'''simple docstring'''






import pyarrow.parquet as pq
import pytest

from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       Dict							):
   assert isinstance(__snake_case,					__snake_case							)
   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    __UpperCAmelCase			(  a_:       Dict,					a_:       Union[str, Any],					a_:       Optional[int]							):
   _UpperCAmelCase							:	List[str]							=   tmp_path / "cache"
   _UpperCAmelCase							:	List[Any]							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _UpperCAmelCase							:	List[Any]							=   ParquetDatasetReader(__snake_case,					cache_dir=__snake_case,					keep_in_memory=__snake_case							).read()
   _check_parquet_dataset(__snake_case,					__snake_case							)






@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    __UpperCAmelCase			(  a_:       Tuple,					a_:       List[str],					a_:       Any							):
   _UpperCAmelCase							:	int							=   tmp_path / "cache"
   _UpperCAmelCase							:	Optional[Any]							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _UpperCAmelCase							:	Any							=   features.copy() if features else default_expected_features
   _UpperCAmelCase							:	int							=   (
       Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
   )
   _UpperCAmelCase							:	Optional[Any]							=   ParquetDatasetReader(__snake_case,					features=__snake_case,					cache_dir=__snake_case							).read()
   _check_parquet_dataset(__snake_case,					__snake_case							)






@pytest.mark.parametrize("split",					[None, NamedSplit("train"							), "train", "test"]							)
def    __UpperCAmelCase			(  a_:       Union[str, Any],					a_:       str,					a_:       str							):
   _UpperCAmelCase							:	Dict							=   tmp_path / "cache"
   _UpperCAmelCase							:	Tuple							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _UpperCAmelCase							:	str							=   ParquetDatasetReader(__snake_case,					cache_dir=__snake_case,					split=__snake_case							).read()
   _check_parquet_dataset(__snake_case,					__snake_case							)
   assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type",					[str, list]							)
def    __UpperCAmelCase			(  a_:       str,					a_:       Union[str, Any],					a_:       List[str]							):


   if issubclass(__snake_case,					__snake_case							):
      _UpperCAmelCase							:	Optional[Any]							=   parquet_path
   elif issubclass(__snake_case,					__snake_case							):
      _UpperCAmelCase							:	Optional[int]							=   [parquet_path]
   _UpperCAmelCase							:	str							=   tmp_path / "cache"
   _UpperCAmelCase							:	Optional[Any]							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _UpperCAmelCase							:	Dict							=   ParquetDatasetReader(__snake_case,					cache_dir=__snake_case							).read()
   _check_parquet_dataset(__snake_case,					__snake_case							)






def    __UpperCAmelCase			(  a_:       Optional[Any],					a_:       Optional[Any],					a_:       str=("train",)							):
   assert isinstance(__snake_case,					__snake_case							)
   for split in splits:
      _UpperCAmelCase							:	Dict							=   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    __UpperCAmelCase			(  a_:       Tuple,					a_:       List[Any],					a_:       List[Any]							):
   _UpperCAmelCase							:	Tuple							=   tmp_path / "cache"
   _UpperCAmelCase							:	Tuple							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      _UpperCAmelCase							:	Optional[Any]							=   ParquetDatasetReader(
          {"train": parquet_path},					cache_dir=__snake_case,					keep_in_memory=__snake_case							).read()
   _check_parquet_datasetdict(__snake_case,					__snake_case							)






@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    __UpperCAmelCase			(  a_:       List[str],					a_:       Optional[Any],					a_:       List[Any]							):
   _UpperCAmelCase							:	Optional[Any]							=   tmp_path / "cache"
   _UpperCAmelCase							:	str							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _UpperCAmelCase							:	str							=   features.copy() if features else default_expected_features
   _UpperCAmelCase							:	List[Any]							=   (
       Features({feature: Value(__snake_case							) for feature, dtype in features.items()}							) if features is not None else None
   )
   _UpperCAmelCase							:	int							=   ParquetDatasetReader({"train": parquet_path},					features=__snake_case,					cache_dir=__snake_case							).read()
   _check_parquet_datasetdict(__snake_case,					__snake_case							)






@pytest.mark.parametrize("split",					[None, NamedSplit("train"							), "train", "test"]							)
def    __UpperCAmelCase			(  a_:       List[str],					a_:       List[str],					a_:       Tuple							):


   if split:
      _UpperCAmelCase							:	int							=   {split: parquet_path}
   else:
      _UpperCAmelCase							:	Any							=   "train"
      _UpperCAmelCase							:	Optional[Any]							=   {"train": parquet_path, "test": parquet_path}
   _UpperCAmelCase							:	str							=   tmp_path / "cache"
   _UpperCAmelCase							:	Tuple							=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
   _UpperCAmelCase							:	Optional[Any]							=   ParquetDatasetReader(__snake_case,					cache_dir=__snake_case							).read()
   _check_parquet_datasetdict(__snake_case,					__snake_case,					splits=list(path.keys()							)							)
   assert all(dataset[split].split == split for split in path.keys()							)






def    __UpperCAmelCase			(  a_:       List[Any],					a_:       Dict							):
   _UpperCAmelCase							:	Dict							=   ParquetDatasetWriter(__snake_case,					tmp_path / "foo.parquet"							)
   assert writer.write() > 0
   _UpperCAmelCase							:	int							=   pq.ParquetFile(tmp_path / "foo.parquet"							)
   _UpperCAmelCase							:	Optional[Any]							=   pf.read()
   assert dataset.data.table == output_table






def    __UpperCAmelCase			(  a_:       Tuple,					a_:       int							):
   _UpperCAmelCase							:	Optional[Any]							=   str(shared_datadir / "test_image_rgb.jpg"							)
   _UpperCAmelCase							:	Tuple							=   {"image": [image_path]}
   _UpperCAmelCase							:	List[str]							=   Features({"image": Image()}							)
   _UpperCAmelCase							:	Any							=   Dataset.from_dict(__snake_case,					features=__snake_case							)
   _UpperCAmelCase							:	Union[str, Any]							=   ParquetDatasetWriter(__snake_case,					tmp_path / "foo.parquet"							)
   assert writer.write() > 0

   _UpperCAmelCase							:	int							=   Dataset.from_parquet(str(tmp_path / "foo.parquet"							)							)
   assert dataset.features == reloaded_dataset.features

   _UpperCAmelCase							:	str							=   ParquetDatasetReader(str(tmp_path / "foo.parquet"							),					streaming=__snake_case							).read()
   assert dataset.features == reloaded_iterable_dataset.features






@pytest.mark.parametrize(
    "feature, expected",					[
        (Features({"foo": Value("int32"							)}							), None),
        (Features({"image": Image(), "foo": Value("int32"							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
        (Features({"nested": Sequence(Audio()							)}							), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
    ],					)
def    __UpperCAmelCase			(  a_:       Dict,					a_:       Optional[int]							):
   assert get_writer_batch_size(__snake_case							) == expected



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase__   	=							logging.get_logger(__name__)

UpperCAmelCase__   	=							{
    '''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''wavlm'''





     def __init__(self		,     UpperCAmelCase=3_2		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.0		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-5		,     UpperCAmelCase="group"		,     UpperCAmelCase="gelu"		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)		,     UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)		,     UpperCAmelCase=(1_0, 3, 3, 3, 3, 2, 2)		,     UpperCAmelCase=False		,     UpperCAmelCase=1_2_8		,     UpperCAmelCase=1_6		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=8_0_0		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=0.05		,     UpperCAmelCase=1_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.0		,     UpperCAmelCase=1_0		,     UpperCAmelCase=3_2_0		,     UpperCAmelCase=2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_0		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=0.1		,     UpperCAmelCase="mean"		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=2_5_6		,     UpperCAmelCase=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)		,     UpperCAmelCase=(5, 3, 3, 1, 1)		,     UpperCAmelCase=(1, 2, 3, 1, 1)		,     UpperCAmelCase=5_1_2		,     UpperCAmelCase=8_0		,     UpperCAmelCase=0		,     UpperCAmelCase=1		,     UpperCAmelCase=2		,     UpperCAmelCase=False		,     UpperCAmelCase=3		,     UpperCAmelCase=2		,     UpperCAmelCase=3		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Optional[Any]:
      super().__init__(**UpperCAmelCase		,     pad_token_id=UpperCAmelCase		,     bos_token_id=UpperCAmelCase		,     eos_token_id=UpperCAmelCase       )
      _lowercase							      =hidden_size
      _lowercase							      =feat_extract_norm
      _lowercase							      =feat_extract_activation
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =conv_bias
      _lowercase							      =num_buckets
      _lowercase							      =max_bucket_distance
      _lowercase							      =num_conv_pos_embeddings
      _lowercase							      =num_conv_pos_embedding_groups
      _lowercase							      =len(self.conv_dim       )
      _lowercase							      =num_hidden_layers
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_act
      _lowercase							      =num_attention_heads
      _lowercase							      =hidden_dropout
      _lowercase							      =attention_dropout
      _lowercase							      =activation_dropout
      _lowercase							      =feat_proj_dropout
      _lowercase							      =final_dropout
      _lowercase							      =layerdrop
      _lowercase							      =layer_norm_eps
      _lowercase							      =initializer_range
      _lowercase							      =num_ctc_classes
      _lowercase							      =vocab_size
      _lowercase							      =do_stable_layer_norm
      _lowercase							      =use_weighted_layer_sum
      _lowercase							      =classifier_proj_size

      if (
          (len(self.conv_stride       ) != self.num_feat_extract_layers)
          or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
          or (len(self.conv_dim       ) != self.num_feat_extract_layers)
      ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           f" {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,"
           f" `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

      # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
      _lowercase							      =apply_spec_augment
      _lowercase							      =mask_time_prob
      _lowercase							      =mask_time_length
      _lowercase							      =mask_time_min_masks
      _lowercase							      =mask_feature_prob
      _lowercase							      =mask_feature_length

      # parameters for pretraining with codevector quantized representations
      _lowercase							      =num_codevectors_per_group
      _lowercase							      =num_codevector_groups
      _lowercase							      =contrastive_logits_temperature
      _lowercase							      =num_negatives
      _lowercase							      =codevector_dim
      _lowercase							      =proj_codevector_dim
      _lowercase							      =diversity_loss_weight

      # ctc loss
      _lowercase							      =ctc_loss_reduction
      _lowercase							      =ctc_zero_infinity

      # adapter
      _lowercase							      =add_adapter
      _lowercase							      =adapter_kernel_size
      _lowercase							      =adapter_stride
      _lowercase							      =num_adapter_layers
      _lowercase							      =output_hidden_size or hidden_size

      # SequenceClassification-specific parameter. Feel free to ignore for other classes.
      _lowercase							      =classifier_proj_size

      # XVector-specific parameters. Feel free to ignore for other classes.
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =list(UpperCAmelCase       )
      _lowercase							      =xvector_output_dim





     @property
     def      __A					(self       )     ->   int:
      return functools.reduce(operator.mul		,     self.conv_stride		,     1       )

