



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      UpperCamelCase_						(					_lowerCamelCase   ):
					lowerCAmelCase_							=		['''image_processor''']
					lowerCAmelCase_							=		'''SamImageProcessor'''




					def __init__(						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										super().__init__(_SCREAMING_SNAKE_CASE					)
										_snake_case													=			self.image_processor
										_snake_case													=			-10
										_snake_case													=			self.image_processor.size['''longest_edge''']




					def __call__(						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_ = None			,  **lowerCAmelCase_			,  )						->							BatchEncoding:
										_snake_case													=			self.image_processor(
										    _SCREAMING_SNAKE_CASE			,  return_tensors=_SCREAMING_SNAKE_CASE			,  **_SCREAMING_SNAKE_CASE			,  )

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

										if hasattr(_SCREAMING_SNAKE_CASE			,  'numpy'					):  # Checks if Torch or TF tensor
															_snake_case													=			original_sizes.numpy()

										_snake_case													=			self._check_and_preprocess_points(
										    input_points=_SCREAMING_SNAKE_CASE			,  input_labels=_SCREAMING_SNAKE_CASE			,  input_boxes=_SCREAMING_SNAKE_CASE			,  )

										_snake_case													=			self._normalize_and_convert(
										    _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE			,  input_points=_SCREAMING_SNAKE_CASE			,  input_labels=_SCREAMING_SNAKE_CASE			,  input_boxes=_SCREAMING_SNAKE_CASE			,  return_tensors=_SCREAMING_SNAKE_CASE			,  )

										return encoding_image_processor




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_="pt"			,  )						->							Dict:





										if input_points is not None:
															if len(_SCREAMING_SNAKE_CASE					) != len(_SCREAMING_SNAKE_CASE					):
																				_snake_case													=			[
																				    self._normalize_coordinates(self.target_size			,  _SCREAMING_SNAKE_CASE			,  original_sizes[0]					) for point in input_points
																				]
															else:
																				_snake_case													=			[
																				    self._normalize_coordinates(self.target_size			,  _SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE					)
																				    for point, original_size in zip(_SCREAMING_SNAKE_CASE			,  _SCREAMING_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:
																									_snake_case													=			self._pad_points_and_labels(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE					)

															_snake_case													=			np.array(_SCREAMING_SNAKE_CASE					)

										if input_labels is not None:
															_snake_case													=			np.array(_SCREAMING_SNAKE_CASE					)

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

										if input_boxes is not None:
															if return_tensors == "pt":
																				_snake_case													=			torch.from_numpy(_SCREAMING_SNAKE_CASE					)
																				# boxes batch size of 1 by default
																				_snake_case													=			input_boxes.unsqueeze(1					) if len(input_boxes.shape					) != 3 else input_boxes
															elif return_tensors == "tf":
																				_snake_case													=			tf.convert_to_tensor(_SCREAMING_SNAKE_CASE					)
																				# boxes batch size of 1 by default
																				_snake_case													=			tf.expand_dims(_SCREAMING_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":
																				_snake_case													=			torch.from_numpy(_SCREAMING_SNAKE_CASE					)
																				# point batch size of 1 by default
																				_snake_case													=			input_points.unsqueeze(1					) if len(input_points.shape					) != 4 else input_points
															elif return_tensors == "tf":
																				_snake_case													=			tf.convert_to_tensor(_SCREAMING_SNAKE_CASE					)
																				# point batch size of 1 by default
																				_snake_case													=			tf.expand_dims(_SCREAMING_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":
																				_snake_case													=			torch.from_numpy(_SCREAMING_SNAKE_CASE					)
																				# point batch size of 1 by default
																				_snake_case													=			input_labels.unsqueeze(1					) if len(input_labels.shape					) != 3 else input_labels
															elif return_tensors == "tf":
																				_snake_case													=			tf.convert_to_tensor(_SCREAMING_SNAKE_CASE					)
																				# point batch size of 1 by default
																				_snake_case													=			tf.expand_dims(_SCREAMING_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 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_					)						->							Dict:
										_snake_case													=			max([point.shape[0] for point in input_points]					)
										_snake_case													=			[]
										for i, point in enumerate(_SCREAMING_SNAKE_CASE					):
															if point.shape[0] != expected_nb_points:
																				_snake_case													=			np.concatenate(
																				    [point, np.zeros((expected_nb_points - point.shape[0], 2)					) + self.point_pad_value]			,  axis=0					)
																				_snake_case													=			np.append(input_labels[i]			,  [self.point_pad_value]					)
															processed_input_points.append(_SCREAMING_SNAKE_CASE					)
										_snake_case													=			processed_input_points
										return input_points, input_labels




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=False					)						->							np.ndarray:
										_snake_case													=			original_size
										_snake_case													=			self.image_processor._get_preprocess_shape(_SCREAMING_SNAKE_CASE			,  longest_edge=_SCREAMING_SNAKE_CASE					)
										_snake_case													=			deepcopy(_SCREAMING_SNAKE_CASE					).astype(_SCREAMING_SNAKE_CASE					)

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

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

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

										return coords




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  lowerCAmelCase_=None			,  )						->							str:





										if input_points is not None:
															if hasattr(_SCREAMING_SNAKE_CASE			,  'numpy'					):  # Checks for TF or Torch tensor
																				_snake_case													=			input_points.numpy().tolist()

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

										if input_labels is not None:
															if hasattr(_SCREAMING_SNAKE_CASE			,  'numpy'					):
																				_snake_case													=			input_labels.numpy().tolist()

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

										if input_boxes is not None:
															if hasattr(_SCREAMING_SNAKE_CASE			,  'numpy'					):
																				_snake_case													=			input_boxes.numpy().tolist()

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

										return input_points, input_labels, input_boxes




					@property
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			self.image_processor.model_input_names
										return list(dict.fromkeys(_SCREAMING_SNAKE_CASE					)					)






					def 		lowerCAmelCase     (						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							Union[str, Any]:
										return self.image_processor.post_process_masks(*_SCREAMING_SNAKE_CASE			,  **_SCREAMING_SNAKE_CASE					)










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



