




import math







def SCREAMING_SNAKE_CASE    (	)       ->	None:
       A__							=   input('''Enter message: '''						)
       A__							=   int(input(f"""Enter key [2-{len(lowercase_						) - 1}]: """						)						)
       A__							=   input('''Encryption/Decryption [e/d]: '''						)

       if mode.lower().startswith('''e'''						):
              A__							=   encrypt_message(lowercase_			,    lowercase_						)
       elif mode.lower().startswith('''d'''						):
              A__							=   decrypt_message(lowercase_			,    lowercase_						)

       # Append pipe symbol (vertical bar) to identify spaces at the end.
       print(f"""Output:\n{text + "|"}"""						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:
       A__							=   [''''''] * key
       for col in range(lowercase_						):
              A__							=   col
              while pointer < len(lowercase_						):
                     cipher_text[col] += message[pointer]
                     pointer += key
       return "".join(lowercase_						)







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:
       A__							=   math.ceil(len(lowercase_						) / key						)
       A__							=   key
       A__							=   (num_cols * num_rows) - len(lowercase_						)
       A__							=   [''''''] * num_cols
       A__							=   0
       A__							=   0

       for symbol in message:
              plain_text[col] += symbol
              col += 1

              if (
                  (col == num_cols)
                  or (col == num_cols - 1)
                  and (row >= num_rows - num_shaded_boxes)
              ):
                     A__							=   0
                     row += 1

       return "".join(lowercase_						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()








# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_lowerCamelCase     :		Optional[Any]								=       """facebook/wmt19-en-de"""
_lowerCamelCase     :		Optional[Any]								=       FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_lowerCamelCase     :		int								=       FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_lowerCamelCase     :		Union[str, Any]								=       FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_lowerCamelCase     :		str								=       """tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-de


