
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

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

SCREAMING_SNAKE_CASE__  :     str   				=       FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

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

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

# Save
SCREAMING_SNAKE_CASE__  :     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









'''simple docstring'''







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
lowercase      :   List[str]      =       """."""


if __name__ == "__main__":
      lowercase      :   List[str]      =       os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
      lowercase      :   Union[str, Any]      =       []
      lowercase      :   List[str]      =       []
      with open(doctest_file_path) as fp:
            for line in fp:
                  lowercase      :   List[str]      =       line.strip()
                  lowercase      :   Optional[Any]      =       os.path.join(REPO_PATH, line)
                  if not (os.path.isfile(path) or os.path.isdir(path)):
                        non_existent_paths.append(line)
                  all_paths.append(path)
      if len(non_existent_paths) > 0:
            lowercase      :   int      =       """\n""".join(non_existent_paths)
            raise ValueError(F"""`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}""")
      if all_paths != sorted(all_paths):
            raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")



