






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def      _snake_case     ():
   UpperCamelCase_						=  HfArgumentParser(__lowercase)
   UpperCamelCase_						=  parser.parse_args_into_dataclasses()[0]
   UpperCamelCase_						=  TensorFlowBenchmark(args=__lowercase)
   try:
      UpperCamelCase_						=  parser.parse_args_into_dataclasses()[0]
   except ValueError as e:
      UpperCamelCase_						=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
      UpperCamelCase_						=  ' '.join(str(__lowercase).split(' ')[:-1])
      UpperCamelCase_						=  ''
      UpperCamelCase_						=  eval(str(__lowercase).split(' ')[-1])
      UpperCamelCase_						=  []
      for arg in depreciated_args:
         # arg[2:] removes '--'
         if arg[2:] in TensorFlowBenchmark.deprecated_args:
            # arg[5:] removes '--no_'
            full_error_msg += arg_error_msg.format(arg[5:])
         else:
            wrong_args.append(__lowercase)
      if len(__lowercase) > 0:
         UpperCamelCase_						=  full_error_msg + begin_error_msg + str(__lowercase)
      raise ValueError(__lowercase)
   benchmark.run()


if __name__ == "__main__":
 main()







"""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     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[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     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

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

# Save
_A     :			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


