


"""simple docstring"""
from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE							:			List[str]						=			logging.get_logger(__name__)





class    __A   (snake_case__):





							'''simple docstring'''

							__lowercase:						Optional[Any]												=			["""pixel_values"""]



							def __init__( self							:					Optional[Any]						,				UpperCAmelCase_							:					str = True						,				UpperCAmelCase_							:					Optional[int] = 1 / 255						,				UpperCAmelCase_							:					Tuple = True						,				UpperCAmelCase_							:					str = 8						,				**UpperCAmelCase_							:					str						,				)    ->None:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_				)

														snake_case_							=						do_rescale
														snake_case_							=						rescale_factor
														snake_case_							=						do_pad
														snake_case_							=						pad_size



							def 						lowerCAmelCase				( self							:					Any						,				UpperCAmelCase_							:					int						,				UpperCAmelCase_							:					Optional[Any]						,				UpperCAmelCase_							:					str = None						,				**UpperCAmelCase_							:					Optional[int]				)    ->np.ndarray:



														"""simple docstring"""



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



							def 						lowerCAmelCase				( self							:					str						,				UpperCAmelCase_							:					Optional[int]						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					str = None				)    ->Optional[Any]:



														"""simple docstring"""



														snake_case_							=						get_image_size(UpperCAmelCase_				)
														snake_case_							=						(old_height // size + 1) * size - old_height
														snake_case_							=						(old_width // size + 1) * size - old_width

														return pad(UpperCAmelCase_						,				((0, pad_height), (0, pad_width))						,				mode="""symmetric"""						,				data_format=UpperCAmelCase_				)



							def 						lowerCAmelCase				( self							:					Dict						,				UpperCAmelCase_							:					str						,				UpperCAmelCase_							:					int = None						,				UpperCAmelCase_							:					int = None						,				UpperCAmelCase_							:					Tuple = None						,				UpperCAmelCase_							:					str = None						,				UpperCAmelCase_							:					int = None						,				UpperCAmelCase_							:					Optional[Any] = ChannelDimension.FIRST						,				**UpperCAmelCase_							:					Dict						,				)    ->List[Any]:



														"""simple docstring"""



														snake_case_							=						do_rescale if do_rescale is not None else self.do_rescale
														snake_case_							=						rescale_factor if rescale_factor is not None else self.rescale_factor
														snake_case_							=						do_pad if do_pad is not None else self.do_pad
														snake_case_							=						pad_size if pad_size is not None else self.pad_size

														snake_case_							=						make_list_of_images(UpperCAmelCase_				)

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

														if do_rescale and rescale_factor is None:
																					raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

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

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

														if do_pad:
																					snake_case_							=						[self.pad(UpperCAmelCase_						,				size=UpperCAmelCase_				) for image in images]

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

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








'''simple docstring'''




from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap



def       __lowerCamelCase  (					_lowercase		)      ->			Optional[Any]:
							return getitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			List[str]:
							return setitem, k, v



def       __lowerCamelCase  (					_lowercase		)      ->			int:
							return delitem, k



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				*_lowercase		)      ->			Optional[Any]:
							try:
														return fun(_lowercase	,				*_lowercase		), None
							except Exception as e:
														return None, e


a   :			List[str]					  =    (
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
)

a   :			List[Any]					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_a""", """val_b"""),
]

a   :			int					  =    [
    _set("""key_a""", """val_a"""),
    _set("""key_b""", """val_b"""),
    _del("""key_a"""),
    _del("""key_b"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
]

a   :			List[Any]					  =    [
    _get("""key_a"""),
    _del("""key_a"""),
    _set("""key_a""", """val_a"""),
    _del("""key_a"""),
    _del("""key_a"""),
    _get("""key_a"""),
]

a   :			Tuple					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
]

a   :			Optional[Any]					  =    [
    *[_set(x, x) for x in range(5)],  # guaranteed upsize
    *[_del(x) for x in range(5)],
    _set("""key_a""", """val_b"""),
]



@pytest.mark.parametrize(
    """operations"""	,				(
        pytest.param(_add_items	,				id="""add items"""		),
        pytest.param(_overwrite_items	,				id="""overwrite items"""		),
        pytest.param(_delete_items	,				id="""delete items"""		),
        pytest.param(_access_absent_items	,				id="""access absent items"""		),
        pytest.param(_add_with_resize_up	,				id="""add with resize up"""		),
        pytest.param(_add_with_resize_down	,				id="""add with resize down"""		),
    )	,				)
def       __lowerCamelCase  (					_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		List[str]							     =		HashMap(initial_block_size=4		)
							UpperCAmelCase     :		Dict							     =		{}
							for _, (fun, *args) in enumerate(_lowercase		):
														UpperCAmelCase     ,     UpperCAmelCase     :		Union[str, Any]							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														UpperCAmelCase     ,     UpperCAmelCase     :		Any							     =		_run_operation(_lowercase	,				_lowercase	,				*_lowercase		)
														assert my_res == py_res
														assert str(_lowercase		) == str(_lowercase		)
														assert set(_lowercase		) == set(_lowercase		)
														assert len(_lowercase		) == len(_lowercase		)
														assert set(my.items()		) == set(py.items()		)



def       __lowerCamelCase  (					)      ->			List[Any]:
							def is_public(_lowercase		) -> bool:
														return not name.startswith("""_"""		)

							UpperCAmelCase     :		int							     =		{name for name in dir({}		) if is_public(_lowercase		)}
							UpperCAmelCase     :		Any							     =		{name for name in dir(HashMap()		) if is_public(_lowercase		)}

							assert dict_public_names > hash_public_names

