


'''simple docstring'''


from __future__ import annotations

import random
import string





class    lowercase__  :





							'''simple docstring'''


							def __init__(   self   , __snake_case = None  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				passcode or self.__passcode_creator()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.__make_key_list()
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.__make_shift_key()






							def __str__(   self  ):
											return "".join(self.__passcode  )






							def UpperCAmelCase_     (   self   , __snake_case  ):
											for i in range(1   , len(__snake_case  )   , 2  ):
															iterlist[i] *= -1
											return iterlist






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				string.ascii_letters + string.digits
											_SCREAMING_SNAKE_CASE			:	List[str]							=				[random.choice(__snake_case  ) for _ in range(random.randint(10   , 20  )  )]
											return password






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				(
											    string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
											)

											_SCREAMING_SNAKE_CASE			:	Dict							=				[]

											# creates points known as breakpoints to break the key_list_options at those
											# points and pivot each substring
											_SCREAMING_SNAKE_CASE			:	List[str]							=				sorted(set(self.__passcode  )  )
											_SCREAMING_SNAKE_CASE			:	list[str]							=				[]

											# algorithm for creating a new shuffled list, keys_l, out of key_list_options
											for i in key_list_options:
															temp_list.extend(__snake_case  )

															# checking breakpoints at which to pivot temporary sublist and add it into
															# keys_l
															if i in breakpoints or i == key_list_options[-1]:
																			keys_l.extend(temp_list[::-1]  )
																			temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
											return keys_l






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				sum(self.__neg_pos([ord(__snake_case  ) for x in self.__passcode]  )  )
											return num if num > 0 else len(self.__passcode  )






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				""""""

											# decoding shift like Caesar cipher algorithm implementing negative shift or
											# reverse shift or left shift
											for i in encoded_message:
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.__key_list.index(__snake_case  )
															decoded_message += self.__key_list[
															    (position - self.__shift_key) % -len(self.__key_list  )
															]

											return decoded_message






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				""""""

											# encoding shift like Caesar cipher algorithm implementing positive shift or
											# forward shift or right shift
											for i in plaintext:
															_SCREAMING_SNAKE_CASE			:	Dict							=				self.__key_list.index(__snake_case  )
															encoded_message += self.__key_list[
															    (position + self.__shift_key) % len(self.__key_list  )
															]

											return encoded_message







def       snake_case_       (  SCREAMING_SNAKE_CASE__ = "Hello, this is a modified Caesar cipher"					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[Any]							=				ShuffledShiftCipher()
				return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__					)					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()








'''simple docstring'''


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


UpperCAmelCase_		:       Optional[int]							       =		logging.get_logger(__name__)


UpperCAmelCase_		:       List[str]							       =		Dict[str, Any]
UpperCAmelCase_		:       List[str]							       =		List[Prediction]





@add_end_docstrings(_snake_case )
class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def __init__(   self   , *__snake_case   , **__snake_case  ):
											super().__init__(*__snake_case   , **__snake_case  )

											if self.framework == "tf":
															raise ValueError(f"""The {self.__class__} is only available in PyTorch."""  )

											requires_backends(self   , """vision"""  )
											self.check_model_type(
											    dict(
											        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{}
											_SCREAMING_SNAKE_CASE			:	Any							=				{}
											if "subtask" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Any							=				kwargs["""subtask"""]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				kwargs["""subtask"""]
											if "threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				kwargs["""threshold"""]
											if "mask_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Dict							=				kwargs["""mask_threshold"""]
											if "overlap_mask_area_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				kwargs["""overlap_mask_area_threshold"""]

											return preprocess_kwargs, {}, postprocess_kwargs






							def __call__(   self   , __snake_case   , **__snake_case  ):
											return super().__call__(__snake_case   , **__snake_case  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				[(image.height, image.width)]
											if self.model.config.__class__.__name__ == "OneFormerConfig":
															if subtask is None:
																			_SCREAMING_SNAKE_CASE			:	int							=				{}
															else:
																			_SCREAMING_SNAKE_CASE			:	str							=				{"""task_inputs""": [subtask]}
															_SCREAMING_SNAKE_CASE			:	Any							=				self.image_processor(images=[image]   , return_tensors="""pt"""   , **__snake_case  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.tokenizer(
															    inputs["""task_inputs"""]   , padding="""max_length"""   , max_length=self.model.config.task_seq_len   , return_tensors=self.framework   , )["""input_ids"""]
											else:
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor(images=[image]   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				target_size
											return inputs






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				model_inputs.pop("""target_size"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				self.model(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				target_size
											return model_outputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None   , __snake_case=0.9   , __snake_case=0.5   , __snake_case=0.5  ):
											_SCREAMING_SNAKE_CASE			:	str							=				None
											if subtask in {"panoptic", None} and hasattr(self.image_processor   , """post_process_panoptic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor   , """post_process_instance_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	str							=				self.image_processor.post_process_instance_segmentation

											if fn is not None:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				fn(
															    __snake_case   , threshold=__snake_case   , mask_threshold=__snake_case   , overlap_mask_area_threshold=__snake_case   , target_sizes=model_outputs["""target_size"""]   , )[0]

															_SCREAMING_SNAKE_CASE			:	Dict							=				[]
															_SCREAMING_SNAKE_CASE			:	int							=				outputs["""segmentation"""]

															for segment in outputs["segments_info"]:
																			_SCREAMING_SNAKE_CASE			:	str							=				(segmentation == segment["""id"""]) * 255
																			_SCREAMING_SNAKE_CASE			:	int							=				Image.fromarray(mask.numpy().astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model.config.idalabel[segment["""label_id"""]]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				segment["""score"""]
																			annotation.append({"""score""": score, """label""": label, """mask""": mask}  )

											elif subtask in {"semantic", None} and hasattr(self.image_processor   , """post_process_semantic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.image_processor.post_process_semantic_segmentation(
															    __snake_case   , target_sizes=model_outputs["""target_size"""]  )[0]

															_SCREAMING_SNAKE_CASE			:	str							=				[]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				outputs.numpy()
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.unique(__snake_case  )

															for label in labels:
																			_SCREAMING_SNAKE_CASE			:	Any							=				(segmentation == label) * 255
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				Image.fromarray(mask.astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				self.model.config.idalabel[label]
																			annotation.append({"""score""": None, """label""": label, """mask""": mask}  )
											else:
															raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model  )}"""  )
											return annotation




