




"""simple docstring"""

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
       from transformers import TvltImageProcessor

if is_speech_available():
       from transformers import TvltFeatureExtractor

from transformers import TvltProcessor





@require_torch
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       'ZinengTang/tvlt-base'
       _A        =       tempfile.mkdtemp()

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	str		):
       return TvltImageProcessor.from_pretrained(self.checkpoint   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Any		):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       shutil.rmtree(self.tmpdirname		)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self.get_image_processor()
       _A        =       self.get_feature_extractor()

       _A        =       TvltProcessor(image_processor=_UpperCAmelCase   ,							feature_extractor=_UpperCAmelCase		)
       processor.save_pretrained(self.tmpdirname		)
       _A        =       TvltProcessor.from_pretrained(self.tmpdirname		)

       self.assertIsInstance(processor.feature_extractor   ,							_UpperCAmelCase		)
       self.assertIsInstance(processor.image_processor   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self.get_image_processor()
       _A        =       self.get_feature_extractor()

       _A        =       TvltProcessor(image_processor=_UpperCAmelCase   ,							feature_extractor=_UpperCAmelCase		)

       _A        =       np.ones([12_000]		)

       _A        =       feature_extractor(_UpperCAmelCase   ,							return_tensors='np'		)
       _A        =       processor(audio=_UpperCAmelCase   ,							return_tensors='np'		)

       for key in audio_dict.keys():
             self.assertAlmostEqual(audio_dict[key].sum()   ,							input_processor[key].sum()   ,							delta=1E-2		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       self.get_image_processor()
       _A        =       self.get_feature_extractor()

       _A        =       TvltProcessor(image_processor=_UpperCAmelCase   ,							feature_extractor=_UpperCAmelCase		)

       _A        =       np.ones([3, 224, 224]		)

       _A        =       image_processor(_UpperCAmelCase   ,							return_tensors='np'		)
       _A        =       processor(images=_UpperCAmelCase   ,							return_tensors='np'		)

       for key in image_dict.keys():
             self.assertAlmostEqual(image_dict[key].sum()   ,							input_processor[key].sum()   ,							delta=1E-2		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       _A        =       self.get_image_processor()
       _A        =       self.get_feature_extractor()

       _A        =       TvltProcessor(image_processor=_UpperCAmelCase   ,							feature_extractor=_UpperCAmelCase		)

       _A        =       np.ones([12_000]		)
       _A        =       np.ones([3, 224, 224]		)

       _A        =       processor(audio=_UpperCAmelCase   ,							images=_UpperCAmelCase		)

       self.assertListEqual(list(inputs.keys()		)   ,							['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask']		)

       # test if it raises when no input is passed
       with pytest.raises(_UpperCAmelCase		):
             processor()



 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self.get_image_processor()
       _A        =       self.get_feature_extractor()

       _A        =       TvltProcessor(image_processor=_UpperCAmelCase   ,							feature_extractor=_UpperCAmelCase		)

       self.assertListEqual(
           processor.model_input_names   ,							image_processor.model_input_names + feature_extractor.model_input_names   ,							msg='`processor` and `image_processor`+`feature_extractor` model input names do not match'   ,							)







"""simple docstring"""

def 						_snake_case					( _snake_case							: str							)   -> str:






      '''simple docstring'''


      _A        =       ''
      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 						_snake_case					( _snake_case							: str							)   -> dict[str, str]:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: dict[str, str]							)   -> str:






      '''simple docstring'''


      _A        =       {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_snake_case    ,  _snake_case							) for ch in message.upper()							)






def 						_snake_case					( )   -> None:






      '''simple docstring'''


      _A        =       input('Enter message to encode or decode: '							).strip()
      _A        =       input('Enter keyword: '							).strip()
      _A        =       input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            _A        =       {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      _A        =       create_cipher_map(_snake_case							)
      print(func(_snake_case    ,  _snake_case							)							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()

