

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__A			:					Union[str, Any]						=			logging.get_logger(__name__)





def 	__a     (      A__			:  str			,  A__			:  Optional[Any]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForSequenceClassification.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["projector.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["projector.bias"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.post_net.linear.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.post_net.linear.bias"]
    return model





def 	__a     (      A__			:  Dict			,  A__			:  Dict			,  A__			:  List[Any]       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForAudioFrameClassification.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.linear.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.linear.bias"]
    return model





def 	__a     (      A__			:  List[Any]			,  A__			:  str			,  A__			:  Tuple       ):
    SCREAMING_SNAKE_CASE										=   UniSpeechSatForXVector.from_pretrained(A__			,  config=A__       )
    SCREAMING_SNAKE_CASE										=   downstream_dict["connector.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["connector.bias"]
    for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
        SCREAMING_SNAKE_CASE										=   downstream_dict[
            F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        SCREAMING_SNAKE_CASE										=   downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

    SCREAMING_SNAKE_CASE										=   downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
    SCREAMING_SNAKE_CASE										=   downstream_dict["objective.W"]
    return model





@torch.no_grad()
def 	__a     (      A__			:  Union[str, Any]			,  A__			:  Any			,  A__			:  Dict			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   torch.load(A__			,  map_location="cpu"       )

    SCREAMING_SNAKE_CASE										=   checkpoint["Downstream"]

    SCREAMING_SNAKE_CASE										=   UniSpeechSatConfig.from_pretrained(A__       )
    SCREAMING_SNAKE_CASE										=   WavaVecaFeatureExtractor.from_pretrained(
        A__			,  return_attention_mask=A__			,  do_normalize=A__       )

    SCREAMING_SNAKE_CASE										=   hf_config.architectures[0]
    if arch.endswith("ForSequenceClassification"       ):
        SCREAMING_SNAKE_CASE										=   convert_classification(A__			,  A__			,  A__       )
    elif arch.endswith("ForAudioFrameClassification"       ):
        SCREAMING_SNAKE_CASE										=   convert_diarization(A__			,  A__			,  A__       )
    elif arch.endswith("ForXVector"       ):
        SCREAMING_SNAKE_CASE										=   convert_xvector(A__			,  A__			,  A__       )
    else:
        raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"       )

    if hf_config.use_weighted_layer_sum:
        SCREAMING_SNAKE_CASE										=   checkpoint["Featurizer"]["weights"]

    hf_feature_extractor.save_pretrained(A__       )
    hf_model.save_pretrained(A__       )


if __name__ == "__main__":
    __A			:					Dict						=			argparse.ArgumentParser()
    parser.add_argument(
        '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
    )
    parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
    parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
    __A			:					List[str]						=			parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[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 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )