


'''simple docstring'''






# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowercase				='tiny-wmt19-en-ru'

# Build

# borrowed from a test
lowercase				=[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
lowercase				=dict(zip(vocab, range(len(vocab))))
lowercase				=['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowercase				=Path(tmpdirname)
 lowercase				=build_dir / VOCAB_FILES_NAMES['src_vocab_file']
 lowercase				=build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
 lowercase				=build_dir / VOCAB_FILES_NAMES['merges_file']
 with open(src_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, 'w') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, 'w') as fp:
  fp.write('\n'.join(merges))

 lowercase				=FSMTTokenizer(
     langs=['en', 'ru'],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowercase				=FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

lowercase				=FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
lowercase				=tokenizer(['Making tiny model'], return_tensors='pt')
lowercase				=tiny_model(**batch)

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

# Save
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-ru





'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =''
   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 lowerCamelCase__     (		__lowerCamelCase			:				str				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =[chr(i + 6_5				) for i in range(2_6				)]
   # Remove duplicate characters from key
   _UpperCAmelCase  :  Any				       =remove_duplicates(key.upper()				)
   _UpperCAmelCase  :  Optional[Any]				       =len(__lowerCamelCase				)
   # First fill cipher with key characters
   _UpperCAmelCase  :  List[Any]				       ={alphabet[i]: char for i, char in enumerate(__lowerCamelCase				)}
   # Then map remaining characters in alphabet to
   # the alphabet from the beginning
   for i in range(len(__lowerCamelCase				)			,       2_6				):
      _UpperCAmelCase  :  Union[str, Any]				       =alphabet[i - offset]
      # Ensure we are not mapping letters to letters previously mapped
      while char in key:
         offset -= 1
         _UpperCAmelCase  :  str				       =alphabet[i - offset]
      _UpperCAmelCase  :  Any				       =char
   return cipher_alphabet





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




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





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				dict[str, str]				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       ={v: k for k, v in cipher_map.items()}
   return "".join(rev_cipher_map.get(__lowerCamelCase			,       __lowerCamelCase				) for ch in message.upper()				)





def lowerCamelCase__     (		):







   '''simple docstring'''




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


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

