


from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				HfArgumentParser(InitializationArguments)
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
SCREAMING_SNAKE_CASE :   Any							      =				AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
SCREAMING_SNAKE_CASE :   Optional[int]							      =				{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
SCREAMING_SNAKE_CASE :   Tuple							      =				AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)






import pytest


SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"__dummy_dataset1__"

SCREAMING_SNAKE_CASE :   int							      =				"\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"



@pytest.fixture
def        UpperCamelCase_(   )					->		Dict:
    return DATASET_LOADING_SCRIPT_NAME



@pytest.fixture
def        UpperCamelCase_(   )					->		List[Any]:
    return DATASET_LOADING_SCRIPT_CODE



@pytest.fixture
def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		int:
    _lowercase :							List[str]   	=	dataset_loading_script_name
    _lowercase :							Union[str, Any]   	=	tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=lowerCamelCase_				)
    _lowercase :							Optional[int]   	=	script_dir / F'''{script_name}.py'''
    with open(lowerCamelCase_							,				'w'				) as f:
        f.write(lowerCamelCase_				)
    return str(lowerCamelCase_				)


