





import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					assert isinstance(UpperCamelCase__							,     UpperCamelCase__    )
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'							,     [False, True]    )
def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							List[str]    )      ->			Union[str, Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_snake_case						=					CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__							,     keep_in_memory=UpperCamelCase__    ).read()
					_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__    )





@pytest.mark.parametrize(
    'features'							,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]							,     )
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							Tuple    )      ->			Union[str, Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					_snake_case						=					features.copy() if features else default_expected_features
					_snake_case						=					(
					    Features({feature: Value(UpperCamelCase__    ) for feature, dtype in features.items()}    ) if features is not None else None
					)
					_snake_case						=					CsvDatasetReader(UpperCamelCase__							,     features=UpperCamelCase__							,     cache_dir=UpperCamelCase__    ).read()
					_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__    )





@pytest.mark.parametrize('split'							,     [None, NamedSplit('train'    ), 'train', 'test']    )
def     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							Dict    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					_snake_case						=					CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__							,     split=UpperCamelCase__    ).read()
					_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__    )
					assert dataset.split == split if split else "train"





@pytest.mark.parametrize('path_type'							,     [str, list]    )
def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Dict    )      ->			Union[str, Any]:



					'''simple docstring'''




					if issubclass(UpperCamelCase__							,     UpperCamelCase__    ):
										_snake_case						=					csv_path
					elif issubclass(UpperCamelCase__							,     UpperCamelCase__    ):
										_snake_case						=					[csv_path]
					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					_snake_case						=					CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__    ).read()
					_check_csv_dataset(UpperCamelCase__							,     UpperCamelCase__    )





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Any=("train",)    )      ->			Any:



					'''simple docstring'''




					assert isinstance(UpperCamelCase__							,     UpperCamelCase__    )
					for split in splits:
										_snake_case						=					dataset_dict[split]
										assert dataset.num_rows == 4
										assert dataset.num_columns == 3
										assert dataset.column_names == ["col_1", "col_2", "col_3"]
										for feature, expected_dtype in expected_features.items():
															assert dataset.features[feature].dtype == expected_dtype





@pytest.mark.parametrize('keep_in_memory'							,     [False, True]    )
def     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							int							,     UpperCamelCase__      :							Tuple    )      ->			Union[str, Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										_snake_case						=					CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__							,     keep_in_memory=UpperCamelCase__    ).read()
					_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__    )





@pytest.mark.parametrize(
    'features'							,     [
        None,
        {'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
        {'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
        {'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
        {'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
    ]							,     )
def     lowerCamelCase__  (  UpperCamelCase__      :							str							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							Any    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					_snake_case						=					features.copy() if features else default_expected_features
					_snake_case						=					(
					    Features({feature: Value(UpperCamelCase__    ) for feature, dtype in features.items()}    ) if features is not None else None
					)
					_snake_case						=					CsvDatasetReader({'train': csv_path}							,     features=UpperCamelCase__							,     cache_dir=UpperCamelCase__    ).read()
					_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__    )





@pytest.mark.parametrize('split'							,     [None, NamedSplit('train'    ), 'train', 'test']    )
def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							Optional[int]							,     UpperCamelCase__      :							Tuple    )      ->			int:



					'''simple docstring'''




					if split:
										_snake_case						=					{split: csv_path}
					else:
										_snake_case						=					{'train': csv_path, 'test': csv_path}
					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
					_snake_case						=					CsvDatasetReader(UpperCamelCase__							,     cache_dir=UpperCamelCase__    ).read()
					_check_csv_datasetdict(UpperCamelCase__							,     UpperCamelCase__							,     splits=list(path.keys()    )    )
					assert all(dataset[split].split == split for split in path.keys()    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]    )      ->			int:



					'''simple docstring'''




					with open(UpperCamelCase__							,     encoding='utf-8'    ) as csvfile:
										yield from csv.reader(UpperCamelCase__    )





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Tuple    )      ->			List[Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					os.path.join(UpperCamelCase__							,     'tmp.csv'    )
					_snake_case						=					CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__    ).read()
					CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=1    ).write()

					_snake_case						=					iter_csv_file(UpperCamelCase__    )
					_snake_case						=					iter_csv_file(UpperCamelCase__    )

					for rowa, rowa in zip(UpperCamelCase__							,     UpperCamelCase__    ):
										assert rowa == rowa





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					os.path.join(UpperCamelCase__							,     'tmp.csv'    )
					_snake_case						=					CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__    ).read()
					CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=2    ).write()

					_snake_case						=					iter_csv_file(UpperCamelCase__    )
					_snake_case						=					iter_csv_file(UpperCamelCase__    )

					for rowa, rowa in zip(UpperCamelCase__							,     UpperCamelCase__    ):
										assert rowa == rowa





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							List[Any]    )      ->			Union[str, Any]:



					'''simple docstring'''




					_snake_case						=					tmp_path / 'cache'
					_snake_case						=					os.path.join(UpperCamelCase__							,     'tmp.csv'    )
					_snake_case						=					CsvDatasetReader({'train': csv_path}							,     cache_dir=UpperCamelCase__    ).read()
					with pytest.raises(UpperCamelCase__    ):
										CsvDatasetWriter(dataset['train']							,     UpperCamelCase__							,     num_proc=0    )










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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





def     lowerCamelCase__  (  UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Optional[Any]    )      ->			Optional[int]:



					'''simple docstring'''




					_snake_case						=					WavLMForSequenceClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['projector.weight']
					_snake_case						=					downstream_dict['projector.bias']
					_snake_case						=					downstream_dict['model.post_net.linear.weight']
					_snake_case						=					downstream_dict['model.post_net.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Optional[Any]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							List[Any]    )      ->			Optional[Any]:



					'''simple docstring'''




					_snake_case						=					WavLMForAudioFrameClassification.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['model.linear.weight']
					_snake_case						=					downstream_dict['model.linear.bias']
					return model





def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]							,     UpperCamelCase__      :							Dict    )      ->			Tuple:



					'''simple docstring'''




					_snake_case						=					WavLMForXVector.from_pretrained(UpperCamelCase__							,     config=UpperCamelCase__    )
					_snake_case						=					downstream_dict['connector.weight']
					_snake_case						=					downstream_dict['connector.bias']
					for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
										_snake_case						=					downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										_snake_case						=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
					_snake_case						=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
					_snake_case						=					downstream_dict['objective.W']
					return model





@torch.no_grad()
def     lowerCamelCase__  (  UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							Union[str, Any]							,     UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							Optional[int]    )      ->			int:



					'''simple docstring'''




					_snake_case						=					torch.load(UpperCamelCase__							,     map_location='cpu'    )

					_snake_case						=					checkpoint['Downstream']

					_snake_case						=					WavLMConfig.from_pretrained(UpperCamelCase__    )
					_snake_case						=					WavaVecaFeatureExtractor.from_pretrained(
					    UpperCamelCase__							,     return_attention_mask=UpperCamelCase__							,     do_normalize=UpperCamelCase__    )

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

					if hf_config.use_weighted_layer_sum:
										_snake_case						=					checkpoint['Featurizer']['weights']

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


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



