



from typing import List

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


if is_torch_available():
		import torch


class 	lowerCamelCase__ (		lowerCAmelCase__   ):
						_lowerCAmelCase								= ['''image_processor''', '''tokenizer''']
						_lowerCAmelCase								= '''OneFormerImageProcessor'''
						_lowerCAmelCase								= ('''CLIPTokenizer''', '''CLIPTokenizerFast''')


						def __init__( self						:  Tuple      ,       _a						:  List[Any]=None      ,       _a						:  List[Any]=None      ,       _a						:  Any = 7_7      ,       _a						:  str = 7_7      ,       **_a						:  List[str]			):

													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__:       List[Any]											=max_seq_length
													a__:       Tuple											=task_seq_length

													super().__init__(_lowerCamelCase      ,       _lowerCamelCase			)


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  Union[str, Any]=None      ,       _a						:  List[Any]=7_7			):

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

													a__:       Any											=self.tokenizer(_lowerCamelCase      ,       padding="max_length"      ,       max_length=_lowerCamelCase      ,       truncation=_lowerCamelCase			)

													a__:       List[str]											=tokens["""attention_mask"""], tokens["""input_ids"""]

													a__:       str											=[]
													for attn_mask, input_id in zip(_lowerCamelCase      ,       _lowerCamelCase			):
																				a__:       List[str]											=torch.tensor(_lowerCamelCase			) * torch.tensor(_lowerCamelCase			)
																				token_inputs.append(token.unsqueeze(0			)			)

													a__:       Tuple											=torch.cat(_lowerCamelCase      ,       dim=0			)
													return token_inputs


						def __call__( self						:  List[Any]      ,       _a						:  Any=None      ,       _a						:  Optional[int]=None      ,       _a						:  List[str]=None      ,       **_a						:  List[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."			)

													a__:       Dict											=self.image_processor(_lowerCamelCase      ,       _lowerCamelCase      ,       _lowerCamelCase      ,       **_lowerCamelCase			)

													if isinstance(_lowerCamelCase      ,       _lowerCamelCase			):
																				a__:       str											=[task_inputs]

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

													if hasattr(_lowerCamelCase      ,       "text_inputs"			):
																				a__:       str											=encoded_inputs.text_inputs

																				a__:       Optional[Any]											=[]
																				for texts in texts_list:
																											a__:       int											=self._preprocess_text(_lowerCamelCase      ,       max_length=self.max_seq_length			)
																											text_inputs.append(text_input_list.unsqueeze(0			)			)

																				a__:       List[str]											=torch.cat(_lowerCamelCase      ,       dim=0			)

													return encoded_inputs


						def   _lowerCamelCase			( self						:  List[Any]      ,       _a						:  List[Any]=None      ,       _a						:  List[Any]=None      ,       _a						:  List[Any]=None      ,       **_a						:  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."			)

													a__:       str											=self.image_processor.encode_inputs(_lowerCamelCase      ,       _lowerCamelCase      ,       _lowerCamelCase      ,       **_lowerCamelCase			)

													if isinstance(_lowerCamelCase      ,       _lowerCamelCase			):
																				a__:       Tuple											=[task_inputs]

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

													if hasattr(_lowerCamelCase      ,       "text_inputs"			):
																				a__:       Tuple											=encoded_inputs.text_inputs

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

																				a__:       Union[str, Any]											=torch.cat(_lowerCamelCase      ,       dim=0			)

													return encoded_inputs


						def   _lowerCamelCase			( self						:  Dict      ,       *_a						:  Dict      ,       **_a						:  str			):
													return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase      ,       **_lowerCamelCase			)


						def   _lowerCamelCase			( self						:  Union[str, Any]      ,       *_a						:  str      ,       **_a						:  int			):
													return self.image_processor.post_process_instance_segmentation(*_lowerCamelCase      ,       **_lowerCamelCase			)







						def   _lowerCamelCase			( self						:  int      ,       *_a						:  Optional[int]      ,       **_a						:  Any			):
													return self.image_processor.post_process_panoptic_segmentation(*_lowerCamelCase      ,       **_lowerCamelCase			)











def 						__lowerCamelCase       (      __magic_name__	:  list     , __magic_name__	:  int						):
							for index, item in enumerate(__magic_name__						):
														if item == target:
																					return index
							return -1





def 						__lowerCamelCase       (      __magic_name__	:  list     , __magic_name__	:  int     , __magic_name__	:  int     , __magic_name__	:  int						):
							if not (0 <= high < len(__magic_name__						) and 0 <= low < len(__magic_name__						)):
														raise Exception("Invalid upper or lower bound!"						)
							if high < low:
														return -1
							if sequence[low] == target:
														return low
							if sequence[high] == target:
														return high
							return rec_linear_search(__magic_name__     , low + 1     , high - 1     , __magic_name__						)


if __name__ == "__main__":
		__UpperCAmelCase						  =     input('''Enter numbers separated by comma:\n''').strip()
		__UpperCAmelCase						  =     [int(item.strip()) for item in user_input.split(''',''')]

		__UpperCAmelCase						  =     int(input('''Enter a single number to be found in the list:\n''').strip())
		__UpperCAmelCase						  =     linear_search(sequence, target)
		if result != -1:
				print(f"""linear_search({sequence}, {target}) = {result}""")
		else:
				print(f"""{target} was not found in {sequence}""")





