

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,
   )


__A    	=			logging.get_logger(__name__)


__A    	=			Dict[str, Any]
__A    	=			List[Prediction]



@add_end_docstrings(A_  )
class 						lowercase_					(						A_  ):



      def __init__(    self   :       Dict    ,      *A__   :       Any    ,      **A__   :       Optional[Any]	)					->  Any:
        super().__init__(*A__    ,      **A__	)

        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   :       Tuple    ,      **A__   :       str	)					->  int:
        _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   :       Optional[int]    ,      A__   :       Tuple    ,      **A__   :       Union[str, Any]	)					->  Optional[int]:
        return super().__call__(A__    ,      **A__	)



      def 		UpperCamelCase_	(    self   :       int    ,      A__   :       int    ,      A__   :       Tuple=None	)					->  Dict:
        _snake_case		=							load_image(A__	)
        _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'''    ,      **A__	)
          _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 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Dict	)					->  List[str]:
        _snake_case		=							model_inputs.pop('''target_size'''	)
        _snake_case		=							self.model(**A__	)
        _snake_case		=							target_size
        return model_outputs







      def 		UpperCamelCase_	(    self   :       List[Any]    ,      A__   :       List[str]    ,      A__   :       str=None    ,      A__   :       str=0.9    ,      A__   :       Dict=0.5    ,      A__   :       int=0.5	)					->  List[Any]:
        _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(
              A__    ,      threshold=A__    ,      mask_threshold=A__    ,      overlap_mask_area_threshold=A__    ,      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(
              A__    ,      target_sizes=model_outputs['''target_size''']	)[0]

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

          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

from . import cryptomath_module as cryptomath

__A    	=			(
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)



def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase  )							->			None:



  """simple docstring"""






  if mode == "encrypt":
    if key_a == 1:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''A is set to 1. Choose different key'''  )
    if key_b == 0:
      sys.exit(
          '''The affine cipher becomes weak when key '''
          '''B is set to 0. Choose different key'''  )
  if key_a < 0 or key_b < 0 or key_b > len(_UpperCamelCase  ) - 1:
    sys.exit(
        '''Key A must be greater than 0 and key B must '''
        F"""be between 0 and {len(_UpperCamelCase  ) - 1}."""  )
  if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) != 1:
    sys.exit(
        F"""Key A {key_a} and the symbol set size {len(_UpperCamelCase  )} """
        '''are not relatively prime. Choose a different key.'''  )



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''encrypt'''  )
  _snake_case		=							''''''
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(_UpperCamelCase  )]
    else:
      cipher_text += symbol
  return cipher_text



def    snake_case_(_UpperCamelCase				, _UpperCamelCase  )							->			str:



  """simple docstring"""






  _snake_case,  _snake_case		=							divmod(_UpperCamelCase				, len(_UpperCamelCase  )  )
  check_keys(_UpperCamelCase				, _UpperCamelCase				, '''decrypt'''  )
  _snake_case		=							''''''
  _snake_case		=							cryptomath.find_mod_inverse(_UpperCamelCase				, len(_UpperCamelCase  )  )
  for symbol in message:
    if symbol in SYMBOLS:
      _snake_case		=							SYMBOLS.find(_UpperCamelCase  )
      plain_text += SYMBOLS[
          (sym_index - key_b) * mod_inverse_of_key_a % len(_UpperCamelCase  )
      ]
    else:
      plain_text += symbol
  return plain_text



def    snake_case_()							->			int:



  """simple docstring"""






  while True:
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    _snake_case		=							random.randint(2				, len(_UpperCamelCase  )  )
    if cryptomath.gcd(_UpperCamelCase				, len(_UpperCamelCase  )  ) == 1 and key_b % len(_UpperCamelCase  ) != 0:
      return key_b * len(_UpperCamelCase  ) + key_b



def    snake_case_()							->			None:



  """simple docstring"""






  _snake_case		=							input('''Enter message: '''  ).strip()
  _snake_case		=							int(input('''Enter key [2000 - 9000]: '''  ).strip()  )
  _snake_case		=							input('''Encrypt/Decrypt [E/D]: '''  ).strip().lower()

  if mode.startswith('''e'''  ):
    _snake_case		=							'''encrypt'''
    _snake_case		=							encrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  elif mode.startswith('''d'''  ):
    _snake_case		=							'''decrypt'''
    _snake_case		=							decrypt_message(_UpperCamelCase				, _UpperCamelCase  )
  print(F"""\n{mode.title()}ed text: \n{translated}"""  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # main()

