





# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__UpperCAmelCase	:       List[Any]    	=							"tiny-wmt19-en-ru"

# Build

# borrowed from a test
__UpperCAmelCase	:       List[Any]    	=							[
    "l",
    "o",
    "w",
    "e",
    "r",
    "s",
    "t",
    "i",
    "d",
    "n",
    "w</w>",
    "r</w>",
    "t</w>",
    "lo",
    "low",
    "er</w>",
    "low</w>",
    "lowest</w>",
    "newer</w>",
    "wider</w>",
    "<unk>",
]
__UpperCAmelCase	:       str    	=							dict(zip(vocab, range(len(vocab))))
__UpperCAmelCase	:       Tuple    	=							["l o 123", "lo w 1456", "e r</w> 1789", ""]

with tempfile.TemporaryDirectory() as tmpdirname:
      __UpperCAmelCase	:       Optional[Any]    	=							Path(tmpdirname)
      __UpperCAmelCase	:       List[Any]    	=							build_dir / VOCAB_FILES_NAMES["src_vocab_file"]
      __UpperCAmelCase	:       str    	=							build_dir / VOCAB_FILES_NAMES["tgt_vocab_file"]
      __UpperCAmelCase	:       int    	=							build_dir / VOCAB_FILES_NAMES["merges_file"]
      with open(src_vocab_file, "w") as fp:
            fp.write(json.dumps(vocab_tokens))
      with open(tgt_vocab_file, "w") as fp:
            fp.write(json.dumps(vocab_tokens))
      with open(merges_file, "w") as fp:
            fp.write("\n".join(merges))

      __UpperCAmelCase	:       Optional[int]    	=							FSMTTokenizer(
          langs=["en", "ru"],
          src_vocab_size=len(vocab),
          tgt_vocab_size=len(vocab),
          src_vocab_file=src_vocab_file,
          tgt_vocab_file=tgt_vocab_file,
          merges_file=merges_file,
      )

__UpperCAmelCase	:       Union[str, Any]    	=							FSMTConfig(
    langs=["ru", "en"],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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	:       Tuple    	=							FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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

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

# Save
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-ru













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






