






# 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

UpperCamelCase        =				"facebook/wmt19-en-de"
UpperCamelCase        =				FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
UpperCamelCase        =				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,
    )
)

UpperCamelCase        =				FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
UpperCamelCase        =				tokenizer(["Making tiny model"], return_tensors="pt")
UpperCamelCase        =				tiny_model(**batch)

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

# Save
UpperCamelCase        =				"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 ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
else:
  from .modeling_text_unet import UNetFlatConditionModel
  from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
  from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
  from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
  from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline

