





from typing import 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():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

__A             =       logging.get_logger(__name__)



@add_end_docstrings(__SCREAMING_SNAKE_CASE					)
class      _SCREAMING_SNAKE_CASE		(			__SCREAMING_SNAKE_CASE					):







 '''simple docstring'''

 def __init__(self       :     List[str] ,				*UpperCAmelCase_       :     List[str] ,				**UpperCAmelCase_       :     Tuple) ->Any:



   '''simple docstring'''



   super().__init__(*UpperCamelCase_ ,				**UpperCamelCase_)
   requires_backends(self ,				"vision")
   self.check_model_type(UpperCamelCase_)

 def __call__(self       :     int ,				UpperCAmelCase_       :     Union[str, List[str], "Image.Image", List["Image.Image"]] ,				**UpperCAmelCase_       :     List[str]) ->List[Any]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_      (self       :     int ,				**UpperCAmelCase_       :     Dict) ->Tuple:



   '''simple docstring'''



   return {}, {}, {}

 def 							SCREAMING_SNAKE_CASE_      (self       :     Any ,				UpperCAmelCase_       :     Tuple) ->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:   List[str]    						=load_image(UpperCamelCase_)
   lowerCamelCase__:   Optional[Any]    						=image.size
   lowerCamelCase__:   Optional[Any]    						=self.image_processor(images=UpperCamelCase_ ,				return_tensors=self.framework)
   return model_inputs

 def 							SCREAMING_SNAKE_CASE_      (self       :     Optional[int] ,				UpperCAmelCase_       :     Union[str, Any]) ->Any:



   '''simple docstring'''



   lowerCamelCase__:   Union[str, Any]    						=self.model(**UpperCamelCase_)
   return model_outputs







 def 							SCREAMING_SNAKE_CASE_      (self       :     List[Any] ,				UpperCAmelCase_       :     Dict) ->Dict:



   '''simple docstring'''



   lowerCamelCase__:   int    						=model_outputs.predicted_depth
   lowerCamelCase__:   List[str]    						=torch.nn.functional.interpolate(
       predicted_depth.unsqueeze(1) ,				size=self.image_size[::-1] ,				mode="bicubic" ,				align_corners=UpperCamelCase_)
   lowerCamelCase__:   Optional[Any]    						=prediction.squeeze().cpu().numpy()
   lowerCamelCase__:   List[str]    						=(output * 255 / np.max(UpperCamelCase_)).astype("uint8")
   lowerCamelCase__:   Union[str, Any]    						=Image.fromarray(UpperCamelCase_)
   lowerCamelCase__:   Tuple    						={}
   lowerCamelCase__:   Tuple    						=predicted_depth
   lowerCamelCase__:   Dict    						=depth
   return output_dict












def   lowerCAmelCase_				( __a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[str]    						=""
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups





def   lowerCAmelCase_				( __a	)  ->	dict[str, str]:







  """simple docstring"""





  lowerCamelCase__:   Dict    						=[chr(i + 65	) for i in range(26	)]
  # Remove duplicate characters from key
  lowerCamelCase__:   Union[str, Any]    						=remove_duplicates(key.upper()	)
  lowerCamelCase__:   List[Any]    						=len(__a	)
  # First fill cipher with key characters
  lowerCamelCase__:   Optional[Any]    						={alphabet[i]: char for i, char in enumerate(__a	)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(__a	)			,					26	):
    lowerCamelCase__:   Optional[int]    						=alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      lowerCamelCase__:   List[Any]    						=alphabet[i - offset]
    lowerCamelCase__:   str    						=char
  return cipher_alphabet





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  return "".join(cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( __a			,					__a	)  ->	str:







  """simple docstring"""





  lowerCamelCase__:   List[Any]    						={v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(__a			,					__a	) for ch in message.upper()	)





def   lowerCAmelCase_				( )  ->	None:







  """simple docstring"""





  lowerCamelCase__:   Optional[Any]    						=input("Enter message to encode or decode: "	).strip()
  lowerCamelCase__:   Dict    						=input("Enter keyword: "	).strip()
  lowerCamelCase__:   Any    						=input("Encipher or decipher? E/D:"	).strip()[0].lower()
  try:
    lowerCamelCase__:   Union[str, Any]    						={"e": encipher, "d": decipher}[option]
  except KeyError:
    raise KeyError("invalid input option"	)
  lowerCamelCase__:   List[Any]    						=create_cipher_map(__a	)
  print(func(__a			,					__a	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

