
"""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__					(					__magic_name__				):





			'''simple docstring'''
			lowerCamelCase  					=		['''image_processor''', '''tokenizer''']
			lowerCamelCase  					=		'''OneFormerImageProcessor'''
			lowerCamelCase  					=		('''CLIPTokenizer''', '''CLIPTokenizerFast''')



			def __init__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase = 77     ,     __UpperCAmelCase = 77     ,     **__UpperCAmelCase  ) ->     Tuple:
				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__(__UpperCAmelCase     ,     __UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=77  ) ->     Optional[Any]:
				if text_list is None:
					raise ValueError("""tokens cannot be None."""  )

				_lowerCAmelCase								=self.tokenizer(__UpperCAmelCase     ,     padding="""max_length"""     ,     max_length=__UpperCAmelCase     ,     truncation=__UpperCAmelCase  )

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

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

				_lowerCAmelCase								=torch.cat(__UpperCAmelCase     ,     dim=0  )
				return token_inputs



			def __call__(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     **__UpperCAmelCase  ) ->     int:

				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(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )

				if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
					_lowerCAmelCase								=[task_inputs]

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

				if hasattr(__UpperCAmelCase     ,     """text_inputs"""  ):
					_lowerCAmelCase								=encoded_inputs.text_inputs

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

					_lowerCAmelCase								=torch.cat(__UpperCAmelCase     ,     dim=0  )

				return encoded_inputs



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=None     ,     **__UpperCAmelCase  ) ->     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."""  )

				_lowerCAmelCase								=self.image_processor.encode_inputs(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     **__UpperCAmelCase  )

				if isinstance(__UpperCAmelCase     ,     __UpperCAmelCase  ):
					_lowerCAmelCase								=[task_inputs]

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

				if hasattr(__UpperCAmelCase     ,     """text_inputs"""  ):
					_lowerCAmelCase								=encoded_inputs.text_inputs

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

					_lowerCAmelCase								=torch.cat(__UpperCAmelCase     ,     dim=0  )

				return encoded_inputs



			def 					_lowerCAmelCase		(							self     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     Any:
				return self.image_processor.post_process_semantic_segmentation(*__UpperCAmelCase     ,     **__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     Optional[Any]:
				return self.image_processor.post_process_instance_segmentation(*__UpperCAmelCase     ,     **__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     Optional[int]:
				return self.image_processor.post_process_panoptic_segmentation(*__UpperCAmelCase     ,     **__UpperCAmelCase  )





"""simple docstring"""


from .hash_table import HashTable







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



			def __init__(							self     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     List[str]:
				super().__init__(*__UpperCAmelCase     ,     **__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=None  ) ->     Union[str, Any]:
				_lowerCAmelCase								=1
				_lowerCAmelCase								=self.hash_function(key + i * i  )

				while self.values[new_key] is not None and self.values[new_key] != key:
					i += 1
					_lowerCAmelCase								=(
					    self.hash_function(key + i * i  )
					    if not self.balanced_factor() >= self.lim_charge
					    else None
					)

					if new_key is None:
						break

				return new_key



