
'''simple docstring'''

import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   import numpy as np
   import torch

if is_vision_available():
   from PIL import Image



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Union[str, Any]   ,     snake_case_				:	Tuple=False   ,     snake_case_				:	Optional[int]=False   ,     snake_case_				:	int=False							)       ->	Any:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 SCREAMING_SNAKE_CASE  :  List[Any]								=		[]
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   SCREAMING_SNAKE_CASE  :  Tuple								=		image_processor_tester.max_resolution
  else:
   # To avoid getting image width/height 0
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_processor_tester.min_resolution
   if getattr(snake_case_   ,     'size_divisor'   ,     snake_case_							):
    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
    SCREAMING_SNAKE_CASE  :  Any								=		max(image_processor_tester.size_divisor   ,     snake_case_							)
   SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		np.random.choice(np.arange(snake_case_   ,     image_processor_tester.max_resolution							)   ,     2							)
  image_inputs.append(
      np.random.randint(255   ,     size=(image_processor_tester.num_channels, width, height)   ,     dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  SCREAMING_SNAKE_CASE  :  Any								=		[Image.fromarray(np.moveaxis(snake_case_   ,     0   ,     -1							)							) for image in image_inputs]

 if torchify:
  SCREAMING_SNAKE_CASE  :  List[Any]								=		[torch.from_numpy(snake_case_							) for image in image_inputs]

 return image_inputs



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any   ,     snake_case_				:	Dict=10   ,     snake_case_				:	Optional[int]=10   ,     snake_case_				:	Any=False   ,     snake_case_				:	List[str]=False							)       ->	Any:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		[]
 for i in range(image_processor_tester.num_frames							):
  video.append(np.random.randint(255   ,     size=(image_processor_tester.num_channels, width, height)   ,     dtype=np.uinta							)							)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[Image.fromarray(np.moveaxis(snake_case_   ,     0   ,     -1							)							) for frame in video]

 if torchify:
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		[torch.from_numpy(snake_case_							) for frame in video]

 return video



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	str=False   ,     snake_case_				:	Optional[int]=False   ,     snake_case_				:	List[Any]=False							)       ->	Tuple:
 assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

 SCREAMING_SNAKE_CASE  :  int								=		[]
 for i in range(image_processor_tester.batch_size							):
  if equal_resolution:
   SCREAMING_SNAKE_CASE  :  str								=		image_processor_tester.max_resolution
  else:
   SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		np.random.choice(
       np.arange(image_processor_tester.min_resolution   ,     image_processor_tester.max_resolution							)   ,     2							)
   SCREAMING_SNAKE_CASE  :  Tuple								=		prepare_video(
       image_processor_tester=snake_case_   ,     width=snake_case_   ,     height=snake_case_   ,     numpify=snake_case_   ,     torchify=snake_case_   ,     )
  video_inputs.append(snake_case_							)

 return video_inputs





class 		_a     :







   """simple docstring"""




   A		= None






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.image_processing_class(**self.image_processor_dict      )
    SCREAMING_SNAKE_CASE  :  int								=		json.loads(image_processor.to_json_string()      )
    for key, value in self.image_processor_dict.items():
     self.assertEqual(obj[key]    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
     SCREAMING_SNAKE_CASE  :  List[Any]								=		os.path.join(__SCREAMING_SNAKE_CASE    ,'image_processor.json'      )
     image_processor_first.to_json_file(__SCREAMING_SNAKE_CASE      )
     SCREAMING_SNAKE_CASE  :  Any								=		self.image_processing_class.from_json_file(__SCREAMING_SNAKE_CASE      )

    self.assertEqual(image_processor_second.to_dict()    ,image_processor_first.to_dict()      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  int								=		self.image_processing_class(**self.image_processor_dict      )

    with tempfile.TemporaryDirectory() as tmpdirname:
     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_processor_first.save_pretrained(__SCREAMING_SNAKE_CASE      )[0]
     check_json_file_has_correct_format(__SCREAMING_SNAKE_CASE      )
     SCREAMING_SNAKE_CASE  :  List[str]								=		self.image_processing_class.from_pretrained(__SCREAMING_SNAKE_CASE      )

    self.assertEqual(image_processor_second.to_dict()    ,image_processor_first.to_dict()      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		self.image_processing_class()
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE      )






   @require_torch
   @require_vision
   def 					__a			(       self      ):
    if self.test_cast_dtype is not None:
     # Initialize image_processor
     SCREAMING_SNAKE_CASE  :  List[Any]								=		self.image_processing_class(**self.image_processor_dict      )

     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE  :  List[str]								=		prepare_image_inputs(self.image_processor_tester    ,equal_resolution=__SCREAMING_SNAKE_CASE    ,torchify=__SCREAMING_SNAKE_CASE      )

     SCREAMING_SNAKE_CASE  :  Tuple								=		image_processor(__SCREAMING_SNAKE_CASE    ,return_tensors='pt'      )
     # for layoutLM compatiblity
     self.assertEqual(encoding.pixel_values.device    ,torch.device('cpu'      )      )
     self.assertEqual(encoding.pixel_values.dtype    ,torch.floataa      )

     SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		image_processor(__SCREAMING_SNAKE_CASE    ,return_tensors='pt'      ).to(torch.floataa      )
     self.assertEqual(encoding.pixel_values.device    ,torch.device('cpu'      )      )
     self.assertEqual(encoding.pixel_values.dtype    ,torch.floataa      )

     SCREAMING_SNAKE_CASE  :  Dict								=		image_processor(__SCREAMING_SNAKE_CASE    ,return_tensors='pt'      ).to('cpu'    ,torch.bfloataa      )
     self.assertEqual(encoding.pixel_values.device    ,torch.device('cpu'      )      )
     self.assertEqual(encoding.pixel_values.dtype    ,torch.bfloataa      )

     with self.assertRaises(__SCREAMING_SNAKE_CASE      ):
      SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_processor(__SCREAMING_SNAKE_CASE    ,return_tensors='pt'      ).to(torch.bfloataa    ,'cpu'      )

     # Try with text + image feature
     SCREAMING_SNAKE_CASE  :  Any								=		image_processor(__SCREAMING_SNAKE_CASE    ,return_tensors='pt'      )
     encoding.update({'input_ids': torch.LongTensor([[1, 2, 3], [4, 5, 6]]      )}      )
     SCREAMING_SNAKE_CASE  :  str								=		encoding.to(torch.floataa      )

     self.assertEqual(encoding.pixel_values.device    ,torch.device('cpu'      )      )
     self.assertEqual(encoding.pixel_values.dtype    ,torch.floataa      )
     self.assertEqual(encoding.input_ids.dtype    ,torch.long      )




'''simple docstring'''

import random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


