

"""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 							_lowerCAmelCase   (    UpperCAmelCase  :				Tuple					):


					'''simple docstring'''






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



@pytest.fixture
def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






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



@pytest.fixture
def 							_lowerCAmelCase   (    UpperCAmelCase  :				Union[str, Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     int								=tmp_path / '''file_with_list_of_dicts.json'''
					UpperCamelCase__		:     Any								=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(lowerCamelCase__   ,				'''w'''					) as f:
										f.write(lowerCamelCase__					)
					return str(lowerCamelCase__					)



@pytest.fixture
def 							_lowerCAmelCase   (    UpperCAmelCase  :				Any					):


					'''simple docstring'''






					UpperCamelCase__		:     Optional[int]								=tmp_path / '''file_with_list_of_dicts_field.json'''
					UpperCamelCase__		:     Optional[Any]								=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(lowerCamelCase__   ,				'''w'''					) as f:
										f.write(lowerCamelCase__					)
					return str(lowerCamelCase__					)



@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 							_lowerCAmelCase   (    UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[str]   ,				UpperCAmelCase  :				List[Any]					):


					'''simple docstring'''






					UpperCamelCase__		:     Dict								=Json(**lowerCamelCase__					)
					UpperCamelCase__		:     Dict								=json._generate_tables([[request.getfixturevalue(lowerCamelCase__					)]]					)
					UpperCamelCase__		:     Union[str, Any]								=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 							_lowerCAmelCase   (    UpperCAmelCase  :				str   ,				UpperCAmelCase  :				Union[str, Any]   ,				UpperCAmelCase  :				List[str]					):


					'''simple docstring'''






					UpperCamelCase__		:     Any								=Json(**lowerCamelCase__					)
					UpperCamelCase__		:     List[Any]								=json._generate_tables([[request.getfixturevalue(lowerCamelCase__					)]]					)
					UpperCamelCase__		:     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]}



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




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

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

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

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


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

