





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__A    :   str          =       logging.get_logger(__name__)

__A    :   List[str]          =       ["UtteranceLevel"]





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    torch.load(UpperCamelCase__      ,      map_location='''cpu'''			)
			if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
						raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""			)

			UpperCAmelCase       		=    checkpoint['''Downstream''']

			UpperCAmelCase       		=    HubertConfig.from_pretrained(UpperCamelCase__			)
			UpperCAmelCase       		=    HubertForSequenceClassification.from_pretrained(UpperCamelCase__      ,      config=UpperCamelCase__			)
			UpperCAmelCase       		=    WavaVecaFeatureExtractor.from_pretrained(
			    UpperCamelCase__      ,      return_attention_mask=UpperCamelCase__      ,      do_normalize=UpperCamelCase__			)

			if hf_congfig.use_weighted_layer_sum:
						UpperCAmelCase       		=    checkpoint['''Featurizer''']['''weights''']

			UpperCAmelCase       		=    downstream_dict['''projector.weight''']
			UpperCAmelCase       		=    downstream_dict['''projector.bias''']
			UpperCAmelCase       		=    downstream_dict['''model.post_net.linear.weight''']
			UpperCAmelCase       		=    downstream_dict['''model.post_net.linear.bias''']

			hf_feature_extractor.save_pretrained(UpperCamelCase__			)
			hf_model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				__A    :   Optional[Any]          =       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    :   Tuple          =       parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_utf16_encoded.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''      ,      encoding='''utf-16'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts_field.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}






@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			),
            },
        ),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}



