
"""simple docstring"""

import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict






@pytest.mark.parametrize(
    'files'    , [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]    , )
def 							A						(    snake_case				:str    , snake_case				:Optional[int]		)				->     Optional[int]:
 __UpperCamelCase      =	tmp_path_factory.mktemp('dset_infos_dir'		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / 'README.md'    , 'w'		) as f:
   f.write('---\ndataset_info:\n  dataset_size: 42\n---'		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / 'README.md'    , 'w'		) as f:
   f.write(''		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / 'dataset_infos.json'    , 'w'		) as f:
   f.write('{"default": {"dataset_size": 42}}'		)
 __UpperCamelCase      =	DatasetInfosDict.from_directory(snake_case		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 4_2






@pytest.mark.parametrize(
    'dataset_info'    , [
        DatasetInfo(),
        DatasetInfo(
            description='foo'    , features=Features({'a': Value('int32'		)}		)    , builder_name='builder'    , config_name='config'    , version='1.0.0'    , splits=[{'name': 'train'}]    , download_size=4_2    , ),
    ]    , )
def 							A						(    snake_case				:List[Any]    , snake_case				:DatasetInfo		)				->     Union[str, Any]:
 __UpperCamelCase      =	str(snake_case		)
 dataset_info.write_to_directory(snake_case		)
 __UpperCamelCase      =	DatasetInfo.from_directory(snake_case		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(snake_case    , 'dataset_info.json'		)		)






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	DatasetInfo(
     description='foo'    , citation='bar'    , homepage='https://foo.bar'    , license='CC0'    , features=Features({'a': Value('int32'		)}		)    , post_processed={}    , supervised_keys=()    , task_templates=[]    , builder_name='builder'    , config_name='config'    , version='1.0.0'    , splits=[{'name': 'train', 'num_examples': 4_2}]    , download_checksums={}    , download_size=1_3_3_7    , post_processing_size=4_4_2    , dataset_size=1_2_3_4    , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4    , )
 __UpperCamelCase      =	dataset_info._to_yaml_dict()
 assert sorted(snake_case		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    , (list, dict, int, str)		)
 __UpperCamelCase      =	yaml.safe_dump(snake_case		)
 __UpperCamelCase      =	yaml.safe_load(snake_case		)
 assert dataset_info_yaml_dict == reloaded






def 							A						(    )				->     List[str]:
 __UpperCamelCase      =	DatasetInfo()
 __UpperCamelCase      =	dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    'dataset_infos_dict'    , [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}		),
        DatasetInfosDict({'my_config_name': DatasetInfo()}		),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'    , features=Features({'a': Value('int32'		)}		)    , builder_name='builder'    , config_name='config'    , version='1.0.0'    , splits=[{'name': 'train'}]    , download_size=4_2    , )
            }		),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=4_2		),
                'v2': DatasetInfo(dataset_size=1_3_3_7		),
            }		),
    ]    , )
def 							A						(    snake_case				:Optional[Any]    , snake_case				:DatasetInfosDict		)				->     List[str]:
 __UpperCamelCase      =	str(snake_case		)
 dataset_infos_dict.write_to_directory(snake_case		)
 __UpperCamelCase      =	DatasetInfosDict.from_directory(snake_case		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  __UpperCamelCase      =	config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  __UpperCamelCase      =	DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(snake_case    , 'README.md'		)		)





"""simple docstring"""

import functools
import operator

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


UpperCamelCase     :    str       =			logging.get_logger(__name__)

UpperCamelCase     :    Dict       =			{
    "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		(				__SCREAMING_SNAKE_CASE      ):
     lowercase						       =						"wavlm"





     def __init__(     self     ,					__UpperCAmelCase=32     ,					__UpperCAmelCase=768     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=12     ,					__UpperCAmelCase=3072     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=0.0_2     ,					__UpperCAmelCase=1E-5     ,					__UpperCAmelCase="group"     ,					__UpperCAmelCase="gelu"     ,					__UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)     ,					__UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)     ,					__UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=128     ,					__UpperCAmelCase=16     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=800     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=0.0_5     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.0     ,					__UpperCAmelCase=10     ,					__UpperCAmelCase=320     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase=100     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=0.1     ,					__UpperCAmelCase="mean"     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=256     ,					__UpperCAmelCase=(512, 512, 512, 512, 1500)     ,					__UpperCAmelCase=(5, 3, 3, 1, 1)     ,					__UpperCAmelCase=(1, 2, 3, 1, 1)     ,					__UpperCAmelCase=512     ,					__UpperCAmelCase=80     ,					__UpperCAmelCase=0     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=False     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=2     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=None     ,					**__UpperCAmelCase     ,					):

      '''simple docstring'''




      super().__init__(**__UpperCAmelCase     ,					pad_token_id=__UpperCAmelCase     ,					bos_token_id=__UpperCAmelCase     ,					eos_token_id=__UpperCAmelCase				)
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	feat_extract_norm
      __UpperCamelCase      =	feat_extract_activation
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	list(__UpperCAmelCase				)
      __UpperCamelCase      =	conv_bias
      __UpperCamelCase      =	num_buckets
      __UpperCamelCase      =	max_bucket_distance
      __UpperCamelCase      =	num_conv_pos_embeddings
      __UpperCamelCase      =	num_conv_pos_embedding_groups
      __UpperCamelCase      =	len(self.conv_dim				)
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	hidden_dropout
      __UpperCamelCase      =	attention_dropout
      __UpperCamelCase      =	activation_dropout
      __UpperCamelCase      =	feat_proj_dropout
      __UpperCamelCase      =	final_dropout
      __UpperCamelCase      =	layerdrop
      __UpperCamelCase      =	layer_norm_eps
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	num_ctc_classes
      __UpperCamelCase      =	vocab_size
      __UpperCamelCase      =	do_stable_layer_norm
      __UpperCamelCase      =	use_weighted_layer_sum
      __UpperCamelCase      =	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
      __UpperCamelCase      =	apply_spec_augment
      __UpperCamelCase      =	mask_time_prob
      __UpperCamelCase      =	mask_time_length
      __UpperCamelCase      =	mask_time_min_masks
      __UpperCamelCase      =	mask_feature_prob
      __UpperCamelCase      =	mask_feature_length

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

      # ctc loss
      __UpperCamelCase      =	ctc_loss_reduction
      __UpperCamelCase      =	ctc_zero_infinity

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

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

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







     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return functools.reduce(operator.mul     ,					self.conv_stride     ,					1				)



