
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,
			)


_lowerCamelCase :       Dict     					=    logging.get_logger(__name__)


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




@add_end_docstrings(UpperCAmelCase      )
class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	def __init__(						self			:					Tuple    ,						*lowercase			:					List[str]    ,						**lowercase			:					int			):


				'''simple docstring'''


				super().__init__(*lowercase    ,						**lowercase			)

				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 				A							(						self			:					Tuple    ,						**lowercase			:					Union[str, Any]			):


				'''simple docstring'''


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

				return preprocess_kwargs, {}, postprocess_kwargs



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Any    ,						**lowercase			:					int			):


				'''simple docstring'''


				return super().__call__(lowercase    ,						**lowercase			)



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					Optional[int]    ,						lowercase			:					List[Any]=None			):


				'''simple docstring'''


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



	def 				A							(						self			:					List[str]    ,						lowercase			:					Union[str, Any]			):


				'''simple docstring'''


				_snake_case						=    model_inputs.pop('target_size'			)
				_snake_case						=    self.model(**lowercase			)
				_snake_case						=    target_size
				return model_outputs





	def 				A							(						self			:					Any    ,						lowercase			:					Optional[int]    ,						lowercase			:					str=None    ,						lowercase			:					Optional[int]=0.9    ,						lowercase			:					Tuple=0.5    ,						lowercase			:					Union[str, Any]=0.5			):


				'''simple docstring'''


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

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

							_snake_case						=    []
							_snake_case						=    outputs['segmentation']

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

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

							_snake_case						=    []
							_snake_case						=    outputs.numpy()
							_snake_case						=    np.unique(lowercase			)

							for label in labels:
										_snake_case						=    (segmentation == label) * 255
										_snake_case						=    Image.fromarray(mask.astype(np.uinta			)    ,						mode='L'			)
										_snake_case						=    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

_lowerCamelCase :       Tuple     					=    '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def  a_	( )				->						None:
			_snake_case						=    input('Enter message: '				)
			_snake_case						=    'LFWOAYUISVKMNXPBDCRJTQEGHZ'
			_snake_case						=    input('Encrypt/Decrypt [e/d]: '				)

			check_valid_key(__lowercase				)

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

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






def  a_	( __lowercase     :			str				)				->						None:
			_snake_case						=    list(__lowercase				)
			_snake_case						=    list(__lowercase				)
			key_list.sort()
			letters_list.sort()

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






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'encrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			return translate_message(__lowercase ,      __lowercase ,      'decrypt'				)






def  a_	( __lowercase     :			str ,      __lowercase     :			str ,      __lowercase     :			str				)				->						str:
			_snake_case						=    ''
			_snake_case						=    LETTERS
			_snake_case						=    key

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

			for symbol in message:
						if symbol.upper() in chars_a:
									_snake_case						=    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  a_	( )				->						str:
			_snake_case						=    list(__lowercase				)
			random.shuffle(__lowercase				)
			return "".join(__lowercase				)


if __name__ == "__main__":
			main()