

"""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

_a							=							'tiny-wmt19-en-ru'

# Build

# borrowed from a test
_a							=							[
    '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>',
]
_a							=							dict(zip(vocab, range(len(vocab))))
_a							=							['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
   _a							=							Path(tmpdirname)
   _a							=							build_dir / VOCAB_FILES_NAMES['src_vocab_file']
   _a							=							build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
   _a							=							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))

   _a							=							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,
   )

_a							=							FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    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,
)

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

# Test
_a							=							tokenizer(['Making tiny model'], return_tensors='pt')
_a							=							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"""





import os
import random
import sys

from . import cryptomath_module as cryptoMath  # noqa: N812
from . import rabin_miller as rabinMiller  # noqa: N812




def   _A  (  )     ->						None:
   '''simple docstring'''


   print("Making key files...")
   make_key_files("rsa",      1024)
   print("Key files generation successful.")




def   _A  (  UpperCamelCase_					:	int)     ->						tuple[tuple[int, int], tuple[int, int]]:
   '''simple docstring'''


   print("Generating prime p...")
   __lowercase								=						rabinMiller.generate_large_prime(UpperCamelCase_)
   print("Generating prime q...")
   __lowercase								=						rabinMiller.generate_large_prime(UpperCamelCase_)
   __lowercase								=						p * q

   print("Generating e that is relatively prime to (p - 1) * (q - 1)...")
   while True:
      __lowercase								=						random.randrange(2 ** (key_size - 1),      2 ** (key_size))
      if cryptoMath.gcd(UpperCamelCase_,      (p - 1) * (q - 1)) == 1:
         break

   print("Calculating d that is mod inverse of e...")
   __lowercase								=						cryptoMath.find_mod_inverse(UpperCamelCase_,      (p - 1) * (q - 1))

   __lowercase								=						(n, e)
   __lowercase								=						(n, d)
   return (public_key, private_key)




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	int)     ->						None:
   '''simple docstring'''


   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()

   __lowercase		,__lowercase								=						generate_key(UpperCamelCase_)
   print(F"""\nWriting public key to file {name}_pubkey.txt...""")
   with open(F"""{name}_pubkey.txt""",      "w") as out_file:
      out_file.write(F"""{key_size},{public_key[0]},{public_key[1]}""")

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


if __name__ == "__main__":
   main()




