
"""simple docstring"""
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
			import torch
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							_snake_case		     =						['''image_processor''', '''tokenizer''']
							_snake_case		     =						'''OneFormerImageProcessor'''
							_snake_case		     =						('''CLIPTokenizer''', '''CLIPTokenizerFast''')




							def __init__(				self  ,							snake_case_=None  ,							snake_case_=None  ,							snake_case_ = 77  ,							snake_case_ = 77  ,							**snake_case_							)   ->       Dict:
													if image_processor is None:
																			raise ValueError("""You need to specify an `image_processor`."""							)
													if tokenizer is None:
																			raise ValueError("""You need to specify a `tokenizer`."""							)

													__lowerCAmelCase        =       max_seq_length
													__lowerCAmelCase        =       task_seq_length

													super().__init__(snake_case_  ,							snake_case_							)




							def  A__		(				self  ,							snake_case_=None  ,							snake_case_=77							)   ->       Tuple:
													if text_list is None:
																			raise ValueError("""tokens cannot be None."""							)

													__lowerCAmelCase        =       self.tokenizer(snake_case_  ,							padding="""max_length"""  ,							max_length=snake_case_  ,							truncation=snake_case_							)

													__lowerCAmelCase ,      __lowerCAmelCase        =       tokens["""attention_mask"""], tokens["""input_ids"""]

													__lowerCAmelCase        =       []
													for attn_mask, input_id in zip(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       torch.tensor(snake_case_							) * torch.tensor(snake_case_							)
																			token_inputs.append(token.unsqueeze(0							)							)

													__lowerCAmelCase        =       torch.cat(snake_case_  ,							dim=0							)
													return token_inputs




							def __call__(				self  ,							snake_case_=None  ,							snake_case_=None  ,							snake_case_=None  ,							**snake_case_							)   ->       str:

													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""							)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""							)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs							):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""							)

													__lowerCAmelCase        =       self.image_processor(snake_case_  ,							snake_case_  ,							snake_case_  ,							**snake_case_							)

													if isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       [task_inputs]

													if isinstance(snake_case_  ,							snake_case_							) and all(isinstance(snake_case_  ,							snake_case_							) for task_input in task_inputs							):
																			__lowerCAmelCase        =       []
																			for task in task_inputs:
																									__lowerCAmelCase        =       f"""the task is {task}"""
																									task_token_inputs.append(snake_case_							)
																			__lowerCAmelCase        =       self._preprocess_text(snake_case_  ,							max_length=self.task_seq_length							)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""							)

													if hasattr(snake_case_  ,							"""text_inputs"""							):
																			__lowerCAmelCase        =       encoded_inputs.text_inputs

																			__lowerCAmelCase        =       []
																			for texts in texts_list:
																									__lowerCAmelCase        =       self._preprocess_text(snake_case_  ,							max_length=self.max_seq_length							)
																									text_inputs.append(text_input_list.unsqueeze(0							)							)

																			__lowerCAmelCase        =       torch.cat(snake_case_  ,							dim=0							)

													return encoded_inputs




							def  A__		(				self  ,							snake_case_=None  ,							snake_case_=None  ,							snake_case_=None  ,							**snake_case_							)   ->       Dict:

													if task_inputs is None:
																			raise ValueError("""You have to specify the task_input. Found None."""							)
													elif images is None:
																			raise ValueError("""You have to specify the image. Found None."""							)

													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs							):
																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""							)

													__lowerCAmelCase        =       self.image_processor.encode_inputs(snake_case_  ,							snake_case_  ,							snake_case_  ,							**snake_case_							)

													if isinstance(snake_case_  ,							snake_case_							):
																			__lowerCAmelCase        =       [task_inputs]

													if isinstance(snake_case_  ,							snake_case_							) and all(isinstance(snake_case_  ,							snake_case_							) for task_input in task_inputs							):
																			__lowerCAmelCase        =       []
																			for task in task_inputs:
																									__lowerCAmelCase        =       f"""the task is {task}"""
																									task_token_inputs.append(snake_case_							)
																			__lowerCAmelCase        =       self._preprocess_text(snake_case_  ,							max_length=self.task_seq_length							)
													else:
																			raise TypeError("""Task Inputs should be a string or a list of strings."""							)

													if hasattr(snake_case_  ,							"""text_inputs"""							):
																			__lowerCAmelCase        =       encoded_inputs.text_inputs

																			__lowerCAmelCase        =       []
																			for texts in texts_list:
																									__lowerCAmelCase        =       self._preprocess_text(snake_case_  ,							max_length=self.max_seq_length							)
																									text_inputs.append(text_input_list.unsqueeze(0							)							)

																			__lowerCAmelCase        =       torch.cat(snake_case_  ,							dim=0							)

													return encoded_inputs




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Dict:
													return self.image_processor.post_process_semantic_segmentation(*snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       Any:
													return self.image_processor.post_process_instance_segmentation(*snake_case_  ,							**snake_case_							)




							def  A__		(				self  ,							*snake_case_  ,							**snake_case_							)   ->       List[str]:
													return self.image_processor.post_process_panoptic_segmentation(*snake_case_  ,							**snake_case_							)


"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
