

'''simple docstring'''

import textwrap

import pyarrow as pa
import pytest

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





@pytest.fixture
def 				A_					(	snake_case	):
		SCREAMING_SNAKE_CASE:Tuple   =   tmp_path / "file.jsonl"
		SCREAMING_SNAKE_CASE:int   =   textwrap.dedent(
		    "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        "	)
		with open(snake_case ,    "w"	) as f:
				f.write(snake_case	)
		return str(snake_case	)





@pytest.fixture
def 				A_					(	snake_case	):
		SCREAMING_SNAKE_CASE:Tuple   =   tmp_path / "file_utf16_encoded.jsonl"
		SCREAMING_SNAKE_CASE:Any   =   textwrap.dedent(
		    "\\n        {\"col_1\": -1}\n        {\"col_1\": 1, \"col_2\": 2}\n        {\"col_1\": 10, \"col_2\": 20}\n        "	)
		with open(snake_case ,    "w" ,    encoding="utf-16"	) as f:
				f.write(snake_case	)
		return str(snake_case	)





@pytest.fixture
def 				A_					(	snake_case	):
		SCREAMING_SNAKE_CASE:str   =   tmp_path / "file_with_list_of_dicts.json"
		SCREAMING_SNAKE_CASE:Dict   =   textwrap.dedent(
		    "\\n        [\n            {\"col_1\": -1},\n            {\"col_1\": 1, \"col_2\": 2},\n            {\"col_1\": 10, \"col_2\": 20}\n        ]\n        "	)
		with open(snake_case ,    "w"	) as f:
				f.write(snake_case	)
		return str(snake_case	)





@pytest.fixture
def 				A_					(	snake_case	):
		SCREAMING_SNAKE_CASE:int   =   tmp_path / "file_with_list_of_dicts_field.json"
		SCREAMING_SNAKE_CASE:int   =   textwrap.dedent(
		    "\\n        {\n            \"field1\": 1,\n            \"field2\": \"aabb\",\n            \"field3\": [\n                {\"col_1\": -1},\n                {\"col_1\": 1, \"col_2\": 2},\n                {\"col_1\": 10, \"col_2\": 20}\n            ]\n        }\n        "	)
		with open(snake_case ,    "w"	) as f:
				f.write(snake_case	)
		return str(snake_case	)





@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 				A_					(	snake_case ,    snake_case ,    snake_case	):
		SCREAMING_SNAKE_CASE:Any   =   Json(**snake_case	)
		SCREAMING_SNAKE_CASE:str   =   json._generate_tables([[request.getfixturevalue(snake_case	)]]	)
		SCREAMING_SNAKE_CASE:Dict   =   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 				A_					(	snake_case ,    snake_case ,    snake_case	):
		SCREAMING_SNAKE_CASE:List[str]   =   Json(**snake_case	)
		SCREAMING_SNAKE_CASE:str   =   json._generate_tables([[request.getfixturevalue(snake_case	)]]	)
		SCREAMING_SNAKE_CASE:List[str]   =   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]}









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_      =							logging.get_logger(__name__)

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

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

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


if __name__ == "__main__":
    A_      =							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_      =							parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






