

"""simple docstring"""




from typing import List

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


if is_torch_available():
	import torch





class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					["image_processor", "tokenizer"]
		SCREAMING_SNAKE_CASE              =					"OneFormerImageProcessor"
		SCREAMING_SNAKE_CASE              =					("CLIPTokenizer", "CLIPTokenizerFast")


		def __init__(self				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	int = 7_7  ,  __SCREAMING_SNAKE_CASE				:	int = 7_7  ,  **__SCREAMING_SNAKE_CASE				:	int):
							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`.")

							A    =      max_seq_length
							A    =      task_seq_length

							super().__init__(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	str=None  ,  __SCREAMING_SNAKE_CASE				:	Any=7_7):
							if text_list is None:
												raise ValueError("tokens cannot be None.")

							A    =      self.tokenizer(__SCREAMING_SNAKE_CASE  ,  padding="max_length"  ,  max_length=__SCREAMING_SNAKE_CASE  ,  truncation=__SCREAMING_SNAKE_CASE)

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

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

							A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)
							return token_inputs


		def __call__(self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=None  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	List[Any]):

							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.")

							A    =      self.image_processor(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)

							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												A    =      [task_inputs]

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

							if hasattr(__SCREAMING_SNAKE_CASE  ,  "text_inputs"):
												A    =      encoded_inputs.text_inputs

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

												A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)

							return encoded_inputs


		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=None  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	Tuple):

							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.")

							A    =      self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)

							if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
												A    =      [task_inputs]

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

							if hasattr(__SCREAMING_SNAKE_CASE  ,  "text_inputs"):
												A    =      encoded_inputs.text_inputs

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

												A    =      torch.cat(__SCREAMING_SNAKE_CASE  ,  dim=0)

							return encoded_inputs


		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]  ,  *__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	List[Any]):
							return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  *__SCREAMING_SNAKE_CASE				:	Tuple  ,  **__SCREAMING_SNAKE_CASE				:	List[Any]):
							return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  *__SCREAMING_SNAKE_CASE				:	str  ,  **__SCREAMING_SNAKE_CASE				:	Dict):
							return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															res *= a

										a *= a
										b >>= 1

					return res
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															A    =      ((res % c) * (a % c)) % c

										a *= a
										b >>= 1

					return res





