
"""simple docstring"""




from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	from ..models.auto.modeling_auto import (
	    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
	    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
	    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
	    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
	)


snake_case					  =						logging.get_logger(__name__)


snake_case					  =						Dict[str, Any]
snake_case					  =						List[Prediction]




@add_end_docstrings(_snake_case )
class 	UpperCamelCase      (			_snake_case ):

				"""simple docstring"""



				def __init__(							self						,      *lowercase__						,      **lowercase__     )       ->       List[str]:
									"""simple docstring"""







									super().__init__(*lowercase__						,      **lowercase__     )

									if self.framework == "tf":
														raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''     )

									requires_backends(self						,      'vision'     )
									self.check_model_type(
									    dict(
									        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
									        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()     )     )


				def 				A   (							self						,      **lowercase__     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      {}
									SCREAMING_SNAKE_CASE           =      {}
									if "subtask" in kwargs:
														SCREAMING_SNAKE_CASE           =      kwargs['subtask']
														SCREAMING_SNAKE_CASE           =      kwargs['subtask']
									if "threshold" in kwargs:
														SCREAMING_SNAKE_CASE           =      kwargs['threshold']
									if "mask_threshold" in kwargs:
														SCREAMING_SNAKE_CASE           =      kwargs['mask_threshold']
									if "overlap_mask_area_threshold" in kwargs:
														SCREAMING_SNAKE_CASE           =      kwargs['overlap_mask_area_threshold']

									return preprocess_kwargs, {}, postprocess_kwargs


				def __call__(							self						,      lowercase__						,      **lowercase__     )       ->       Union[Predictions, List[Prediction]]:
									"""simple docstring"""







									return super().__call__(lowercase__						,      **lowercase__     )


				def 				A   (							self						,      lowercase__						,      lowercase__=None     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      load_image(lowercase__     )
									SCREAMING_SNAKE_CASE           =      [(image.height, image.width)]
									if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																			SCREAMING_SNAKE_CASE           =      {}
														else:
																			SCREAMING_SNAKE_CASE           =      {'task_inputs': [subtask]}
														SCREAMING_SNAKE_CASE           =      self.image_processor(images=[image]						,      return_tensors='pt'						,      **lowercase__     )
														SCREAMING_SNAKE_CASE           =      self.tokenizer(
														    inputs['task_inputs']						,      padding='max_length'						,      max_length=self.model.config.task_seq_len						,      return_tensors=self.framework						,      )['input_ids']
									else:
														SCREAMING_SNAKE_CASE           =      self.image_processor(images=[image]						,      return_tensors='pt'     )
									SCREAMING_SNAKE_CASE           =      target_size
									return inputs


				def 				A   (							self						,      lowercase__     )       ->       Tuple:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      model_inputs.pop('target_size'     )
									SCREAMING_SNAKE_CASE           =      self.model(**lowercase__     )
									SCREAMING_SNAKE_CASE           =      target_size
									return model_outputs




				def 				A   (							self						,      lowercase__						,      lowercase__=None						,      lowercase__=0.9						,      lowercase__=0.5						,      lowercase__=0.5     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      None
									if subtask in {"panoptic", None} and hasattr(self.image_processor						,      'post_process_panoptic_segmentation'     ):
														SCREAMING_SNAKE_CASE           =      self.image_processor.post_process_panoptic_segmentation
									elif subtask in {"instance", None} and hasattr(self.image_processor						,      'post_process_instance_segmentation'     ):
														SCREAMING_SNAKE_CASE           =      self.image_processor.post_process_instance_segmentation

									if fn is not None:
														SCREAMING_SNAKE_CASE           =      fn(
														    lowercase__						,      threshold=lowercase__						,      mask_threshold=lowercase__						,      overlap_mask_area_threshold=lowercase__						,      target_sizes=model_outputs['target_size']						,      )[0]

														SCREAMING_SNAKE_CASE           =      []
														SCREAMING_SNAKE_CASE           =      outputs['segmentation']

														for segment in outputs["segments_info"]:
																			SCREAMING_SNAKE_CASE           =      (segmentation == segment['id']) * 255
																			SCREAMING_SNAKE_CASE           =      Image.fromarray(mask.numpy().astype(np.uinta     )						,      mode='L'     )
																			SCREAMING_SNAKE_CASE           =      self.model.config.idalabel[segment['label_id']]
																			SCREAMING_SNAKE_CASE           =      segment['score']
																			annotation.append({'score': score, 'label': label, 'mask': mask}     )

									elif subtask in {"semantic", None} and hasattr(self.image_processor						,      'post_process_semantic_segmentation'     ):
														SCREAMING_SNAKE_CASE           =      self.image_processor.post_process_semantic_segmentation(
														    lowercase__						,      target_sizes=model_outputs['target_size']     )[0]

														SCREAMING_SNAKE_CASE           =      []
														SCREAMING_SNAKE_CASE           =      outputs.numpy()
														SCREAMING_SNAKE_CASE           =      np.unique(lowercase__     )

														for label in labels:
																			SCREAMING_SNAKE_CASE           =      (segmentation == label) * 255
																			SCREAMING_SNAKE_CASE           =      Image.fromarray(mask.astype(np.uinta     )						,      mode='L'     )
																			SCREAMING_SNAKE_CASE           =      self.model.config.idalabel[label]
																			annotation.append({'score': None, 'label': label, 'mask': mask}     )
									else:
														raise ValueError(f'''Subtask {subtask} is not supported for model {type(self.model     )}'''     )
									return annotation



'''simple docstring'''







from .balanced_parentheses import balanced_parentheses
from .stack import Stack






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(snake_case_							,       -1	)






def 					_SCREAMING_SNAKE_CASE		(						snake_case_	):
	if not balanced_parentheses(snake_case_	):
		raise ValueError("""Mismatched parentheses"""	)
	_lowercase							=     Stack()
	_lowercase							=     []
	for char in expression_str:
		if char.isalpha() or char.isdigit():
			postfix.append(snake_case_	)
		elif char == "(":
			stack.push(snake_case_	)
		elif char == ")":
			while not stack.is_empty() and stack.peek() != "(":
				postfix.append(stack.pop()	)
			stack.pop()
		else:
			while not stack.is_empty() and precedence(snake_case_	) <= precedence(stack.peek()	):
				postfix.append(stack.pop()	)
			stack.push(snake_case_	)
	while not stack.is_empty():
		postfix.append(stack.pop()	)
	return " ".join(snake_case_	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							_lowerCamelCase    =  'a+b*(c^d-e)^(f+g*h)-i'

							print('Infix to Postfix Notation demonstration:\n')
							print('Infix notation: ' + expression)
							print('Postfix notation: ' + infix_to_postfix(expression))




