]

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

													UpperCAmelCase_     : Optional[int]         =					[to_channel_dimension_format(__magic_name__    ,       __magic_name__				) for image in images]

													UpperCAmelCase_     : Union[str, Any]         =					{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__    ,       tensor_type=__magic_name__				)





