






'''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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					["image_processor"]
			snake_case__	=					"SamImageProcessor"






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Dict       )      ->      Optional[int]:
							super().__init__(__SCREAMING_SNAKE_CASE       )
							a_		:	int									=   self.image_processor
							a_		:	Optional[int]									=   -10
							a_		:	int									=   self.image_processor.size['''longest_edge''']






			def __call__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      str=None	,    __SCREAMING_SNAKE_CASE   :      List[str]=None	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=None	,    __SCREAMING_SNAKE_CASE   :      List[Any]=None	,    __SCREAMING_SNAKE_CASE   :      Optional[Union[str, TensorType]] = None	,    **__SCREAMING_SNAKE_CASE   :      Optional[Any]	,    )      ->      BatchEncoding:
							a_		:	Any									=   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
							a_		:	Dict									=   encoding_image_processor['''original_sizes''']

							if hasattr(__SCREAMING_SNAKE_CASE	,    '''numpy'''       ):  # Checks if Torch or TF tensor
											a_		:	Tuple									=   original_sizes.numpy()

							a_					,      a_					,      a_		:	Optional[int]									=   self._check_and_preprocess_points(
							    input_points=__SCREAMING_SNAKE_CASE	,    input_labels=__SCREAMING_SNAKE_CASE	,    input_boxes=__SCREAMING_SNAKE_CASE	,    )

							a_		:	int									=   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       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      List[Any]=None	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    __SCREAMING_SNAKE_CASE   :      List[str]="pt"	,    )      ->      str:
							if input_points is not None:
											if len(__SCREAMING_SNAKE_CASE       ) != len(__SCREAMING_SNAKE_CASE       ):
															a_		:	List[Any]									=   [
															    self._normalize_coordinates(self.target_size	,    __SCREAMING_SNAKE_CASE	,    original_sizes[0]       ) for point in input_points
															]
											else:
															a_		:	List[Any]									=   [
															    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:
																			a_					,      a_		:	Dict									=   self._pad_points_and_labels(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

											a_		:	Tuple									=   np.array(__SCREAMING_SNAKE_CASE       )

							if input_labels is not None:
											a_		:	Optional[Any]									=   np.array(__SCREAMING_SNAKE_CASE       )

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

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






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      np.ndarray	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=False       )      ->      np.ndarray:
							a_					,      a_		:	Dict									=   original_size
							a_					,      a_		:	Dict									=   self.image_processor._get_preprocess_shape(__SCREAMING_SNAKE_CASE	,    longest_edge=__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   deepcopy(__SCREAMING_SNAKE_CASE       ).astype(__SCREAMING_SNAKE_CASE       )

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

							a_		:	Union[str, Any]									=   coords[..., 0] * (new_w / old_w)
							a_		:	Any									=   coords[..., 1] * (new_h / old_h)

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

							return coords






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=None	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    __SCREAMING_SNAKE_CASE   :      Dict=None	,    )      ->      Optional[Any]:
							if input_points is not None:
											if hasattr(__SCREAMING_SNAKE_CASE	,    '''numpy'''       ):  # Checks for TF or Torch tensor
															a_		:	List[Any]									=   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.'''       )
											a_		:	Dict									=   [np.array(__SCREAMING_SNAKE_CASE       ) for input_point in input_points]
							else:
											a_		:	Union[str, Any]									=   None

							if input_labels is not None:
											if hasattr(__SCREAMING_SNAKE_CASE	,    '''numpy'''       ):
															a_		:	Dict									=   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.'''       )
											a_		:	Any									=   [np.array(__SCREAMING_SNAKE_CASE       ) for label in input_labels]
							else:
											a_		:	Tuple									=   None

							if input_boxes is not None:
											if hasattr(__SCREAMING_SNAKE_CASE	,    '''numpy'''       ):
															a_		:	List[str]									=   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.'''       )
											a_		:	Tuple									=   [np.array(__SCREAMING_SNAKE_CASE       ).astype(np.floataa       ) for box in input_boxes]
							else:
											a_		:	Dict									=   None

							return input_points, input_labels, input_boxes






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Tuple:
							a_		:	List[str]									=   self.image_processor.model_input_names
							return list(dict.fromkeys(__SCREAMING_SNAKE_CASE       )       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]	,    *__SCREAMING_SNAKE_CASE   :      int	,    **__SCREAMING_SNAKE_CASE   :      List[str]       )      ->      Union[str, Any]:
							return self.image_processor.post_process_masks(*__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )












'''simple docstring'''







import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	Union[str, Any]									=   FileLock(str(tmpdir / '''foo.lock'''   )   )
				a_		:	List[Any]									=   0.01
				with locka.acquire():
								with pytest.raises(__A   ):
												a_		:	List[Any]									=   time.time()
												locka.acquire(__A   )
												assert time.time() - _start > timeout





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Tuple									=   '''a''' * 10_00 + '''.lock'''
				a_		:	List[Any]									=   FileLock(str(tmpdir / filename   )   )
				assert locka._lock_file.endswith('''.lock'''   )
				assert not locka._lock_file.endswith(__A   )
				assert len(os.path.basename(locka._lock_file   )   ) <= 2_55
				a_		:	Optional[int]									=   FileLock(tmpdir / filename   )
				with locka.acquire():
								with pytest.raises(__A   ):
												locka.acquire(0   )




