





# 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=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,
)

_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'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int		)					->	int:







     """simple docstring"""
     while second != 0:
          _A				=							first & second
          first ^= second
          _A				=							c << 1
     return first


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowerCAmelCase						=					int(input("""Enter the first number: """).strip())
    lowerCAmelCase						=					int(input("""Enter the second number: """).strip())
    print(F'{add(first, second) = }')





