


"""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

__lowerCamelCase      :Tuple	    =		'facebook/wmt19-en-de'
__lowerCamelCase      :Any	    =		FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
__lowerCamelCase      :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,
    )
)

__lowerCamelCase      :Union[str, Any]	    =		FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__lowerCamelCase      :Optional[Any]	    =		tokenizer(['Making tiny model'], return_tensors='pt')
__lowerCamelCase      :List[Any]	    =		tiny_model(**batch)

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

# Save
__lowerCamelCase      :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 typing import TYPE_CHECKING

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


__lowerCamelCase      :Optional[Any]	    =		{
    '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      :Union[str, Any]	    =		[
        '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      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




