
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  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = ["""image_processor"""]
			__UpperCamelCase      :						Any					      = """SamImageProcessor"""

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

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

							# pop arguments that are not used in the foward but used nevertheless
							UpperCamelCase_:							Optional[Any]	=						encoding_image_processor["""original_sizes"""]

							if hasattr(snake_case_						,	"""numpy"""      ):  # Checks if Torch or TF tensor
											UpperCamelCase_:							Optional[int]	=						original_sizes.numpy()

							UpperCamelCase_:							Optional[int]	=						self._check_and_preprocess_points(
							    input_points=snake_case_						,	input_labels=snake_case_						,	input_boxes=snake_case_						,	)

							UpperCamelCase_:							Any	=						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       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Optional[int]						,	snake_case_ :       List[str]						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Optional[Any]=None						,	snake_case_ :       Dict="pt"						,	):
							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_:							Dict	=						[
															    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_:							Union[str, Any]	=						self._pad_points_and_labels(snake_case_						,	snake_case_      )

											UpperCamelCase_:							Optional[int]	=						np.array(snake_case_      )

							if input_labels is not None:
											UpperCamelCase_:							Tuple	=						np.array(snake_case_      )

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

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

							if is_bounding_box:
											UpperCamelCase_:							Optional[Any]	=						coords.reshape(-1						,	2						,	2      )

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

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

							return coords

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       Union[str, Any]=None						,	snake_case_ :       Tuple=None						,	snake_case_ :       Union[str, Any]=None						,	):
							if input_points is not None:
											if hasattr(snake_case_						,	"""numpy"""      ):  # Checks for TF or Torch tensor
															UpperCamelCase_:							str	=						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_:							Tuple	=						[np.array(snake_case_      ) for input_point in input_points]
							else:
											UpperCamelCase_:							int	=						None

							if input_labels is not None:
											if hasattr(snake_case_						,	"""numpy"""      ):
															UpperCamelCase_:							Any	=						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[Any]	=						[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_:							Union[str, 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_:							List[Any]	=						[np.array(snake_case_      ).astype(np.floataa      ) for box in input_boxes]
							else:
											UpperCamelCase_:							Optional[Any]	=						None

							return input_points, input_labels, input_boxes

			@property
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Tuple	=						self.image_processor.model_input_names
							return list(dict.fromkeys(snake_case_      )      )




			def       lowerCAmelCase__	(       self :       Optional[int]						,	*snake_case_ :       Tuple						,	**snake_case_ :       Tuple      ):
							return self.image_processor.post_process_masks(*snake_case_						,	**snake_case_      )








import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Optional[int]	=						logging.get_logger()

							# the current default level is logging.WARNING
							UpperCamelCase_:							Dict	=						logging.get_verbosity()

							logging.set_verbosity_error()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_warning()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_info()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							logging.set_verbosity_debug()
							self.assertEqual(logger.getEffectiveLevel()						,	logging.get_verbosity()      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						logging.get_verbosity()

							UpperCamelCase_:							int	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Union[str, Any]	=						"""Testing 1, 2, 3"""

							# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
							if level_origin <= logging.WARNING:
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )

							# this is setting the level for all of `transformers.*` loggers
							logging.set_verbosity_error()

							# should not be able to log warnings
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	""""""      )

							# should be able to log warnings again
							logging.set_verbosity_warning()
							with CaptureLogger(snake_case_      ) as cl:
											logger.warning(snake_case_      )
							self.assertEqual(cl.out						,	msg + """\n"""      )

							# restore to the original level
							logging.set_verbosity(snake_case_      )

			@mockenv(TRANSFORMERS_VERBOSITY="""error"""      )
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							# this action activates the env var
							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )

							UpperCamelCase_:							str	=						os.getenv("""TRANSFORMERS_VERBOSITY"""						,	snake_case_      )
							UpperCamelCase_:							Any	=						logging.log_levels[env_level_str]

							UpperCamelCase_:							Dict	=						logging.get_verbosity()
							self.assertEqual(
							    snake_case_						,	snake_case_						,	f'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''						,	)

							# restore to the original level
							UpperCamelCase_:							str	=						""""""
							transformers.utils.logging._reset_library_root_logger()

			@mockenv(TRANSFORMERS_VERBOSITY="""super-error"""      )
			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# reset for the env var to take effect, next time some logger call is made
							transformers.utils.logging._reset_library_root_logger()
							UpperCamelCase_:							str	=						logging.logging.getLogger()
							with CaptureLogger(snake_case_      ) as cl:
											# this action activates the env var
											logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							self.assertIn("""Unknown option TRANSFORMERS_VERBOSITY=super-error"""						,	cl.out      )

							# no need to restore as nothing was changed




			def       lowerCAmelCase__	(       self :       List[Any]      ):
							# testing `logger.warning_advice()`
							transformers.utils.logging._reset_library_root_logger()

							UpperCamelCase_:							List[str]	=						logging.get_logger("""transformers.models.bart.tokenization_bart"""      )
							UpperCamelCase_:							Any	=						"""Testing 1, 2, 3"""

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="""1"""      ):
											# nothing should be logged as env var disables this method
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	""""""      )

							with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""""""      ):
											# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
											with CaptureLogger(snake_case_      ) as cl:
															logger.warning_advice(snake_case_      )
											self.assertEqual(cl.out						,	msg + """\n"""      )







def        A__					(						)  ->	Union[str, Any]:
				disable_progress_bar()
				assert are_progress_bars_disabled()

				enable_progress_bar()
				assert not are_progress_bars_disabled()






