





'''simple docstring'''

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


UpperCAmelCase					:		Any		=							logging.get_logger(__name__)

UpperCAmelCase					:		List[str]		=							{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class lowerCamelCase						(a__					):
  _lowercase  :	Union[str, Any]       					=				"""speech_to_text"""
  _lowercase  :	str       					=				["""past_key_values"""]
  _lowercase  :	List[str]       					=				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







  def __init__(		self     ,     lowercase__=10_000     ,     lowercase__=12     ,     lowercase__=2_048     ,     lowercase__=4     ,     lowercase__=6     ,     lowercase__=2_048     ,     lowercase__=4     ,     lowercase__=0.0     ,     lowercase__=0.0     ,     lowercase__=True     ,     lowercase__=True     ,     lowercase__="relu"     ,     lowercase__=256     ,     lowercase__=0.1     ,     lowercase__=0.0     ,     lowercase__=0.0     ,     lowercase__=0.02     ,     lowercase__=2     ,     lowercase__=True     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=2     ,     lowercase__=6_000     ,     lowercase__=1_024     ,     lowercase__=2     ,     lowercase__=(5, 5)     ,     lowercase__=1_024     ,     lowercase__=80     ,     lowercase__=1     ,     **lowercase__     ,     )		->						List[Any]:






        """simple docstring"""





        _snake_case		:	int					     =     vocab_size
        _snake_case		:	str					     =     d_model
        _snake_case		:	Optional[Any]					     =     encoder_ffn_dim
        _snake_case		:	List[str]					     =     encoder_layers
        _snake_case		:	List[Any]					     =     encoder_attention_heads
        _snake_case		:	Union[str, Any]					     =     decoder_ffn_dim
        _snake_case		:	List[str]					     =     decoder_layers
        _snake_case		:	Any					     =     decoder_attention_heads
        _snake_case		:	Any					     =     dropout
        _snake_case		:	List[str]					     =     attention_dropout
        _snake_case		:	Dict					     =     activation_dropout
        _snake_case		:	List[str]					     =     activation_function
        _snake_case		:	Optional[int]					     =     init_std
        _snake_case		:	List[str]					     =     encoder_layerdrop
        _snake_case		:	Any					     =     decoder_layerdrop
        _snake_case		:	Any					     =     use_cache
        _snake_case		:	Optional[Any]					     =     encoder_layers
        _snake_case		:	Optional[int]					     =     scale_embedding  # scale factor will be sqrt(d_model) if True
        _snake_case		:	int					     =     max_source_positions
        _snake_case		:	Optional[int]					     =     max_target_positions
        _snake_case		:	int					     =     num_conv_layers
        _snake_case		:	Optional[int]					     =     list(lowercase__		)
        _snake_case		:	List[str]					     =     conv_channels
        _snake_case		:	Tuple					     =     input_feat_per_channel
        _snake_case		:	int					     =     input_channels

        if len(self.conv_kernel_sizes		) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
                  F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes		)}`, '''
                  F'''`config.num_conv_layers = {self.num_conv_layers}`.'''		)

        super().__init__(
            pad_token_id=lowercase__     ,     bos_token_id=lowercase__     ,     eos_token_id=lowercase__     ,     is_encoder_decoder=lowercase__     ,     decoder_start_token_id=lowercase__     ,     **lowercase__     ,     )









'''simple docstring'''

UpperCAmelCase					:		Union[str, Any]		=							tuple[float, float, float]
UpperCAmelCase					:		int		=							tuple[float, float, float]




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	str					     =     end_pointa[0] - end_pointa[0]
      _snake_case		:	Tuple					     =     end_pointa[1] - end_pointa[1]
      _snake_case		:	Any					     =     end_pointa[2] - end_pointa[2]
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     ab[1] * ac[2] - ab[2] * ac[1]  # *i
      _snake_case		:	List[str]					     =     (ab[0] * ac[2] - ab[2] * ac[0]) * -1  # *j
      _snake_case		:	Optional[int]					     =     ab[0] * ac[1] - ab[1] * ac[0]  # *k
      return (x, y, z)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      return tuple(round(lowerCAmelCase_			,      lowerCAmelCase_	) for x in vector	) == (0, 0, 0)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = 10	):





      """simple docstring"""



      _snake_case		:	str					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     create_vector(lowerCAmelCase_			,      lowerCAmelCase_	)
      return is_zero_vector(get_ad_vectors_cross(lowerCAmelCase_			,      lowerCAmelCase_	)			,      lowerCAmelCase_	)


