

"""simple docstring"""





from typing import List

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


if is_torch_available():
				import torch

class     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE_						     =						'OneFormerImageProcessor'
			SCREAMING_SNAKE_CASE_						     =						('CLIPTokenizer', 'CLIPTokenizerFast')






			def __init__(							self     :					Optional[int]						,  lowercase_     :					str=None						,  lowercase_     :					Optional[Any]=None						,  lowercase_     :					int = 77						,  lowercase_     :					int = 77						,  **lowercase_     :					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`.'''						)

								UpperCamelCase__		:    str         =max_seq_length
								UpperCamelCase__		:    Any         =task_seq_length

								super().__init__(lowercase_						,  lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  lowercase_     :					Optional[Any]=None						,  lowercase_     :					Union[str, Any]=77						):
								if text_list is None:
													raise ValueError('''tokens cannot be None.'''						)

								UpperCamelCase__		:    Union[str, Any]         =self.tokenizer(lowercase_						,  padding='''max_length'''						,  max_length=lowercase_						,  truncation=lowercase_						)

								UpperCamelCase__  ,			UpperCamelCase__		:    Optional[Any]         =tokens['''attention_mask'''], tokens['''input_ids''']

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

								UpperCamelCase__		:    Any         =torch.cat(lowercase_						,  dim=0						)
								return token_inputs






			def __call__(							self     :					Union[str, Any]						,  lowercase_     :					Optional[Any]=None						,  lowercase_     :					Dict=None						,  lowercase_     :					List[Any]=None						,  **lowercase_     :					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(lowercase_						,  lowercase_						,  lowercase_						,  **lowercase_						)

								if isinstance(lowercase_						,  lowercase_						):
													UpperCamelCase__		:    Union[str, Any]         =[task_inputs]

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

								if hasattr(lowercase_						,  '''text_inputs'''						):
													UpperCamelCase__		:    List[Any]         =encoded_inputs.text_inputs

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

													UpperCamelCase__		:    List[str]         =torch.cat(lowercase_						,  dim=0						)

								return encoded_inputs






			def    _lowerCAmelCase			(							self     :					Tuple						,  lowercase_     :					Dict=None						,  lowercase_     :					Dict=None						,  lowercase_     :					List[Any]=None						,  **lowercase_     :					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.'''						)

								UpperCamelCase__		:    Optional[Any]         =self.image_processor.encode_inputs(lowercase_						,  lowercase_						,  lowercase_						,  **lowercase_						)

								if isinstance(lowercase_						,  lowercase_						):
													UpperCamelCase__		:    Dict         =[task_inputs]

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

								if hasattr(lowercase_						,  '''text_inputs'''						):
													UpperCamelCase__		:    Tuple         =encoded_inputs.text_inputs

													UpperCamelCase__		:    Optional[int]         =[]
													for texts in texts_list:
																		UpperCamelCase__		:    Tuple         =self._preprocess_text(lowercase_						,  max_length=self.max_seq_length						)
																		text_inputs.append(text_input_list.unsqueeze(0						)						)

													UpperCamelCase__		:    str         =torch.cat(lowercase_						,  dim=0						)

								return encoded_inputs






			def    _lowerCAmelCase			(							self     :					List[Any]						,  *lowercase_     :					Dict						,  **lowercase_     :					List[Any]						):
								return self.image_processor.post_process_semantic_segmentation(*lowercase_						,  **lowercase_						)






			def    _lowerCAmelCase			(							self     :					List[str]						,  *lowercase_     :					Dict						,  **lowercase_     :					List[str]						):
								return self.image_processor.post_process_instance_segmentation(*lowercase_						,  **lowercase_						)






			def    _lowerCAmelCase			(							self     :					Dict						,  *lowercase_     :					Optional[int]						,  **lowercase_     :					Dict						):
								return self.image_processor.post_process_panoptic_segmentation(*lowercase_						,  **lowercase_						)





"""simple docstring"""





import math as m



def        _lowerCAmelCase      (						UpperCAmelCase     :							Optional[int]      ,							UpperCAmelCase     :							Optional[Any]      ,							UpperCAmelCase     :							Optional[Any]=0.001				):


					'''simple docstring'''






					return (f(a + h				) - f(a - h				)) / (2 * h)



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							Optional[Any]=0      ,							UpperCAmelCase     :							Optional[int]=100      ,							UpperCAmelCase     :							Union[str, Any]=0.0001      ,							UpperCAmelCase     :							Dict=1E-6      ,							UpperCAmelCase     :							Any=False				):


					'''simple docstring'''






					UpperCamelCase__		:    Optional[Any]         =xa  # set the initial guess
					UpperCamelCase__		:    List[Any]         =[a]
					UpperCamelCase__		:    Optional[Any]         =abs(f(UpperCAmelCase				)				)
					UpperCamelCase__		:    List[str]         =lambda UpperCAmelCase				: calc_derivative(UpperCAmelCase      ,							UpperCAmelCase      ,							h=UpperCAmelCase				)  # noqa: E731  Derivative of f(x)
					for _ in range(UpperCAmelCase				):
										if fa(UpperCAmelCase				) == 0:
															raise ValueError('''No converging solution found'''				)
										UpperCamelCase__		:    Any         =a - f(UpperCAmelCase				) / fa(UpperCAmelCase				)  # Calculate the next estimate
										if logsteps:
															steps.append(UpperCAmelCase				)
										if error < maxerror:
															break
					else:
										raise ValueError('''Iteration limit reached, no converging solution found'''				)
					if logsteps:
										# If logstep is true, then log intermediate steps
										return a, error, steps
					return a, error


if __name__ == "__main__":
				from matplotlib import pyplot as plt

				_SCREAMING_SNAKE_CASE       :  str        = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
				_SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       ,       _SCREAMING_SNAKE_CASE       :  Any        = newton_raphson(
				    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
				)
				plt.plot([abs(f(x)) for x in steps])
				plt.xlabel("""step""")
				plt.ylabel("""error""")
				plt.show()
				print(F'''solution = {{{solution:f}}}, error = {{{error:f}}}''')


