



from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

a__      :				Union[str, Any]   	=			"facebook/wmt19-en-de"
a__      :				List[Any]   	=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
a__      :				Optional[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,
    )
)

a__      :				List[Any]   	=			FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
a__      :				Optional[Any]   	=			tokenizer(["Making tiny model"], return_tensors="pt")
a__      :				Tuple   	=			tiny_model(**batch)

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

# Save
a__      :				Optional[int]   	=			"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   _lowerCAmelCase   (						A__						,  A__						,  A__	):
      if principal <= 0:
            raise Exception('Principal borrowed must be > 0'	)
      if rate_per_annum < 0:
            raise Exception('Rate of interest must be >= 0'	)
      if years_to_repay <= 0 or not isinstance(A__						,  A__	):
            raise Exception('Years to repay must be an integer > 0'	)

      # Yearly rate is divided by 12 to get monthly rate
      lowercase__     =  rate_per_annum / 12

      # Years to repay is multiplied by 12 to get number of payments as payment is monthly
      lowercase__     =  years_to_repay * 12

      return (
          principal
          * rate_per_month
          * (1 + rate_per_month) ** number_of_payments
          / ((1 + rate_per_month) ** number_of_payments - 1)
      )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
