






'''simple docstring'''





# 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

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

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

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

# Save
__A							:							List[str]  							=		"""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









'''simple docstring'''





from __future__ import annotations






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     list(range(len(lowercase__)))
 lowerCamelCase__  =     [v / w for v, w in zip(lowercase__				,     lowercase__)]
 index.sort(key=lambda lowercase__: ratio[i]				,     reverse=lowercase__)

 lowerCamelCase__  =     0
 lowerCamelCase__  =     [0] * len(lowercase__)
 for i in index:
  if weight[i] <= capacity:
   lowerCamelCase__  =     1
   max_value += value[i]
   capacity -= weight[i]
  else:
   lowerCamelCase__  =     capacity / weight[i]
   max_value += value[i] * capacity / weight[i]
   break

 return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()

