
'''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_				(	snake_case__					):
 UpperCAmelCase_		    =						["""image_processor"""]
 UpperCAmelCase_		    =						"""SamImageProcessor"""





 def __init__(    self ,       lowercase_):
     super().__init__(lowercase_)
     snake_case_ :    Optional[Any]     			=						self.image_processor
     snake_case_ :    str     			=						-10
     snake_case_ :    Dict     			=						self.image_processor.size["longest_edge"]





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_ = None ,       **lowercase_ ,       ):
     snake_case_ :    Tuple     			=						self.image_processor(
         lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_ ,       )

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

     if hasattr(lowercase_ ,       "numpy"):  # Checks if Torch or TF tensor
         snake_case_ :    List[Any]     			=						original_sizes.numpy()

     snake_case_			, snake_case_			, snake_case_ :    List[Any]     			=						self._check_and_preprocess_points(
         input_points=lowercase_ ,       input_labels=lowercase_ ,       input_boxes=lowercase_ ,       )

     snake_case_ :    List[str]     			=						self._normalize_and_convert(
         lowercase_ ,       lowercase_ ,       input_points=lowercase_ ,       input_labels=lowercase_ ,       input_boxes=lowercase_ ,       return_tensors=lowercase_ ,       )

     return encoding_image_processor





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_="pt" ,       ):







     if input_points is not None:
         if len(lowercase_) != len(lowercase_):
             snake_case_ :    str     			=						[
                 self._normalize_coordinates(self.target_size ,       lowercase_ ,       original_sizes[0]) for point in input_points
             ]
         else:
             snake_case_ :    Dict     			=						[
                 self._normalize_coordinates(self.target_size ,       lowercase_ ,       lowercase_)
                 for point, original_size in zip(lowercase_ ,       lowercase_)
             ]
         # 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_			, snake_case_ :    Union[str, Any]     			=						self._pad_points_and_labels(lowercase_ ,       lowercase_)

         snake_case_ :    List[str]     			=						np.array(lowercase_)

     if input_labels is not None:
         snake_case_ :    Any     			=						np.array(lowercase_)

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

     if input_boxes is not None:
         if return_tensors == "pt":
             snake_case_ :    Optional[int]     			=						torch.from_numpy(lowercase_)
             # boxes batch size of 1 by default
             snake_case_ :    Dict     			=						input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
         elif return_tensors == "tf":
             snake_case_ :    Dict     			=						tf.convert_to_tensor(lowercase_)
             # boxes batch size of 1 by default
             snake_case_ :    Optional[int]     			=						tf.expand_dims(lowercase_ ,       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_ :    Tuple     			=						torch.from_numpy(lowercase_)
             # point batch size of 1 by default
             snake_case_ :    Union[str, Any]     			=						input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
         elif return_tensors == "tf":
             snake_case_ :    Any     			=						tf.convert_to_tensor(lowercase_)
             # point batch size of 1 by default
             snake_case_ :    Dict     			=						tf.expand_dims(lowercase_ ,       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_ :    int     			=						torch.from_numpy(lowercase_)
             # point batch size of 1 by default
             snake_case_ :    int     			=						input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
         elif return_tensors == "tf":
             snake_case_ :    Optional[Any]     			=						tf.convert_to_tensor(lowercase_)
             # point batch size of 1 by default
             snake_case_ :    List[str]     			=						tf.expand_dims(lowercase_ ,       1) if len(input_labels.shape) != 3 else input_labels
         encoding_image_processor.update({"input_labels": input_labels})

     return encoding_image_processor





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     snake_case_ :    Tuple     			=						max([point.shape[0] for point in input_points])
     snake_case_ :    List[Any]     			=						[]
     for i, point in enumerate(lowercase_):
         if point.shape[0] != expected_nb_points:
             snake_case_ :    int     			=						np.concatenate(
                 [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value] ,       axis=0)
             snake_case_ :    Any     			=						np.append(input_labels[i] ,       [self.point_pad_value])
         processed_input_points.append(lowercase_)
     snake_case_ :    List[str]     			=						processed_input_points
     return input_points, input_labels





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_=False):
     snake_case_			, snake_case_ :    Union[str, Any]     			=						original_size
     snake_case_			, snake_case_ :    Dict     			=						self.image_processor._get_preprocess_shape(lowercase_ ,       longest_edge=lowercase_)
     snake_case_ :    Any     			=						deepcopy(lowercase_).astype(lowercase_)

     if is_bounding_box:
         snake_case_ :    Union[str, Any]     			=						coords.reshape(-1 ,       2 ,       2)

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

     if is_bounding_box:
         snake_case_ :    List[str]     			=						coords.reshape(-1 ,       4)

     return coords





 def        snake_case__		(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       ):







     if input_points is not None:
         if hasattr(lowercase_ ,       "numpy"):  # Checks for TF or Torch tensor
             snake_case_ :    List[str]     			=						input_points.numpy().tolist()

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

     if input_labels is not None:
         if hasattr(lowercase_ ,       "numpy"):
             snake_case_ :    Optional[int]     			=						input_labels.numpy().tolist()

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

     if input_boxes is not None:
         if hasattr(lowercase_ ,       "numpy"):
             snake_case_ :    Optional[int]     			=						input_boxes.numpy().tolist()

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

     return input_points, input_labels, input_boxes





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.image_processor.model_input_names
     return list(dict.fromkeys(lowercase_))





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.image_processor.post_process_masks(*lowercase_ ,       **lowercase_)






'''simple docstring'''
from __future__ import annotations

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						0.00
    snake_case_ :    int     			=						0
    for resistor in resistors:
        if resistor <= 0:
            snake_case_ :    Dict     			=						f'Resistor at index {index} has a negative or zero value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        first_sum += 1 / float(__SCREAMING_SNAKE_CASE       )
        index += 1
    return 1 / first_sum

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						0.00
    snake_case_ :    List[Any]     			=						0
    for resistor in resistors:
        sum_r += resistor
        if resistor < 0:
            snake_case_ :    str     			=						f'Resistor at index {index} has a negative value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        index += 1
    return sum_r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




