"""simple docstring"""


import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
__UpperCamelCase									=			logging.get_logger('''transformers.models.speecht5''')

__UpperCamelCase									=			{
    '''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
    '''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
    '''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
    '''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
__UpperCamelCase									=			{
    '''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
    '''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
__UpperCamelCase									=			{
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
    '''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
    '''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
    '''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
    '''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
__UpperCamelCase									=			{
    '''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
    '''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
    '''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
    '''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
    '''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
    '''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
    '''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
    '''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
    '''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
__UpperCamelCase									=			{
    '''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
__UpperCamelCase									=			{
    '''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
__UpperCamelCase									=			{
    '''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
    '''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
    '''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
    '''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
    '''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
    '''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
    '''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
    '''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
    '''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
__UpperCamelCase									=			{
    '''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
    '''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
    '''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
    '''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
    '''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
    '''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
    '''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
    '''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
    '''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
    '''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
    '''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
    '''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
    '''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
__UpperCamelCase									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__UpperCamelCase									=			{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCamelCase									=			{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__UpperCamelCase									=			[]
__UpperCamelCase									=			[
    '''encoder.version''',
    '''encoder.layers.*.norm_k.weight''',
    '''encoder.layers.*.norm_k.bias''',
    '''decoder.version''',
    '''decoder.layers.*.norm_k.weight''',
    '''decoder.layers.*.norm_k.bias''',
    '''decoder.pos_emb.pe_k''',
    '''speech_encoder_prenet.embed_positions._float_tensor''',
    '''text_decoder_prenet.embed_positions._float_tensor''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''speech_decoder_prenet.*''',
    '''speech_decoder_postnet.*''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''speech_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]
__UpperCamelCase									=			IGNORE_KEYS + [
    '''encoder.proj''',
    '''text_encoder_prenet.*''',
    '''text_decoder_prenet.*''',
    '''text_decoder_postnet.*''',
]







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      str:
     for attribute in key.split('.'					):
          snake_case_					       =					getattr(UpperCAmelCase						,					UpperCAmelCase					)

     if weight_type is not None:
          snake_case_					       =					getattr(UpperCAmelCase						,					UpperCAmelCase					).shape
     else:
          snake_case_					       =					hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
              f' {value.shape} for {full_name}'					)

     if weight_type == "weight":
          snake_case_					       =					value
     elif weight_type == "weight_g":
          snake_case_					       =					value
     elif weight_type == "weight_v":
          snake_case_					       =					value
     elif weight_type == "bias":
          snake_case_					       =					value
     elif weight_type == "running_mean":
          snake_case_					       =					value
     elif weight_type == "running_var":
          snake_case_					       =					value
     elif weight_type == "num_batches_tracked":
          snake_case_					       =					value
     else:
          snake_case_					       =					value

     logger.info(f'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      List[str]:
     for key in ignore_keys:
          if key.endswith('.*'					):
               if name.startswith(key[:-1]					):
                    return True
          elif ".*." in key:
               snake_case_       ,	snake_case_					       =					key.split('.*.'					)
               if prefix in name and suffix in name:
                    return True
          elif key in name:
               return True
     return False







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      Any:
     snake_case_					       =					[]

     if task == "s2t":
          snake_case_					       =					hf_model.speechta.encoder.prenet.feature_encoder
          snake_case_					       =					MAPPING_S2T
          snake_case_					       =					IGNORE_KEYS_S2T
     elif task == "t2s":
          snake_case_					       =					None
          snake_case_					       =					MAPPING_T2S
          snake_case_					       =					IGNORE_KEYS_T2S
     elif task == "s2s":
          snake_case_					       =					hf_model.speechta.encoder.prenet.feature_encoder
          snake_case_					       =					MAPPING_S2S
          snake_case_					       =					IGNORE_KEYS_S2S
     else:
          raise ValueError(f'Unsupported task: {task}'					)

     for name, value in fairseq_dict.items():
          if should_ignore(UpperCAmelCase						,					UpperCAmelCase					):
               logger.info(f'{name} was ignored'					)
               continue

          snake_case_					       =					False
          if "conv_layers" in name:
               load_conv_layer(
                   UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					hf_model.config.feat_extract_norm == 'group'						,					)
               snake_case_					       =					True
          else:
               for key, mapped_key in MAPPING.items():
                    # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

                    if "*" in key:
                         snake_case_       ,	snake_case_					       =					key.split('.*.'					)
                         if prefix in name and suffix in name:
                              snake_case_					       =					suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
                    if key in name:
                         snake_case_					       =					True
                         if "*" in mapped_key:
                              snake_case_					       =					name.split(UpperCAmelCase					)[0].split('.'					)[-2]
                              snake_case_					       =					mapped_key.replace('*'						,					UpperCAmelCase					)
                         if "weight_g" in name:
                              snake_case_					       =					'weight_g'
                         elif "weight_v" in name:
                              snake_case_					       =					'weight_v'
                         elif "bias" in name:
                              snake_case_					       =					'bias'
                         elif "weight" in name:
                              snake_case_					       =					'weight'
                         elif "running_mean" in name:
                              snake_case_					       =					'running_mean'
                         elif "running_var" in name:
                              snake_case_					       =					'running_var'
                         elif "num_batches_tracked" in name:
                              snake_case_					       =					'num_batches_tracked'
                         else:
                              snake_case_					       =					None
                         set_recursively(UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)
                    continue
          if not is_used:
               unused_weights.append(UpperCAmelCase					)

     logger.warning(f'Unused weights: {unused_weights}'					)







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase					)				->      List[str]:
     snake_case_					       =					full_name.split('conv_layers.'					)[-1]
     snake_case_					       =					name.split('.'					)
     snake_case_					       =					int(items[0]					)
     snake_case_					       =					int(items[1]					)

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'					)
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f'{full_name} has size {value.shape}, but'
                        f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'					)
               snake_case_					       =					value
               logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
     else:
          unused_weights.append(UpperCAmelCase					)







@torch.no_grad()
def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					UpperCAmelCase=None						,					)				->      Dict:
     if config_path is not None:
          snake_case_					       =					SpeechTaConfig.from_pretrained(UpperCAmelCase					)
     else:
          snake_case_					       =					SpeechTaConfig()

     if task == "s2t":
          snake_case_					       =					config.max_text_positions
          snake_case_					       =					SpeechTaForSpeechToText(UpperCAmelCase					)
     elif task == "t2s":
          snake_case_					       =					1876
          snake_case_					       =					600
          snake_case_					       =					config.max_speech_positions
          snake_case_					       =					SpeechTaForTextToSpeech(UpperCAmelCase					)
     elif task == "s2s":
          snake_case_					       =					1876
          snake_case_					       =					config.max_speech_positions
          snake_case_					       =					SpeechTaForSpeechToSpeech(UpperCAmelCase					)
     else:
          raise ValueError(f'Unknown task name: {task}'					)

     if vocab_path:
          snake_case_					       =					SpeechTaTokenizer(UpperCAmelCase						,					model_max_length=config.max_text_positions					)

          # Mask token behaves like a normal word, i.e. include the space before it
          snake_case_					       =					AddedToken('<mask>'						,					lstrip=UpperCAmelCase						,					rstrip=UpperCAmelCase					)
          snake_case_					       =					mask_token
          tokenizer.add_special_tokens({'mask_token': mask_token}					)
          tokenizer.add_tokens(['<ctc_blank>']					)

     snake_case_					       =					SpeechTaFeatureExtractor()
     snake_case_					       =					SpeechTaProcessor(tokenizer=UpperCAmelCase						,					feature_extractor=UpperCAmelCase					)
     processor.save_pretrained(UpperCAmelCase					)

     snake_case_					       =					torch.load(UpperCAmelCase					)
     recursively_load_weights(fairseq_checkpoint['model']						,					UpperCAmelCase						,					UpperCAmelCase					)

     model.save_pretrained(UpperCAmelCase					)

     if repo_id:
          print('Pushing to the hub...'					)
          processor.push_to_hub(UpperCAmelCase					)
          model.push_to_hub(UpperCAmelCase					)


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()
    parser.add_argument(
        '''--task''',
        default='''s2t''',
        type=str,
        help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
    )
    parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
    parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
    parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
    parser.add_argument(
        '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )

    __UpperCamelCase									=			parser.parse_args()
    convert_speechta_checkpoint(
        args.task,
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.vocab_path,
        args.push_to_hub,
    )





import string

import numpy
def 				__lowerCamelCase						( UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		int						):




      """simple docstring"""





      return b if a == 0 else greatest_common_divisor(b % a ,  UpperCAmelCase_						)







class 		_snake_case  :
 SCREAMING_SNAKE_CASE__      =       string.ascii_uppercase + string.digits
 # This cipher takes alphanumerics into account
 # i.e. a total of 36 characters

 # take x and return x % len(key_string)
 SCREAMING_SNAKE_CASE__      =       numpy.vectorize(lambda _snake_case							: x % 36							)

 SCREAMING_SNAKE_CASE__      =       numpy.vectorize(_snake_case							)




 def __init__(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.modulus(_lowerCamelCase    )  # mod36 calc's on the encrypt key
       self.check_determinant()  # validate the determinant of the encryption key
       a						:int 							=       encrypt_key.shape[0]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string.index(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       return self.key_string[round(_lowerCamelCase    )]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       round(numpy.linalg.det(self.encrypt_key    )    )

       if det < 0:
             a						:Any 							=       det % len(self.key_string    )

       a						:Dict 							=       len(self.key_string    )
       if greatest_common_divisor(_lowerCamelCase					,  len(self.key_string    )    ) != 1:
             a						:int 							=       (
                 F'''determinant modular {req_l} of encryption key({det}) '''
                 F'''is not co prime w.r.t {req_l}.\nTry another key.'''
             )
             raise ValueError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       [char for char in text.upper() if char in self.key_string]

       a						:List[str] 							=       chars[-1]
       while len(_lowerCamelCase    ) % self.break_key != 0:
             chars.append(_lowerCamelCase    )

       return "".join(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:Dict 							=       self.process_text(text.upper()    )
       a						:List[str] 							=       ''''''

       for i in range(0					,  len(_lowerCamelCase    ) - self.break_key + 1					,  self.break_key    ):
             a						:int 							=       text[i : i + self.break_key]
             a						:Optional[int] 							=       [self.replace_letters(_lowerCamelCase    ) for char in batch]
             a						:Union[str, Any] 							=       numpy.array([vec]    ).T
             a						:str 							=       self.modulus(self.encrypt_key.dot(_lowerCamelCase    )    ).T.tolist()[
                 0
             ]
             a						:List[Any] 							=       ''''''.join(
                 self.replace_digits(_lowerCamelCase    ) for num in batch_encrypted    )
             encrypted += encrypted_batch

       return encrypted




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       round(numpy.linalg.det(self.encrypt_key    )    )

       if det < 0:
             a						:int 							=       det % len(self.key_string    )
       a						:Tuple 							=       None
       for i in range(len(self.key_string    )    ):
             if (det * i) % len(self.key_string    ) == 1:
                   a						:Tuple 							=       i
                   break

       a						:List[str] 							=       (
           det_inv
           * numpy.linalg.det(self.encrypt_key    )
           * numpy.linalg.inv(self.encrypt_key    )
       )

       return self.to_int(self.modulus(_lowerCamelCase    )    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       self.make_decrypt_key()
       a						:str 							=       self.process_text(text.upper()    )
       a						:List[Any] 							=       ''''''

       for i in range(0					,  len(_lowerCamelCase    ) - self.break_key + 1					,  self.break_key    ):
             a						:Optional[Any] 							=       text[i : i + self.break_key]
             a						:List[Any] 							=       [self.replace_letters(_lowerCamelCase    ) for char in batch]
             a						:str 							=       numpy.array([vec]    ).T
             a						:Dict 							=       self.modulus(decrypt_key.dot(_lowerCamelCase    )    ).T.tolist()[0]
             a						:List[Any] 							=       ''''''.join(
                 self.replace_digits(_lowerCamelCase    ) for num in batch_decrypted    )
             decrypted += decrypted_batch

       return decrypted







def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Tuple 							=       int(input('''Enter the order of the encryption key: '''						)						)
      a						:Dict 							=       []

      print('''Enter each row of the encryption key with space separated integers'''						)
      for _ in range(UpperCAmelCase_						):
            a						:List[str] 							=       [int(UpperCAmelCase_						) for x in input().split()]
            hill_matrix.append(UpperCAmelCase_						)

      a						:Any 							=       HillCipher(numpy.array(UpperCAmelCase_						)						)

      print('''Would you like to encrypt or decrypt some text? (1 or 2)'''						)
      a						:Any 							=       input('''\n1. Encrypt\n2. Decrypt\n'''						)
      if option == "1":
            a						:str 							=       input('''What text would you like to encrypt?: '''						)
            print('''Your encrypted text is:'''						)
            print(hc.encrypt(UpperCAmelCase_						)						)
      elif option == "2":
            a						:Dict 							=       input('''What text would you like to decrypt?: '''						)
            print('''Your decrypted text is:'''						)
            print(hc.decrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     main()





