# 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

SCREAMING_SNAKE_CASE :	Any							     =		"""tiny-wmt19-en-ru"""

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
  SCREAMING_SNAKE_CASE :	Tuple							     =		Path(tmpdirname)
  SCREAMING_SNAKE_CASE :	Dict							     =		build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
  SCREAMING_SNAKE_CASE :	Tuple							     =		build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
  SCREAMING_SNAKE_CASE :	Tuple							     =		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))

  SCREAMING_SNAKE_CASE :	List[Any]							     =		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,
  )

SCREAMING_SNAKE_CASE :	str							     =		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,
)

SCREAMING_SNAKE_CASE :	Optional[int]							     =		FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

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



def   __A		(			_A = 100_0000					):



 """simple docstring"""


 __a       =  [i - 1 for i in range(limit + 1					)]

 for i in range(2							,				limit + 1					):
  if phi[i] == i - 1:
   for j in range(2 * i							,				limit + 1							,				_A					):
    phi[j] -= phi[j] // i

 return sum(phi[2 : limit + 1]					)


if __name__ == "__main__":
  print(solution())


