
# 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__      :		Dict       							=  "tiny-wmt19-en-ru"

# Build

# borrowed from a test
UpperCamelCase__      :		str       							=  [
    "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__      :		Optional[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__      :		int       							=  Path(tmpdirname)
     UpperCamelCase__      :		Union[str, Any]       							=  build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
     UpperCamelCase__      :		List[str]       							=  build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
     UpperCamelCase__      :		Union[str, Any]       							=  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__      :		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__      :		Dict       							=  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__      :		int       							=  FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
UpperCamelCase__      :		Optional[Any]       							=  tokenizer(["Making tiny model"], return_tensors="pt")
UpperCamelCase__      :		Tuple       							=  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_	(				SCREAMING_SNAKE_CASE_						)     ->     str:




   """simple docstring"""
   return "".join([hex(SCREAMING_SNAKE_CASE_						)[2:].zfill(2						).upper() for byte in list(SCREAMING_SNAKE_CASE_						)]						)
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bytes:




   """simple docstring"""
   # Check data validity, following RFC3548
   # https://www.ietf.org/rfc/rfc3548.txt
   if (len(SCREAMING_SNAKE_CASE_						) % 2) != 0:
      raise ValueError(
          """Base16 encoded data is invalid:
Data does not have an even number of hex digits."""						)
   # Check the character set - the standard base16 alphabet
   # is uppercase according to RFC3548 section 6
   if not set(SCREAMING_SNAKE_CASE_						) <= set("""0123456789ABCDEF"""						):
      raise ValueError(
          """Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters."""						)
   # For every two hexadecimal digits (= a byte), turn it into an integer.
   # Then, string the result together into bytes, and return it.
   return bytes(int(data[i] + data[i + 1]    ,      16						) for i in range(0    ,      len(SCREAMING_SNAKE_CASE_						)    ,      2						)						)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






