


# 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

__lowerCAmelCase		       =							'''facebook/wmt19-en-de'''
__lowerCAmelCase		       =							FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCAmelCase		       =							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,
    )
)

__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
__lowerCAmelCase		       =							'''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






import unittest

from knapsack import knapsack as k





class   __a			(    unittest.TestCase      ):
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   0
        lowercase__:   List[Any]  			=   [0]
        lowercase__:   str  			=   [0]
        lowercase__:   Tuple  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )

        lowercase__:   Optional[Any]  			=   [60]
        lowercase__:   Dict  			=   [10]
        lowercase__:   str  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 0 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   3
        lowercase__:   List[str]  			=   [1, 2, 3]
        lowercase__:   Union[str, Any]  			=   [3, 2, 1]
        lowercase__:   Union[str, Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 5 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   50
        lowercase__:   str  			=   [60, 100, 120]
        lowercase__:   Any  			=   [10, 20, 30]
        lowercase__:   List[Any]  			=   len(lowerCAmelCase__ )
        self.assertEqual(k.knapsack(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )				, 220 )


if __name__ == "__main__":
 unittest.main()


