

from typing import List

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


if is_torch_available():
				import torch

class 		UpperCAmelCase__      (      __UpperCamelCase       ):




			'''simple docstring'''






			UpperCamelCase      						=	["""image_processor""", """tokenizer"""]
			UpperCamelCase      						=	"""OneFormerImageProcessor"""
			UpperCamelCase      						=	("""CLIPTokenizer""", """CLIPTokenizerFast""")




			def __init__(       self							:			Dict	,	a_							:			Tuple=None	,	a_							:			Optional[int]=None	,	a_							:			int = 77	,	a_							:			int = 77	,	**a_							:			Tuple			):
					'''simple docstring'''





					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`.'''			)

					__UpperCAmelCase   :					Optional[Any]													=     max_seq_length
					__UpperCAmelCase   :					Optional[int]													=     task_seq_length

					super().__init__(a_	,	a_			)




			def 			snake_case__					(       self							:			str	,	a_							:			str=None	,	a_							:			Tuple=77			):
					'''simple docstring'''





					if text_list is None:
							raise ValueError('''tokens cannot be None.'''			)

					__UpperCAmelCase   :					Any													=     self.tokenizer(a_	,	padding='''max_length'''	,	max_length=a_	,	truncation=a_			)

					__UpperCAmelCase   :					Dict													=     tokens['''attention_mask'''], tokens['''input_ids''']

					__UpperCAmelCase   :					Optional[Any]													=     []
					for attn_mask, input_id in zip(a_	,	a_			):
							__UpperCAmelCase   :					Any													=     torch.tensor(a_			) * torch.tensor(a_			)
							token_inputs.append(token.unsqueeze(0			)			)

					__UpperCAmelCase   :					int													=     torch.cat(a_	,	dim=0			)
					return token_inputs




			def __call__(       self							:			Dict	,	a_							:			str=None	,	a_							:			Any=None	,	a_							:			Union[str, Any]=None	,	**a_							:			Union[str, Any]			):
					'''simple docstring'''





					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.'''			)

					__UpperCAmelCase   :					List[str]													=     self.image_processor(a_	,	a_	,	a_	,	**a_			)

					if isinstance(a_	,	a_			):
							__UpperCAmelCase   :					Optional[int]													=     [task_inputs]

					if isinstance(a_	,	a_			) and all(isinstance(a_	,	a_			) for task_input in task_inputs			):
							__UpperCAmelCase   :					Dict													=     []
							for task in task_inputs:
									__UpperCAmelCase   :					Optional[int]													=     F'the task is {task}'
									task_token_inputs.append(a_			)
							__UpperCAmelCase   :					int													=     self._preprocess_text(a_	,	max_length=self.task_seq_length			)
					else:
							raise TypeError('''Task Inputs should be a string or a list of strings.'''			)

					if hasattr(a_	,	'''text_inputs'''			):
							__UpperCAmelCase   :					Optional[int]													=     encoded_inputs.text_inputs

							__UpperCAmelCase   :					Optional[int]													=     []
							for texts in texts_list:
									__UpperCAmelCase   :					str													=     self._preprocess_text(a_	,	max_length=self.max_seq_length			)
									text_inputs.append(text_input_list.unsqueeze(0			)			)

							__UpperCAmelCase   :					Optional[int]													=     torch.cat(a_	,	dim=0			)

					return encoded_inputs




			def 			snake_case__					(       self							:			Tuple	,	a_							:			List[str]=None	,	a_							:			Optional[int]=None	,	a_							:			int=None	,	**a_							:			str			):
					'''simple docstring'''





					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.'''			)

					__UpperCAmelCase   :					Union[str, Any]													=     self.image_processor.encode_inputs(a_	,	a_	,	a_	,	**a_			)

					if isinstance(a_	,	a_			):
							__UpperCAmelCase   :					List[Any]													=     [task_inputs]

					if isinstance(a_	,	a_			) and all(isinstance(a_	,	a_			) for task_input in task_inputs			):
							__UpperCAmelCase   :					Optional[int]													=     []
							for task in task_inputs:
									__UpperCAmelCase   :					str													=     F'the task is {task}'
									task_token_inputs.append(a_			)
							__UpperCAmelCase   :					Tuple													=     self._preprocess_text(a_	,	max_length=self.task_seq_length			)
					else:
							raise TypeError('''Task Inputs should be a string or a list of strings.'''			)

					if hasattr(a_	,	'''text_inputs'''			):
							__UpperCAmelCase   :					int													=     encoded_inputs.text_inputs

							__UpperCAmelCase   :					Union[str, Any]													=     []
							for texts in texts_list:
									__UpperCAmelCase   :					str													=     self._preprocess_text(a_	,	max_length=self.max_seq_length			)
									text_inputs.append(text_input_list.unsqueeze(0			)			)

							__UpperCAmelCase   :					int													=     torch.cat(a_	,	dim=0			)

					return encoded_inputs




			def 			snake_case__					(       self							:			Any	,	*a_							:			Optional[Any]	,	**a_							:			Tuple			):
					'''simple docstring'''





					return self.image_processor.post_process_semantic_segmentation(*a_	,	**a_			)




			def 			snake_case__					(       self							:			Tuple	,	*a_							:			List[str]	,	**a_							:			str			):
					'''simple docstring'''





					return self.image_processor.post_process_instance_segmentation(*a_	,	**a_			)




			def 			snake_case__					(       self							:			List[str]	,	*a_							:			Optional[int]	,	**a_							:			int			):
					'''simple docstring'''





					return self.image_processor.post_process_panoptic_segmentation(*a_	,	**a_			)










from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

__A             =TypeVar("T", bound=bool)

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			List[Any]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					T													=     value
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None
					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None




			@property
			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					return self._value




			@staticmethod
			def 			snake_case__					(       a_							:			RandomizedHeapNode[T] | None	,	a_							:			RandomizedHeapNode[T] | None			):
					'''simple docstring'''





					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__UpperCAmelCase							, __UpperCAmelCase   :					int													=     roota, roota

					if random.choice([True, False]			):
							__UpperCAmelCase							, __UpperCAmelCase   :					Union[str, Any]													=     roota.right, roota.left

					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(roota.left	,	a_			)

					return roota

class 		UpperCAmelCase__      (      Generic[T]       ):




			'''simple docstring'''






			def __init__(       self							:			Tuple	,	a_							:			Iterable[T] | None = ()			):
					'''simple docstring'''





					__UpperCAmelCase   :					RandomizedHeapNode[T] | None													=     None

					if data:
							for item in data:
									self.insert(a_			)




			def 			snake_case__					(       self							:			List[str]	,	a_							:			T			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     RandomizedHeapNode.merge(self._root	,	RandomizedHeapNode(a_			)			)




			def 			snake_case__					(       self							:			Optional[int]			):
					'''simple docstring'''





					__UpperCAmelCase   :					Optional[Any]													=     self.top()

					if self._root is None:
							return None

					__UpperCAmelCase   :					Tuple													=     RandomizedHeapNode.merge(self._root.left	,	self._root.right			)

					return result




			def 			snake_case__					(       self							:			Any			):
					'''simple docstring'''





					if not self._root:
							raise IndexError('''Can\'t get top element for the empty heap.'''			)
					return self._root.value




			def 			snake_case__					(       self							:			Optional[Any]			):
					'''simple docstring'''





					__UpperCAmelCase   :					str													=     None




			def 			snake_case__					(       self							:			Tuple			):
					'''simple docstring'''





					__UpperCAmelCase   :					List[Any]													=     []
					while self:
							result.append(self.pop()			)

					return result




			def __bool__(       self							:			List[Any]			):
					'''simple docstring'''





					return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()





