
"""simple docstring"""




from typing import List

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


if is_torch_available():
    import torch







class 	_lowercase    (							snake_case__					):


     '''simple docstring'''





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



     def __init__(   self			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase = 77			,      __UpperCamelCase = 77			,      **__UpperCamelCase	)->       Optional[Any]:



          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__				:			Dict							=    max_seq_length
          UpperCAmelCase__				:			List[Any]							=    task_seq_length

          super().__init__(UpperCAmelCase_			,      UpperCAmelCase_	)



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase=None			,      __UpperCamelCase=77	)->       List[str]:



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

          UpperCAmelCase__				:			Dict							=    self.tokenizer(UpperCAmelCase_			,      padding="max_length"			,      max_length=UpperCAmelCase_			,      truncation=UpperCAmelCase_	)

          UpperCAmelCase__				,						UpperCAmelCase__				:			Union[str, Any]							=    tokens["attention_mask"], tokens["input_ids"]

          UpperCAmelCase__				:			Optional[int]							=    []
          for attn_mask, input_id in zip(UpperCAmelCase_			,      UpperCAmelCase_	):
               UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(UpperCAmelCase_	) * torch.tensor(UpperCAmelCase_	)
               token_inputs.append(token.unsqueeze(0	)	)

          UpperCAmelCase__				:			Dict							=    torch.cat(UpperCAmelCase_			,      dim=0	)
          return token_inputs



     def __call__(   self			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      **__UpperCamelCase	)->       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."	)

          UpperCAmelCase__				:			Dict							=    self.image_processor(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_			,      **UpperCAmelCase_	)

          if isinstance(UpperCAmelCase_			,      UpperCAmelCase_	):
               UpperCAmelCase__				:			List[Any]							=    [task_inputs]

          if isinstance(UpperCAmelCase_			,      UpperCAmelCase_	) and all(isinstance(UpperCAmelCase_			,      UpperCAmelCase_	) for task_input in task_inputs	):
               UpperCAmelCase__				:			List[Any]							=    []
               for task in task_inputs:
                    UpperCAmelCase__				:			List[str]							=    F"the task is {task}"
                    task_token_inputs.append(UpperCAmelCase_	)
               UpperCAmelCase__				:			Dict							=    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"	):
               UpperCAmelCase__				:			Optional[Any]							=    encoded_inputs.text_inputs

               UpperCAmelCase__				:			Optional[Any]							=    []
               for texts in texts_list:
                    UpperCAmelCase__				:			List[Any]							=    self._preprocess_text(UpperCAmelCase_			,      max_length=self.max_seq_length	)
                    text_inputs.append(text_input_list.unsqueeze(0	)	)

               UpperCAmelCase__				:			Tuple							=    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."	)

          UpperCAmelCase__				:			Any							=    self.image_processor.encode_inputs(UpperCAmelCase_			,      UpperCAmelCase_			,      UpperCAmelCase_			,      **UpperCAmelCase_	)

          if isinstance(UpperCAmelCase_			,      UpperCAmelCase_	):
               UpperCAmelCase__				:			List[str]							=    [task_inputs]

          if isinstance(UpperCAmelCase_			,      UpperCAmelCase_	) and all(isinstance(UpperCAmelCase_			,      UpperCAmelCase_	) for task_input in task_inputs	):
               UpperCAmelCase__				:			int							=    []
               for task in task_inputs:
                    UpperCAmelCase__				:			List[str]							=    F"the task is {task}"
                    task_token_inputs.append(UpperCAmelCase_	)
               UpperCAmelCase__				:			List[Any]							=    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"	):
               UpperCAmelCase__				:			List[Any]							=    encoded_inputs.text_inputs

               UpperCAmelCase__				:			str							=    []
               for texts in texts_list:
                    UpperCAmelCase__				:			List[str]							=    self._preprocess_text(UpperCAmelCase_			,      max_length=self.max_seq_length	)
                    text_inputs.append(text_input_list.unsqueeze(0	)	)

               UpperCAmelCase__				:			Dict							=    torch.cat(UpperCAmelCase_			,      dim=0	)

          return encoded_inputs



     def 			lowerCAmelCase__							(   self			,      *__UpperCamelCase			,      **__UpperCamelCase	)->       Dict:
          return self.image_processor.post_process_semantic_segmentation(*UpperCAmelCase_			,      **UpperCAmelCase_	)



     def 			lowerCAmelCase__							(   self			,      *__UpperCamelCase			,      **__UpperCamelCase	)->       Union[str, Any]:
          return self.image_processor.post_process_instance_segmentation(*UpperCAmelCase_			,      **UpperCAmelCase_	)



     def 			lowerCAmelCase__							(   self			,      *__UpperCamelCase			,      **__UpperCamelCase	)->       Union[str, Any]:
          return self.image_processor.post_process_panoptic_segmentation(*UpperCAmelCase_			,      **UpperCAmelCase_	)







"""simple docstring"""
import doctest

import projectq
from projectq.ops import H, Measure







def     _a					(   _SCREAMING_SNAKE_CASE					)     ->					int:
							snake_case_     							=		quantum_engine.allocate_qubit()
							H | qubit
							Measure | qubit
							return int(_SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
				doctest.testmod()

				# initialises a new quantum backend
				__SCREAMING_SNAKE_CASE				:     Dict		=     projectq.MainEngine()

				# Generate a list of 10 random numbers
				__SCREAMING_SNAKE_CASE				:     Optional[Any]		=     [get_random_number(quantum_engine) for _ in range(10)]

				# Flushes the quantum engine from memory
				quantum_engine.flush()

				print('Random numbers', random_numbers_list)



