
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 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




  assert isinstance(_a		,				_a    )
  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 	_a  (					__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  str    ):






  """simple docstring"""




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



@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 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Optional[int]    ):






  """simple docstring"""




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



@pytest.mark.parametrize('split'		,				[None, NamedSplit('train'    ), 'train', 'test']    )
def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  Dict		,				__SCREAMING_SNAKE_CASE					:  Tuple    ):






  """simple docstring"""




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



@pytest.mark.parametrize('path_type'		,				[str, list]    )
def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Dict    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  List[str]=("train",)    ):






  """simple docstring"""




  assert isinstance(_a		,				_a    )
  for split in splits:
    _lowerCAmelCase						=		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 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]    ):






  """simple docstring"""




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



@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 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  Optional[int]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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



@pytest.mark.parametrize('split'		,				[None, NamedSplit('train'    ), 'train', 'test']    )
def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  List[str]		,				__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]    ):






  """simple docstring"""




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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  Optional[Any]		,				__SCREAMING_SNAKE_CASE					:  int    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		iter_csv_file(_a    )
  _lowerCAmelCase						=		iter_csv_file(_a    )

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



def 	_a  (					__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Dict    ):






  """simple docstring"""




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

  _lowerCAmelCase						=		iter_csv_file(_a    )
  _lowerCAmelCase						=		iter_csv_file(_a    )

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




def 	_a  (					__SCREAMING_SNAKE_CASE					:  Any		,				__SCREAMING_SNAKE_CASE					:  Any    ):






  """simple docstring"""




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


'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            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:
            snake_case_    							=				checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_a      )
      hf_model.save_pretrained(_a      )


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)

