

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     ['''image_processor''']
			_UpperCAmelCase   :							List[str]										=     '''SamImageProcessor'''



			def __init__( self  :     str   ,						lowerCAmelCase__  :     Any):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.image_processor
									SCREAMING_SNAKE_CASE_: List[Any]  		=       -10
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.image_processor.size["longest_edge"]



			def __call__( self  :     str   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     List[Any]=None   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     int=None   ,						lowerCAmelCase__  :     Optional[Union[str, TensorType]] = None   ,						**lowerCAmelCase__  :     str   ,						):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.image_processor(
									    lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						**lowerCAmelCase__   ,						)

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

									if hasattr(lowerCAmelCase__   ,						"numpy"):  # Checks if Torch or TF tensor
															SCREAMING_SNAKE_CASE_: Any  		=       original_sizes.numpy()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       self._check_and_preprocess_points(
									    input_points=lowerCAmelCase__   ,						input_labels=lowerCAmelCase__   ,						input_boxes=lowerCAmelCase__   ,						)

									SCREAMING_SNAKE_CASE_: int  		=       self._normalize_and_convert(
									    lowerCAmelCase__   ,						lowerCAmelCase__   ,						input_points=lowerCAmelCase__   ,						input_labels=lowerCAmelCase__   ,						input_boxes=lowerCAmelCase__   ,						return_tensors=lowerCAmelCase__   ,						)

									return encoding_image_processor



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     str=None   ,						lowerCAmelCase__  :     Tuple=None   ,						lowerCAmelCase__  :     int="pt"   ,						):
									if input_points is not None:
															if len(lowerCAmelCase__) != len(lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Any  		=       [
																					    self._normalize_coordinates(self.target_size   ,						lowerCAmelCase__   ,						original_sizes[0]) for point in input_points
																					]
															else:
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       [
																					    self._normalize_coordinates(self.target_size   ,						lowerCAmelCase__   ,						lowerCAmelCase__)
																					    for point, original_size in zip(lowerCAmelCase__   ,						lowerCAmelCase__)
																					]
															# 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:
																											SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self._pad_points_and_labels(lowerCAmelCase__   ,						lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       np.array(lowerCAmelCase__)

									if input_labels is not None:
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       np.array(lowerCAmelCase__)

									if input_boxes is not None:
															if len(lowerCAmelCase__) != len(lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: List[str]  		=       [
																					    self._normalize_coordinates(self.target_size   ,						lowerCAmelCase__   ,						original_sizes[0]   ,						is_bounding_box=lowerCAmelCase__)
																					    for box in input_boxes
																					]
															else:
																					SCREAMING_SNAKE_CASE_: str  		=       [
																					    self._normalize_coordinates(self.target_size   ,						lowerCAmelCase__   ,						lowerCAmelCase__   ,						is_bounding_box=lowerCAmelCase__)
																					    for box, original_size in zip(lowerCAmelCase__   ,						lowerCAmelCase__)
																					]
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       np.array(lowerCAmelCase__)

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     np.ndarray   ,						lowerCAmelCase__  :     str   ,						lowerCAmelCase__  :     Optional[Any]=False):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       original_size
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.image_processor._get_preprocess_shape(lowerCAmelCase__   ,						longest_edge=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       deepcopy(lowerCAmelCase__).astype(lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: int  		=       coords[..., 0] * (new_w / old_w)
									SCREAMING_SNAKE_CASE_: List[str]  		=       coords[..., 1] * (new_h / old_h)

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

									return coords



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Any=None   ,						lowerCAmelCase__  :     List[str]=None   ,						lowerCAmelCase__  :     Dict=None   ,						):
									if input_points is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):  # Checks for TF or Torch tensor
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       input_points.numpy().tolist()

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

									if input_labels is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):
																					SCREAMING_SNAKE_CASE_: List[str]  		=       input_labels.numpy().tolist()

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

									if input_boxes is not None:
															if hasattr(lowerCAmelCase__   ,						"numpy"):
																					SCREAMING_SNAKE_CASE_: Tuple  		=       input_boxes.numpy().tolist()

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

									return input_points, input_labels, input_boxes



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.image_processor.model_input_names
									return list(dict.fromkeys(lowerCAmelCase__))




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						*lowerCAmelCase__  :     Optional[Any]   ,						**lowerCAmelCase__  :     Union[str, Any]):
									return self.image_processor.post_process_masks(*lowerCAmelCase__   ,						**lowerCAmelCase__)









def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       [redshift, radiation_density, matter_density, dark_energy]
						if any(p < 0 for p in parameters    ):
												raise ValueError("All input parameters must be positive"    )

						if any(p > 1 for p in parameters[1:4]    ):
												raise ValueError("Relative densities cannot be greater than one"    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       1 - (matter_density + radiation_density + dark_energy)

												SCREAMING_SNAKE_CASE_: Dict  		=       (
												    radiation_density * (redshift + 1) ** 4
												    + matter_density * (redshift + 1) ** 3
												    + curvature * (redshift + 1) ** 2
												    + dark_energy
												)

												SCREAMING_SNAKE_CASE_: Any  		=       hubble_constant * e_a ** (1 / 2)
												return hubble


if __name__ == "__main__":
						import doctest

						# run doctest
						doctest.testmod()

						# demo LCDM approximation
						lowerCAmelCase							:  List[Any]   	=						0.3

						print(
						    hubble_parameter(
						        hubble_constant=68.3,
						        radiation_density=1E-4,
						        matter_density=matter_density,
						        dark_energy=1 - matter_density,
						        redshift=0,
						    )
						)






