






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[Any]								=					logging.get_logger(__name__)


UpperCamelCase__						:      Union[str, Any]								=					Dict[str, Any]
UpperCamelCase__						:      Dict								=					List[Prediction]







@add_end_docstrings(a_     )
class 				lowerCamelCase_     (	a_     ):
							def __init__(						self		:					Tuple							,*__lowerCamelCase		:					Union[str, Any]							,**__lowerCamelCase		:					str					):





								'''simple docstring'''



								super().__init__(*__lowerCamelCase							,**__lowerCamelCase					)

								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  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,**__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			{}
								a		=			{}
								if "subtask" in kwargs:
									a		=			kwargs['''subtask''']
									a		=			kwargs['''subtask''']
								if "threshold" in kwargs:
									a		=			kwargs['''threshold''']
								if "mask_threshold" in kwargs:
									a		=			kwargs['''mask_threshold''']
								if "overlap_mask_area_threshold" in kwargs:
									a		=			kwargs['''overlap_mask_area_threshold''']

								return preprocess_kwargs, {}, postprocess_kwargs
							def __call__(						self		:					Dict							,__lowerCamelCase		:					Any							,**__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								return super().__call__(__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					Tuple							,__lowerCamelCase		:					Any=None					):





								'''simple docstring'''



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





								'''simple docstring'''



								a		=			model_inputs.pop('''target_size'''					)
								a		=			self.model(**__lowerCamelCase					)
								a		=			target_size
								return model_outputs




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Optional[Any]=None							,__lowerCamelCase		:					Optional[int]=0.9							,__lowerCamelCase		:					int=0.5							,__lowerCamelCase		:					str=0.5					):





								'''simple docstring'''



								a		=			None
								if subtask in {"panoptic", None} and hasattr(self.image_processor							,'''post_process_panoptic_segmentation'''					):
									a		=			self.image_processor.post_process_panoptic_segmentation
								elif subtask in {"instance", None} and hasattr(self.image_processor							,'''post_process_instance_segmentation'''					):
									a		=			self.image_processor.post_process_instance_segmentation

								if fn is not None:
									a		=			fn(
									    __lowerCamelCase							,threshold=__lowerCamelCase							,mask_threshold=__lowerCamelCase							,overlap_mask_area_threshold=__lowerCamelCase							,target_sizes=model_outputs['''target_size''']							,)[0]

									a		=			[]
									a		=			outputs['''segmentation''']

									for segment in outputs["segments_info"]:
										a		=			(segmentation == segment['''id''']) * 2_55
										a		=			Image.fromarray(mask.numpy().astype(np.uinta					)							,mode='''L'''					)
										a		=			self.model.config.idalabel[segment['''label_id''']]
										a		=			segment['''score''']
										annotation.append({'''score''': score, '''label''': label, '''mask''': mask}					)

								elif subtask in {"semantic", None} and hasattr(self.image_processor							,'''post_process_semantic_segmentation'''					):
									a		=			self.image_processor.post_process_semantic_segmentation(
									    __lowerCamelCase							,target_sizes=model_outputs['''target_size''']					)[0]

									a		=			[]
									a		=			outputs.numpy()
									a		=			np.unique(__lowerCamelCase					)

									for label in labels:
										a		=			(segmentation == label) * 2_55
										a		=			Image.fromarray(mask.astype(np.uinta					)							,mode='''L'''					)
										a		=			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













import random
import sys

UpperCamelCase__						:      List[str]								=					"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""





def   SCREAMING_SNAKE_CASE__						(   )   ->  None:


	"""simple docstring"""



	a		=			input('''Enter message: '''  )
	a		=			'''LFWOAYUISVKMNXPBDCRJTQEGHZ'''
	a		=			input('''Encrypt/Decrypt [e/d]: '''  )

	check_valid_key(snake_case_  )

	if resp.lower().startswith('''e'''  ):
		a		=			'''encrypt'''
		a		=			encrypt_message(snake_case_,  snake_case_  )
	elif resp.lower().startswith('''d'''  ):
		a		=			'''decrypt'''
		a		=			decrypt_message(snake_case_,  snake_case_  )

	print(f"""\n{mode.title()}ion: \n{translated}"""  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			list(snake_case_  )
	a		=			list(snake_case_  )
	key_list.sort()
	letters_list.sort()

	if key_list != letters_list:
		sys.exit('''Error in the key or symbol set.'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''encrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	return translate_message(snake_case_,  snake_case_,  '''decrypt'''  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_  )   ->  str:


	"""simple docstring"""



	a		=			''''''
	a		=			LETTERS
	a		=			key

	if mode == "decrypt":
		a	, a		=			chars_b, chars_a

	for symbol in message:
		if symbol.upper() in chars_a:
			a		=			chars_a.find(symbol.upper()  )
			if symbol.isupper():
				translated += chars_b[sym_index].upper()
			else:
				translated += chars_b[sym_index].lower()
		else:
			translated += symbol

	return translated





def   SCREAMING_SNAKE_CASE__						(   )   ->  str:


	"""simple docstring"""



	a		=			list(snake_case_  )
	random.shuffle(snake_case_  )
	return "".join(snake_case_  )


if __name__ == "__main__":
				main()





