



"""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


_lowercase			:			Any										=						logging.get_logger(__name__)

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							['pixel_values']



					def __init__(	self							:  List[str],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  int = 8,  **lowerCamelCase							:  Tuple,  )-> None:
								super().__init__(**lowerCamelCase					)

								lowerCamelCase__		:		Union[str, Any]		     =do_rescale
								lowerCamelCase__		:		Any		     =rescale_factor
								lowerCamelCase__		:		Any		     =do_pad
								lowerCamelCase__		:		List[Any]		     =pad_size



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  float,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  int					)-> np.ndarray:
								return rescale(lowerCamelCase,  scale=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase					)



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None					)-> Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]		     =get_image_size(lowerCamelCase					)
								lowerCamelCase__		:		Tuple		     =(old_height // size + 1) * size - old_height
								lowerCamelCase__		:		Tuple		     =(old_width // size + 1) * size - old_width

								return pad(lowerCamelCase,  ((0, pad_height), (0, pad_width)),  mode='''symmetric''',  data_format=lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Union[str, Any],  )-> Union[str, Any]:
								lowerCamelCase__		:		List[Any]		     =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		Optional[int]		     =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		Dict		     =do_pad if do_pad is not None else self.do_pad
								lowerCamelCase__		:		Optional[int]		     =pad_size if pad_size is not None else self.pad_size

								lowerCamelCase__		:		int		     =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_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''					)

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

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

								if do_pad:
											lowerCamelCase__		:		Dict		     =[self.pad(lowerCamelCase,  size=lowerCamelCase					) for image in images]

								lowerCamelCase__		:		str		     =[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase					) for image in images]

								lowerCamelCase__		:		Optional[int]		     ={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase					)









"""simple docstring"""






from __future__ import annotations






def 					snake_case__       (		__lowerCamelCase    :   str							,		__lowerCamelCase    :   list[str] | None = None     ):
			"""simple docstring"""
			lowerCamelCase__		:		List[Any]		     =word_bank or []
			# create a table
			lowerCamelCase__		:		int		     =len(__lowerCamelCase     ) + 1

			lowerCamelCase__		:		list[list[list[str]]]		     =[]
			for _ in range(__lowerCamelCase     ):
						table.append([]     )
			# seed value
			lowerCamelCase__		:		Tuple		     =[[]]  # because empty string has empty combination

			# iterate through the indices
			for i in range(__lowerCamelCase     ):
						# condition
						if table[i] != []:
									for word in word_bank:
												# slice condition
												if target[i : i + len(__lowerCamelCase     )] == word:
															lowerCamelCase__		:		list[list[str]]		     =[
															    [word, *way] for way in table[i]
															]
															# adds the word to every combination the current position holds
															# now,push that combination to the table[i+len(word)]
															table[i + len(__lowerCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
			for combination in table[len(__lowerCamelCase     )]:
						combination.reverse()

			return table[len(__lowerCamelCase     )]


if __name__ == "__main__":
		print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
		print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
		print(
		    all_construct(
		        "hexagonosaurus",
		        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
		    )
		)




