
"""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

lowerCamelCase__        =		"""tiny-wmt19-en-ru"""

# Build

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

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

							lowerCamelCase__        =		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,
							)

lowerCamelCase__        =		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,
)

lowerCamelCase__        =		FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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







from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	if version.parse(hfh.__version__		).release < version.parse('0.11.0'		).release:
		# old versions of hfh don't url-encode the file path
		__lowerCAmelCase      :						List[str]						      =				quote(_UpperCamelCase		)
	return hfh.hf_hub_url(_UpperCamelCase	,  _UpperCamelCase	,  repo_type='dataset'	,  revision=_UpperCamelCase		)