





'''simple docstring'''







from operator import delitem, getitem, setitem

import pytest

from data_structures.hashing.hash_map import HashMap





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Optional[Any]:
						return getitem, k





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			List[Any],			SCREAMING_SNAKE_CASE__      :			Union[str, Any]   ) ->	Optional[int]:
						return setitem, k, v





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Optional[Any]   ) ->	Any:
						return delitem, k





def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			Union[str, Any],			SCREAMING_SNAKE_CASE__      :			Tuple,			*SCREAMING_SNAKE_CASE__      :			List[str]   ) ->	Dict:
						try:
												return fun(SCREAMING_SNAKE_CASE__,			*SCREAMING_SNAKE_CASE__   ), None
						except Exception as e:
												return None, e


snake_case_     :       Optional[Any]						=  (
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
)

snake_case_     :       Optional[int]						=  [
    _set("key_a", "val_a"),
    _set("key_a", "val_b"),
]

snake_case_     :       Dict						=  [
    _set("key_a", "val_a"),
    _set("key_b", "val_b"),
    _del("key_a"),
    _del("key_b"),
    _set("key_a", "val_a"),
    _del("key_a"),
]

snake_case_     :       List[str]						=  [
    _get("key_a"),
    _del("key_a"),
    _set("key_a", "val_a"),
    _del("key_a"),
    _del("key_a"),
    _get("key_a"),
]

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

snake_case_     :       int						=  [
    *[_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_      (  SCREAMING_SNAKE_CASE__      :			Tuple   ) ->	str:
						UpperCAmelCase_			:      Tuple     						=  HashMap(initial_block_size=4   )
						UpperCAmelCase_			:      Dict     						=  {}
						for _, (fun, *args) in enumerate(SCREAMING_SNAKE_CASE__   ):
												UpperCAmelCase_						,       UpperCAmelCase_			:      Union[str, Any]     						=  _run_operation(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			*SCREAMING_SNAKE_CASE__   )
												UpperCAmelCase_						,       UpperCAmelCase_			:      str     						=  _run_operation(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__,			*SCREAMING_SNAKE_CASE__   )
												assert my_res == py_res
												assert str(SCREAMING_SNAKE_CASE__   ) == str(SCREAMING_SNAKE_CASE__   )
												assert set(SCREAMING_SNAKE_CASE__   ) == set(SCREAMING_SNAKE_CASE__   )
												assert len(SCREAMING_SNAKE_CASE__   ) == len(SCREAMING_SNAKE_CASE__   )
												assert set(my.items()   ) == set(py.items()   )





def        lowerCamelCase_      (  ) ->	int:
						def is_public(SCREAMING_SNAKE_CASE__      :			str   ) -> bool:
												return not name.startswith('''_'''   )

						UpperCAmelCase_			:      Optional[int]     						=  {name for name in dir({}   ) if is_public(SCREAMING_SNAKE_CASE__   )}
						UpperCAmelCase_			:      Dict     						=  {name for name in dir(HashMap()   ) if is_public(SCREAMING_SNAKE_CASE__   )}

						assert dict_public_names > hash_public_names












'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case_     :       Union[str, Any]						=  logging.get_logger(__name__)


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

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







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

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







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



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







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

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

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





