

"""simple docstring"""






import math

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


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       '''data2vec-audio'''







     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_="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_=16 ,						UpperCamelCase_=19 ,						UpperCamelCase_=5 ,						UpperCamelCase_=0.0_5 ,						UpperCamelCase_=10 ,						UpperCamelCase_=2 ,						UpperCamelCase_=0.0 ,						UpperCamelCase_=10 ,						UpperCamelCase_=0 ,						UpperCamelCase_="sum" ,						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_=0 ,						UpperCamelCase_=1 ,						UpperCamelCase_=2 ,						UpperCamelCase_=False ,						UpperCamelCase_=3 ,						UpperCamelCase_=2 ,						UpperCamelCase_=3 ,						UpperCamelCase_=None ,						**UpperCamelCase_ ,						):
      super().__init__(**UpperCamelCase_ ,						pad_token_id=UpperCamelCase_ ,						bos_token_id=UpperCamelCase_ ,						eos_token_id=UpperCamelCase_       )
      __magic_name__     =      hidden_size
      __magic_name__     =      feat_extract_activation
      __magic_name__     =      list(UpperCamelCase_       )
      __magic_name__     =      list(UpperCamelCase_       )
      __magic_name__     =      list(UpperCamelCase_       )
      __magic_name__     =      conv_bias
      __magic_name__     =      num_conv_pos_embeddings
      __magic_name__     =      num_conv_pos_embedding_groups
      __magic_name__     =      conv_pos_kernel_size
      __magic_name__     =      len(self.conv_dim       )
      __magic_name__     =      num_hidden_layers
      __magic_name__     =      intermediate_size
      __magic_name__     =      hidden_act
      __magic_name__     =      num_attention_heads
      __magic_name__     =      hidden_dropout
      __magic_name__     =      attention_dropout
      __magic_name__     =      activation_dropout
      __magic_name__     =      feat_proj_dropout
      __magic_name__     =      final_dropout
      __magic_name__     =      layerdrop
      __magic_name__     =      layer_norm_eps
      __magic_name__     =      initializer_range
      __magic_name__     =      vocab_size
      __magic_name__     =      use_weighted_layer_sum

      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
      __magic_name__     =      mask_time_prob
      __magic_name__     =      mask_time_length
      __magic_name__     =      mask_time_min_masks
      __magic_name__     =      mask_feature_prob
      __magic_name__     =      mask_feature_length
      __magic_name__     =      mask_feature_min_masks

      # ctc loss
      __magic_name__     =      ctc_loss_reduction
      __magic_name__     =      ctc_zero_infinity

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

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

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







     @property
     def 	lowerCAmelCase__    (      self       ):
      return math.prod(self.conv_stride       )




"""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 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      List[str]:
 __magic_name__     =      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}}'''						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42





@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=42      ,   ),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_info.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfo.from_directory(__UpperCamelCase						)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(__UpperCamelCase      ,   '''dataset_info.json'''						)						)





def 	lowercase      (   ) ->      Any:
 __magic_name__     =      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''': 42}]      ,   download_checksums={}      ,   download_size=1337      ,   post_processing_size=442      ,   dataset_size=1234      ,   size_in_bytes=1337 + 442 + 1234      ,   )
 __magic_name__     =      dataset_info._to_yaml_dict()
 assert sorted(__UpperCamelCase						) == 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)						)
 __magic_name__     =      yaml.safe_dump(__UpperCamelCase						)
 __magic_name__     =      yaml.safe_load(__UpperCamelCase						)
 assert dataset_info_yaml_dict == reloaded





def 	lowercase      (   ) ->      str:
 __magic_name__     =      DatasetInfo()
 __magic_name__     =      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=42      ,   )
            }						),
        DatasetInfosDict(
            {
                '''v1''': DatasetInfo(dataset_size=42						),
                '''v2''': DatasetInfo(dataset_size=1337						),
            }						),
    ]      ,   )
def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase						) ->      str:
 __magic_name__     =      str(__UpperCamelCase						)
 dataset_infos_dict.write_to_directory(__UpperCamelCase						)
 __magic_name__     =      DatasetInfosDict.from_directory(__UpperCamelCase						)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  __magic_name__     =      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
  __magic_name__     =      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(__UpperCamelCase      ,   '''README.md'''						)						)

