




from typing import List

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


if is_torch_available():
				import torch






class 					__lowerCAmelCase				( lowerCAmelCase__				):
			snake_case_   :				Tuple				  =  ["image_processor", "tokenizer"]
			snake_case_   :				Optional[Any]				  =  "OneFormerImageProcessor"
			snake_case_   :				int				  =  ("CLIPTokenizer", "CLIPTokenizerFast")







			def __init__( self					:    int					,					snake_case__					:    Any=None					,					snake_case__					:    int=None					,					snake_case__					:    Optional[int] = 77					,					snake_case__					:    List[str] = 77					,					**snake_case__					:    Optional[Any]   ):




									"""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									=  max_seq_length
									_UpperCAmelCase									=  task_seq_length

									super().__init__(_lowerCamelCase					,					_lowerCamelCase   )







			def UpperCamelCase    ( self					:    List[str]					,					snake_case__					:    Optional[int]=None					,					snake_case__					:    int=77   ):




									"""simple docstring"""


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

									_UpperCAmelCase									=  self.tokenizer(_lowerCamelCase					,					padding="max_length"					,					max_length=_lowerCamelCase					,					truncation=_lowerCamelCase   )

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

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

									_UpperCAmelCase									=  torch.cat(_lowerCamelCase					,					dim=0   )
									return token_inputs







			def __call__( self					:    Tuple					,					snake_case__					:    int=None					,					snake_case__					:    Union[str, Any]=None					,					snake_case__					:    List[str]=None					,					**snake_case__					:    Optional[int]   ):




									"""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									=  self.image_processor(_lowerCamelCase					,					_lowerCamelCase					,					_lowerCamelCase					,					**_lowerCamelCase   )

									if isinstance(_lowerCamelCase					,					_lowerCamelCase   ):
															_UpperCAmelCase									=  [task_inputs]

									if isinstance(_lowerCamelCase					,					_lowerCamelCase   ) and all(isinstance(_lowerCamelCase					,					_lowerCamelCase   ) for task_input in task_inputs   ):
															_UpperCAmelCase									=  []
															for task in task_inputs:
																					_UpperCAmelCase									=  F"""the task is {task}"""
																					task_token_inputs.append(_lowerCamelCase   )
															_UpperCAmelCase									=  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"   ):
															_UpperCAmelCase									=  encoded_inputs.text_inputs

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

															_UpperCAmelCase									=  torch.cat(_lowerCamelCase					,					dim=0   )

									return encoded_inputs







			def UpperCamelCase    ( self					:    Union[str, Any]					,					snake_case__					:    List[str]=None					,					snake_case__					:    List[Any]=None					,					snake_case__					:    str=None					,					**snake_case__					:    Optional[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									=  self.image_processor.encode_inputs(_lowerCamelCase					,					_lowerCamelCase					,					_lowerCamelCase					,					**_lowerCamelCase   )

									if isinstance(_lowerCamelCase					,					_lowerCamelCase   ):
															_UpperCAmelCase									=  [task_inputs]

									if isinstance(_lowerCamelCase					,					_lowerCamelCase   ) and all(isinstance(_lowerCamelCase					,					_lowerCamelCase   ) for task_input in task_inputs   ):
															_UpperCAmelCase									=  []
															for task in task_inputs:
																					_UpperCAmelCase									=  F"""the task is {task}"""
																					task_token_inputs.append(_lowerCamelCase   )
															_UpperCAmelCase									=  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"   ):
															_UpperCAmelCase									=  encoded_inputs.text_inputs

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

															_UpperCAmelCase									=  torch.cat(_lowerCamelCase					,					dim=0   )

									return encoded_inputs







			def UpperCamelCase    ( self					:    Any					,					*snake_case__					:    List[str]					,					**snake_case__					:    List[str]   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Optional[int]					,					*snake_case__					:    int					,					**snake_case__					:    Dict   ):




									"""simple docstring"""


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







			def UpperCamelCase    ( self					:    Any					,					*snake_case__					:    List[Any]					,					**snake_case__					:    List[Any]   ):




									"""simple docstring"""


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








from math import exp


def 							__SCREAMING_SNAKE_CASE						(    snake_case_		):

						'''simple docstring'''


						return 8 * x - 2 * exp(-x		)


def 							__SCREAMING_SNAKE_CASE						(    snake_case_			,					snake_case_			,					snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase									=  lower_bound
						_UpperCAmelCase									=  upper_bound
						for _ in range(0			,					snake_case_		):
												_UpperCAmelCase							,				_UpperCAmelCase									=  xa, xa - (f(snake_case_		) * (xa - xa)) / (f(snake_case_		) - f(snake_case_		))
						return xa


if __name__ == "__main__":
				print(f"""Example: {secant_method(1, 3, 2)}""")




