

"""simple docstring"""




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   :	List[Any]												=						'tiny-wmt19-en-ru'

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
				_lowercase   :	List[Any]												=						Path(tmpdirname)
				_lowercase   :	Dict												=						build_dir / VOCAB_FILES_NAMES['src_vocab_file']
				_lowercase   :	Dict												=						build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
				_lowercase   :	List[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))

				_lowercase   :	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,
				)

_lowercase   :	Dict												=						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,
)

_lowercase   :	str												=						FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
_lowercase   :	str												=						tokenizer(['Making tiny model'], return_tensors='pt')
_lowercase   :	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


"""simple docstring"""



from itertools import permutations
def 					lowerCamelCase   (     _snake_case ):
    if num[3] % 2 != 0:
        return False

    if (num[2] + num[3] + num[4]) % 3 != 0:
        return False

    if num[5] % 5 != 0:
        return False

    UpperCAmelCase__	:      List[str]     							=					[7, 11, 13, 17]
    for i, test in enumerate(_snake_case ):
        if (num[i + 4] * 100 + num[i + 5] * 10 + num[i + 6]) % test != 0:
            return False
    return True
def 					lowerCamelCase   (     _snake_case = 10 ):
    return sum(
        int(''.join(map(_snake_case    ,_snake_case ) ) )
        for num in permutations(range(_snake_case ) )
        if is_substring_divisible(_snake_case ) )


if __name__ == "__main__":
    print(f'{solution() = }')

