
from __future__ import annotations






def 		_UpperCAmelCase   (	UpperCamelCase:     str			,							UpperCamelCase:     list[str] | None = None  ):






	"""simple docstring"""


	__lowerCAmelCase         =							word_bank or []
	# create a table
	__lowerCAmelCase         =							len(UpperCamelCase  ) + 1

	__lowerCAmelCase         =							[]
	for _ in range(UpperCamelCase  ):
		table.append([]  )
	# seed value
	__lowerCAmelCase         =							[[]]  # because empty string has empty combination

	# iterate through the indices
	for i in range(UpperCamelCase  ):
		# condition
		if table[i] != []:
			for word in word_bank:
				# slice condition
				if target[i : i + len(UpperCamelCase  )] == word:
					__lowerCAmelCase         =							[
					    [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(UpperCamelCase  )] += new_combinations

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

	return table[len(UpperCamelCase  )]


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"],
					    )
					)








from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
					import torch

if is_tf_available():
					import tensorflow as tf

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Tuple   							=  ['image_processor']
							lowercase_		:						Tuple   							=  'SamImageProcessor'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								super().__init__(snake_case__ )
								__lowerCAmelCase         =							self.image_processor
								__lowerCAmelCase         =							-10
								__lowerCAmelCase         =							self.image_processor.size["longest_edge"]







							def __call__(						self  :  Any      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  Optional[int]=None      ,				snake_case__  :  str=None      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  Optional[Union[str, TensorType]] = None      ,				**snake_case__  :  Any      ,				):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processor(
								    snake_case__      ,				return_tensors=snake_case__      ,				**snake_case__      ,				)

								# pop arguments that are not used in the foward but used nevertheless
								__lowerCAmelCase         =							encoding_image_processor["original_sizes"]

								if hasattr(snake_case__      ,				"numpy" ):  # Checks if Torch or TF tensor
									__lowerCAmelCase         =							original_sizes.numpy()

								__lowerCAmelCase   ,						__lowerCAmelCase   ,						__lowerCAmelCase         =							self._check_and_preprocess_points(
								    input_points=snake_case__      ,				input_labels=snake_case__      ,				input_boxes=snake_case__      ,				)

								__lowerCAmelCase         =							self._normalize_and_convert(
								    snake_case__      ,				snake_case__      ,				input_points=snake_case__      ,				input_labels=snake_case__      ,				input_boxes=snake_case__      ,				return_tensors=snake_case__      ,				)

								return encoding_image_processor







							def       UpperCAmelCase__					(						self  :  Tuple      ,				snake_case__  :  Any      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  str=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  Any=None      ,				snake_case__  :  List[str]="pt"      ,				):
								"""simple docstring"""







								if input_points is not None:
									if len(snake_case__ ) != len(snake_case__ ):
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				original_sizes[0] ) for point in input_points
										]
									else:
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				snake_case__ )
										    for point, original_size in zip(snake_case__      ,				snake_case__ )
										]
									# check that all arrays have the same shape
									if not all(point.shape == input_points[0].shape for point in input_points ):
										if input_labels is not None:
											__lowerCAmelCase   ,						__lowerCAmelCase         =							self._pad_points_and_labels(snake_case__      ,				snake_case__ )

									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_labels is not None:
									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_boxes is not None:
									if len(snake_case__ ) != len(snake_case__ ):
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				original_sizes[0]      ,				is_bounding_box=snake_case__ )
										    for box in input_boxes
										]
									else:
										__lowerCAmelCase         =							[
										    self._normalize_coordinates(self.target_size      ,				snake_case__      ,				snake_case__      ,				is_bounding_box=snake_case__ )
										    for box, original_size in zip(snake_case__      ,				snake_case__ )
										]
									__lowerCAmelCase         =							np.array(snake_case__ )

								if input_boxes is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# boxes batch size of 1 by default
										__lowerCAmelCase         =							input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# boxes batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_boxes.shape ) != 3 else input_boxes
									encoding_image_processor.update({"input_boxes": input_boxes} )
								if input_points is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_points.shape ) != 4 else input_points
									encoding_image_processor.update({"input_points": input_points} )
								if input_labels is not None:
									if return_tensors == "pt":
										__lowerCAmelCase         =							torch.from_numpy(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
									elif return_tensors == "tf":
										__lowerCAmelCase         =							tf.convert_to_tensor(snake_case__ )
										# point batch size of 1 by default
										__lowerCAmelCase         =							tf.expand_dims(snake_case__      ,				1 ) if len(input_labels.shape ) != 3 else input_labels
									encoding_image_processor.update({"input_labels": input_labels} )

								return encoding_image_processor







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							max([point.shape[0] for point in input_points] )
								__lowerCAmelCase         =							[]
								for i, point in enumerate(snake_case__ ):
									if point.shape[0] != expected_nb_points:
										__lowerCAmelCase         =							np.concatenate(
										    [point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value]      ,				axis=0 )
										__lowerCAmelCase         =							np.append(input_labels[i]      ,				[self.point_pad_value] )
									processed_input_points.append(snake_case__ )
								__lowerCAmelCase         =							processed_input_points
								return input_points, input_labels







							def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  int      ,				snake_case__  :  np.ndarray      ,				snake_case__  :  List[str]      ,				snake_case__  :  Dict=False ):
								"""simple docstring"""







								__lowerCAmelCase   ,						__lowerCAmelCase         =							original_size
								__lowerCAmelCase   ,						__lowerCAmelCase         =							self.image_processor._get_preprocess_shape(snake_case__      ,				longest_edge=snake_case__ )
								__lowerCAmelCase         =							deepcopy(snake_case__ ).astype(snake_case__ )

								if is_bounding_box:
									__lowerCAmelCase         =							coords.reshape(-1      ,				2      ,				2 )

								__lowerCAmelCase         =							coords[..., 0] * (new_w / old_w)
								__lowerCAmelCase         =							coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
									__lowerCAmelCase         =							coords.reshape(-1      ,				4 )

								return coords







							def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Union[str, Any]=None      ,				snake_case__  :  int=None      ,				snake_case__  :  Any=None      ,				):
								"""simple docstring"""







								if input_points is not None:
									if hasattr(snake_case__      ,				"numpy" ):  # Checks for TF or Torch tensor
										__lowerCAmelCase         =							input_points.numpy().tolist()

									if not isinstance(snake_case__      ,				snake_case__ ) or not isinstance(input_points[0]      ,				snake_case__ ):
										raise ValueError("Input points must be a list of list of floating points." )
									__lowerCAmelCase         =							[np.array(snake_case__ ) for input_point in input_points]
								else:
									__lowerCAmelCase         =							None

								if input_labels is not None:
									if hasattr(snake_case__      ,				"numpy" ):
										__lowerCAmelCase         =							input_labels.numpy().tolist()

									if not isinstance(snake_case__      ,				snake_case__ ) or not isinstance(input_labels[0]      ,				snake_case__ ):
										raise ValueError("Input labels must be a list of list integers." )
									__lowerCAmelCase         =							[np.array(snake_case__ ) for label in input_labels]
								else:
									__lowerCAmelCase         =							None

								if input_boxes is not None:
									if hasattr(snake_case__      ,				"numpy" ):
										__lowerCAmelCase         =							input_boxes.numpy().tolist()

									if (
									    not isinstance(snake_case__      ,				snake_case__ )
									    or not isinstance(input_boxes[0]      ,				snake_case__ )
									    or not isinstance(input_boxes[0][0]      ,				snake_case__ )
									):
										raise ValueError("Input boxes must be a list of list of list of floating points." )
									__lowerCAmelCase         =							[np.array(snake_case__ ).astype(np.floataa ) for box in input_boxes]
								else:
									__lowerCAmelCase         =							None

								return input_points, input_labels, input_boxes







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.image_processor.model_input_names
								return list(dict.fromkeys(snake_case__ ) )







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				*snake_case__  :  Dict      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								return self.image_processor.post_process_masks(*snake_case__      ,				**snake_case__ )






