

# 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

UpperCAmelCase__ :     List[Any]				    =    """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
UpperCAmelCase__ :     int				    =    [
    """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>""",
]
UpperCAmelCase__ :     Union[str, Any]				    =    dict(zip(vocab, range(len(vocab))))
UpperCAmelCase__ :     Optional[Any]				    =    ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
  UpperCAmelCase__ :     List[str]				    =    Path(tmpdirname)
  UpperCAmelCase__ :     List[Any]				    =    build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
  UpperCAmelCase__ :     Optional[Any]				    =    build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
  UpperCAmelCase__ :     int				    =    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))

  UpperCAmelCase__ :     List[str]				    =    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,
  )

UpperCAmelCase__ :     str				    =    FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

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

# Test
UpperCAmelCase__ :     List[Any]				    =    tokenizer(["""Making tiny model"""], return_tensors="""pt""")
UpperCAmelCase__ :     Union[str, Any]				    =    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







import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

UpperCAmelCase__ :     Optional[Any]				    =    3






def 					__lowercase      (     _A			)     ->						int:
      print("""Generating primitive root of p"""			)
      while True:
            SCREAMING_SNAKE_CASE :      int       =   random.randrange(3						,    _A			)
            if pow(_A						,    2						,    _A			) == 1:
                  continue
            if pow(_A						,    _A						,    _A			) == 1:
                  continue
            return g






def 					__lowercase      (     _A			)     ->						tuple[tuple[int, int, int, int], tuple[int, int]]:
      print("""Generating prime p..."""			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   rabin_miller.generate_large_prime(_A			)  # select large prime number.
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   primitive_root(_A			)  # one primitive root on modulo p.
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   random.randrange(3						,    _A			)  # private_key -> have to be greater than 2 for safety.
      SCREAMING_SNAKE_CASE :      List[str]       =   cryptomath.find_mod_inverse(pow(_A						,    _A						,    _A			)						,    _A			)

      SCREAMING_SNAKE_CASE :      str       =   (key_size, e_a, e_a, p)
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (key_size, d)

      return public_key, private_key






def 					__lowercase      (     _A						,    _A			)     ->						None:
      if os.path.exists(F"{name}_pubkey.txt"			) or os.path.exists(F"{name}_privkey.txt"			):
            print("""\nWARNING:"""			)
            print(
                F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
                """Use a different name or delete these files and re-run this program."""			)
            sys.exit()

      SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[str]       =   generate_key(_A			)
      print(F"\nWriting public key to file {name}_pubkey.txt..."			)
      with open(F"{name}_pubkey.txt"						,    """w"""			) as fo:
            fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"			)

      print(F"Writing private key to file {name}_privkey.txt..."			)
      with open(F"{name}_privkey.txt"						,    """w"""			) as fo:
            fo.write(F"{private_key[0]},{private_key[1]}"			)






def 					__lowercase      (     )     ->						None:
      print("""Making key files..."""			)
      make_key_files("""elgamal"""						,    2048			)
      print("""Key files generation successful"""			)


if __name__ == "__main__":
  main()




