






# 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     :							Optional[int]    ='''facebook/wmt19-en-de'''
lowerCamelCase     :							Dict    =FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCamelCase     :							Any    =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     :							Optional[Any]    =FSMTForConditionalGeneration(config)
print(F"""num of params {tiny_model.num_parameters()}""")

# Test
lowerCamelCase     :							Optional[Any]    =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase     :							List[str]    =tiny_model(**batch)

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

# Save
lowerCamelCase     :							List[Any]    ='''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							( __lowerCAmelCase ,   __lowerCAmelCase      )			->     Any:
     # "extended trapezoidal rule"
     # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
     UpperCamelCase__      :       int	    =  (boundary[1] - boundary[0]) / steps
     UpperCamelCase__      :       Optional[Any]	    =  boundary[0]
     UpperCamelCase__      :       List[Any]	    =  boundary[1]
     UpperCamelCase__      :       List[Any]	    =  make_points(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )
     UpperCamelCase__      :       int	    =  0.0
     y += (h / 2.0) * f(__lowerCAmelCase      )
     for i in x_i:
          # print(i)
          y += h * f(__lowerCAmelCase      )
     y += (h / 2.0) * f(__lowerCAmelCase      )
     return y







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase      )			->     List[str]:
     UpperCamelCase__      :       Optional[int]	    =  a + h
     while x < (b - h):
          yield x
          UpperCamelCase__      :       Union[str, Any]	    =  x + h







def 		SCREAMING_SNAKE_CASE							( __lowerCAmelCase      )			->     Any:  # enter your function here
     UpperCamelCase__      :       Dict	    =  (x - 0) * (x - 0)
     return y







def 		SCREAMING_SNAKE_CASE							( )			->     Dict:
     UpperCamelCase__      :       List[Any]	    =  0.0  # Lower bound of integration
     UpperCamelCase__      :       Tuple	    =  1.0  # Upper bound of integration
     UpperCamelCase__      :       Any	    =  1_0.0  # define number of steps or resolution
     UpperCamelCase__      :       List[str]	    =  [a, b]  # define boundary of integration
     UpperCamelCase__      :       Any	    =  method_a(__lowerCAmelCase ,   __lowerCAmelCase      )
     print(f'y = {y}'      )


if __name__ == "__main__":
 main()