
'''simple docstring'''
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






"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A											=						{}


try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						["""MLukeTokenizer"""]

if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mluke import MLukeTokenizer


else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
