





# 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_		:				Union[str, Any]				=						'''tiny-wmt19-en-ru'''

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
		lowercase_		:				Tuple				=						Path(tmpdirname)
		lowercase_		:				List[Any]				=						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
		lowercase_		:				Union[str, Any]				=						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[str]				=						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_		:				Tuple				=						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_		:				List[str]				=						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowercase_		:				Dict				=						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowercase_		:				int				=						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       A__(		__lowerCAmelCase ):

				if not isinstance(__lowerCAmelCase	,			__lowerCAmelCase ):
								raise TypeError('only integers accepted as input' )
				else:
								_snake_case :			Any  						=   str(abs(__lowerCAmelCase ) )
								_snake_case :			List[str]  						=   [list(__lowerCAmelCase ) for char in range(len(__lowerCAmelCase ) )]
								for index in range(len(__lowerCAmelCase ) ):
												num_transpositions[index].pop(__lowerCAmelCase )
								return max(
								    int(''.join(list(__lowerCAmelCase ) ) ) for transposition in num_transpositions )


if __name__ == "__main__":
		__import__('''doctest''').testmod()






