






"""simple docstring"""
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_ (__a  ):
 __magic_name__			   =       ['''image_processor''']
 __magic_name__			   =       '''SamImageProcessor'''





 def __init__(   self      :				Any			,			lowerCAmelCase_      :				List[str]		)		->    Union[str, Any]:
    super().__init__(snake_case__		)
    UpperCAmelCase_  :				Optional[int]      =      self.image_processor
    UpperCAmelCase_  :				int      =      -10
    UpperCAmelCase_  :				str      =      self.image_processor.size["longest_edge"]





 def __call__(   self      :				int			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Union[str, Any]=None			,			lowerCAmelCase_      :				Optional[Any]=None			,			lowerCAmelCase_      :				Union[str, Any]=None			,			lowerCAmelCase_      :				Optional[Union[str, TensorType]] = None			,			**lowerCAmelCase_      :				int			,			)		->    Union[str, Any]:
    UpperCAmelCase_  :				int      =      self.image_processor(
        snake_case__			,			return_tensors=snake_case__			,			**snake_case__			,			)

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

    if hasattr(snake_case__			,			"numpy"		):  # Checks if Torch or TF tensor
       UpperCAmelCase_  :				List[str]      =      original_sizes.numpy()

    UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  :				int      =      self._check_and_preprocess_points(
        input_points=snake_case__			,			input_labels=snake_case__			,			input_boxes=snake_case__			,			)

    UpperCAmelCase_  :				Dict      =      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   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				List[Any]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				str=None			,			lowerCAmelCase_      :				Optional[int]=None			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Tuple="pt"			,			)		->    Optional[int]:
    if input_points is not None:
       if len(snake_case__		) != len(snake_case__		):
          UpperCAmelCase_  :				int      =      [
              self._normalize_coordinates(self.target_size			,			snake_case__			,			original_sizes[0]		) for point in input_points
          ]
       else:
          UpperCAmelCase_  :				Union[str, Any]      =      [
              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:
             UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      self._pad_points_and_labels(snake_case__			,			snake_case__		)

       UpperCAmelCase_  :				str      =      np.array(snake_case__		)

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

    if input_boxes is not None:
       if len(snake_case__		) != len(snake_case__		):
          UpperCAmelCase_  :				str      =      [
              self._normalize_coordinates(self.target_size			,			snake_case__			,			original_sizes[0]			,			is_bounding_box=snake_case__		)
              for box in input_boxes
          ]
       else:
          UpperCAmelCase_  :				Optional[Any]      =      [
              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__		)
          ]
       UpperCAmelCase_  :				int      =      np.array(snake_case__		)

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





 def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				np.ndarray			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Optional[Any]=False		)		->    Union[str, Any]:
    UpperCAmelCase_  ,			UpperCAmelCase_  :				Dict      =      original_size
    UpperCAmelCase_  ,			UpperCAmelCase_  :				int      =      self.image_processor._get_preprocess_shape(snake_case__			,			longest_edge=snake_case__		)
    UpperCAmelCase_  :				Any      =      deepcopy(snake_case__		).astype(snake_case__		)

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

    UpperCAmelCase_  :				Optional[int]      =      coords[..., 0] * (new_w / old_w)
    UpperCAmelCase_  :				Tuple      =      coords[..., 1] * (new_h / old_h)

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

    return coords





 def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				Tuple=None			,			lowerCAmelCase_      :				Optional[int]=None			,			lowerCAmelCase_      :				Dict=None			,			)		->    Tuple:
    if input_points is not None:
       if hasattr(snake_case__			,			"numpy"		):  # Checks for TF or Torch tensor
          UpperCAmelCase_  :				Tuple      =      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."		)
       UpperCAmelCase_  :				List[str]      =      [np.array(snake_case__		) for input_point in input_points]
    else:
       UpperCAmelCase_  :				List[str]      =      None

    if input_labels is not None:
       if hasattr(snake_case__			,			"numpy"		):
          UpperCAmelCase_  :				str      =      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."		)
       UpperCAmelCase_  :				Optional[int]      =      [np.array(snake_case__		) for label in input_labels]
    else:
       UpperCAmelCase_  :				Optional[Any]      =      None

    if input_boxes is not None:
       if hasattr(snake_case__			,			"numpy"		):
          UpperCAmelCase_  :				Optional[Any]      =      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."		)
       UpperCAmelCase_  :				str      =      [np.array(snake_case__		).astype(np.floataa		) for box in input_boxes]
    else:
       UpperCAmelCase_  :				str      =      None

    return input_points, input_labels, input_boxes





 @property
 def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[int]		)		->    Optional[int]:
    UpperCAmelCase_  :				Union[str, Any]      =      self.image_processor.model_input_names
    return list(dict.fromkeys(snake_case__		)		)





 def   _SCREAMING_SNAKE_CASE   (   self      :				Any			,			*lowerCAmelCase_      :				Optional[int]			,			**lowerCAmelCase_      :				Optional[Any]		)		->    List[str]:
    return self.image_processor.post_process_masks(*snake_case__			,			**snake_case__		)











"""simple docstring"""
from itertools import count

def        snake_case   (     A__ = 50		):
			UpperCAmelCase_  :				Any      =      [1] * min_block_length

			for n in count(A__		):
						fill_count_functions.append(1		)

						for block_length in range(A__			,n + 1		):
									for block_start in range(n - block_length		):
												fill_count_functions[n] += fill_count_functions[
												    n - block_start - block_length - 1
												]

									fill_count_functions[n] += 1

						if fill_count_functions[n] > 1_00_00_00:
									break

			return n


if __name__ == "__main__":
			print(f'{solution() = }')



