





# 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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_SCREAMING_SNAKE_CASE 		=						"""facebook/wmt19-en-de"""
_SCREAMING_SNAKE_CASE 		=						FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_SCREAMING_SNAKE_CASE 		=						FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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,
    )
)

_SCREAMING_SNAKE_CASE 		=						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=						tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=						tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_SCREAMING_SNAKE_CASE 		=						"""tiny-wmt19-en-de"""
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-de








def 							SCREAMING_SNAKE_CASE__  (      __a = 60_08_51_47_51_43				):

	try:
		snake_case_						:     Optional[Any]     =		int(__a				)
	except (TypeError, ValueError):
		raise TypeError('Parameter n must be int or castable to int.'				)
	if n <= 0:
		raise ValueError('Parameter n must be greater than or equal to one.'				)
	snake_case_						:     Any     =		2
	snake_case_						:     Any     =		0
	if n == 2:
		return 2
	while n > 2:
		while n % i != 0:
			i += 1
		snake_case_						:     Tuple     =		i
		while n % i == 0:
			snake_case_						:     List[str]     =		n // i
		i += 1
	return int(__a				)


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

