


"""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 numpy as np






def      a__ (				__lowercase     ,			__lowercase   )	->	np.ndarray:
				return np.where(vector > 0     ,			__lowercase     ,			(alpha * (np.exp(__lowercase   ) - 1))   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()