
# 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

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

lowercase__		:Optional[int]   =    FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

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

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

# Save
lowercase__		:Optional[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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCAmelCase		       =							{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		       =							[
     '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''EncodecModel''',
     '''EncodecPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 __lowerCAmelCase		       =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


