'''simple docstring'''




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					List[Any]:
    __lowerCamelCase						=						TaConfig.from_pretrained(UpperCamelCase__       )
    __lowerCamelCase						=						FlaxTaForConditionalGeneration(config=UpperCamelCase__       )
    __lowerCamelCase						=						checkpoints.load_tax_checkpoint(UpperCamelCase__       )

    __lowerCamelCase						=						'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    # Encoder
    for layer_index in range(config.num_layers       ):
        __lowerCamelCase						=						f"""layers_{str(UpperCamelCase__       )}"""

        # Self-Attention
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Layer Normalization
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __lowerCamelCase						=						tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __lowerCamelCase						=						tax_attention_key
        __lowerCamelCase						=						tax_attention_out
        __lowerCamelCase						=						tax_attention_query
        __lowerCamelCase						=						tax_attention_value

        __lowerCamelCase						=						tax_attention_layer_norm

        if split_mlp_wi:
            __lowerCamelCase						=						tax_mlp_wi_a
            __lowerCamelCase						=						tax_mlp_wi_a
        else:
            __lowerCamelCase						=						tax_mlp_wi

        __lowerCamelCase						=						tax_mlp_wo
        __lowerCamelCase						=						tax_mlp_layer_norm

    # Only for layer 0:
    __lowerCamelCase						=						tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    __lowerCamelCase						=						tax_encoder_rel_embedding

    # Assigning
    __lowerCamelCase						=						tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    __lowerCamelCase						=						tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers       ):
        __lowerCamelCase						=						f"""layers_{str(UpperCamelCase__       )}"""

        # Self-Attention
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
            '''kernel'''
        ]
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
            '''kernel'''
        ]
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
            '''kernel'''
        ]
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
            '''kernel'''
        ]

        # Layer Normalization
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        __lowerCamelCase						=						tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        __lowerCamelCase						=						tax_attention_key
        __lowerCamelCase						=						tax_attention_out
        __lowerCamelCase						=						tax_attention_query
        __lowerCamelCase						=						tax_attention_value

        __lowerCamelCase						=						tax_pre_attention_layer_norm

        __lowerCamelCase						=						tax_enc_dec_attention_key
        __lowerCamelCase						=						tax_enc_dec_attention_out
        __lowerCamelCase						=						tax_enc_dec_attention_query
        __lowerCamelCase						=						tax_enc_dec_attention_value

        __lowerCamelCase						=						tax_cross_layer_norm

        if split_mlp_wi:
            __lowerCamelCase						=						tax_mlp_wi_a
            __lowerCamelCase						=						tax_mlp_wi_a
        else:
            __lowerCamelCase						=						tax_mlp_wi

        __lowerCamelCase						=						tax_mlp_wo

        __lowerCamelCase						=						txa_mlp_layer_norm

    # Decoder Normalization
    __lowerCamelCase						=						tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    __lowerCamelCase						=						txa_decoder_norm

    # Only for layer 0:
    __lowerCamelCase						=						tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    __lowerCamelCase						=						tax_decoder_rel_embedding

    # Token Embeddings
    __lowerCamelCase						=						tax_model['''target''']['''token_embedder''']['''embedding''']
    __lowerCamelCase						=						txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        __lowerCamelCase						=						tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(UpperCamelCase__       )
    print('''T5X Model was sucessfully converted!'''       )


if __name__ == "__main__":
  __UpperCAmelCase  							=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
  )
  parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
  parser.add_argument(
      "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
  )
  __UpperCAmelCase  							=parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)

'''simple docstring'''




__UpperCAmelCase  							=[chr(i) for i in range(3_2, 1_2_6)]
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(reversed(range(len(alphabets))))
__UpperCAmelCase  							=[]
__UpperCAmelCase  							=__UpperCAmelCase  							=__UpperCAmelCase  							=0






def __lowerCAmelCase				(							)			->					List[Any]:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    __lowerCamelCase						=						gear_one[0]
    gear_one.append(UpperCamelCase__       )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(UpperCamelCase__       )       ) == 0:
        __lowerCamelCase						=						gear_two[0]
        gear_two.append(UpperCamelCase__       )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(UpperCamelCase__       )       ) == 0:
            __lowerCamelCase						=						gear_three[0]
            gear_three.append(UpperCamelCase__       )
            del gear_three[0]
            gear_three_pos += 1






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Optional[Any]:
    __lowerCamelCase						=						alphabets.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one[target]
    __lowerCamelCase						=						gear_two[target]
    __lowerCamelCase						=						gear_three[target]
    __lowerCamelCase						=						reflector[target]
    __lowerCamelCase						=						gear_three.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_two.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one.index(UpperCamelCase__       )
    code.append(alphabets[target]       )
    rotator()


if __name__ == "__main__":
  __UpperCAmelCase  							=list(input("Type your message:\n"))
  while True:
    try:
      __UpperCAmelCase  							=int(input("Please set token:(must be only digits)\n"))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("\n" + "".join(code))
  print(
      f'\nYour Token is {token} please write it down.\nIf you want to decode '
      "this message again you should input same digits as token!"
  )
