




"""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





def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      while b:
            _A			,   _A							:     List[str]			=						b, a % b
      return a







def 				lowerCAmelCase_	(     snake_case_,snake_case_     ):
      return a if b == 0 else euclidean_gcd_recursive(snake_case_,a % b     )







def 				lowerCAmelCase_	(     ):
      print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3,5     )}'''     )
      print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5,3     )}'''     )
      print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1,3     )}'''     )
      print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3,6     )}'''     )
      print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6,3     )}'''     )

      print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3,5     )}'''     )
      print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5,3     )}'''     )
      print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1,3     )}'''     )
      print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3,6     )}'''     )
      print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6,3     )}'''     )


if __name__ == "__main__":
  main()

