





'''simple docstring'''


import math

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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[int]         =					"data2vec-audio"




 def __init__(	self			,     _lowerCAmelCase=32			,     _lowerCAmelCase=768			,     _lowerCAmelCase=12			,     _lowerCAmelCase=12			,     _lowerCAmelCase=3072			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=1E-5			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)			,     _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)			,     _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)			,     _lowerCAmelCase=False			,     _lowerCAmelCase=16			,     _lowerCAmelCase=19			,     _lowerCAmelCase=5			,     _lowerCAmelCase=0.05			,     _lowerCAmelCase=10			,     _lowerCAmelCase=2			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=10			,     _lowerCAmelCase=0			,     _lowerCAmelCase="sum"			,     _lowerCAmelCase=False			,     _lowerCAmelCase=False			,     _lowerCAmelCase=256			,     _lowerCAmelCase=(512, 512, 512, 512, 1500)			,     _lowerCAmelCase=(5, 3, 3, 1, 1)			,     _lowerCAmelCase=(1, 2, 3, 1, 1)			,     _lowerCAmelCase=512			,     _lowerCAmelCase=0			,     _lowerCAmelCase=1			,     _lowerCAmelCase=2			,     _lowerCAmelCase=False			,     _lowerCAmelCase=3			,     _lowerCAmelCase=2			,     _lowerCAmelCase=3			,     _lowerCAmelCase=None			,     **_lowerCAmelCase			,     )    ->							List[Any]:
     super().__init__(**_lowerCAmelCase			,     pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase		)
     _lowerCAmelCase				=      hidden_size
     _lowerCAmelCase				=      feat_extract_activation
     _lowerCAmelCase				=      list(_lowerCAmelCase		)
     _lowerCAmelCase				=      list(_lowerCAmelCase		)
     _lowerCAmelCase				=      list(_lowerCAmelCase		)
     _lowerCAmelCase				=      conv_bias
     _lowerCAmelCase				=      num_conv_pos_embeddings
     _lowerCAmelCase				=      num_conv_pos_embedding_groups
     _lowerCAmelCase				=      conv_pos_kernel_size
     _lowerCAmelCase				=      len(self.conv_dim		)
     _lowerCAmelCase				=      num_hidden_layers
     _lowerCAmelCase				=      intermediate_size
     _lowerCAmelCase				=      hidden_act
     _lowerCAmelCase				=      num_attention_heads
     _lowerCAmelCase				=      hidden_dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      feat_proj_dropout
     _lowerCAmelCase				=      final_dropout
     _lowerCAmelCase				=      layerdrop
     _lowerCAmelCase				=      layer_norm_eps
     _lowerCAmelCase				=      initializer_range
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      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
     _lowerCAmelCase				=      mask_time_prob
     _lowerCAmelCase				=      mask_time_length
     _lowerCAmelCase				=      mask_time_min_masks
     _lowerCAmelCase				=      mask_feature_prob
     _lowerCAmelCase				=      mask_feature_length
     _lowerCAmelCase				=      mask_feature_min_masks

     # ctc loss
     _lowerCAmelCase				=      ctc_loss_reduction
     _lowerCAmelCase				=      ctc_zero_infinity

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

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

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





 @property
 def 							_snake_case   (	self		)    ->							Tuple:
     return math.prod(self.conv_stride		)









'''simple docstring'''


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					bool = False    ):



    '''simple docstring'''





    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected string as input, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )
    if not isinstance(SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
        _lowerCAmelCase				=      F'''Expected boolean as use_pascal parameter, found {type(SCREAMING_SNAKE_CASE_    )}'''
        raise ValueError(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      input_str.split("_"    )

    _lowerCAmelCase				=      0 if use_pascal else 1

    _lowerCAmelCase				=      words[start_index:]

    _lowerCAmelCase				=      [word[0].upper() + word[1:] for word in words_to_capitalize]

    _lowerCAmelCase				=      "" if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]    )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


