
"""simple docstring"""




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


_A     :			Optional[int]  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """facebook/s2t-wav2vec2-large-en-de""": (
        """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"""
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""speech_to_text_2"""
   __lowerCAmelCase				=							["""past_key_values"""]
   __lowerCAmelCase				=							{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}
   def __init__(     self      ,       _a=10_000      ,       _a=6      ,       _a=2_048      ,       _a=4      ,       _a=0.0      ,       _a=True      ,       _a="relu"      ,       _a=256      ,       _a=0.1      ,       _a=0.0      ,       _a=0.0      ,       _a=0.0_2      ,       _a=2      ,       _a=True      ,       _a=1      ,       _a=0      ,       _a=2      ,       _a=1_024      ,       **_a      ,       ):
          lowercase       :							int									=				vocab_size
          lowercase       :							Any									=				d_model
          lowercase       :							Optional[int]									=				decoder_ffn_dim
          lowercase       :							Dict									=				decoder_layers
          lowercase       :							int									=				decoder_attention_heads
          lowercase       :							Any									=				dropout
          lowercase       :							List[str]									=				attention_dropout
          lowercase       :							Any									=				activation_dropout
          lowercase       :							Optional[Any]									=				activation_function
          lowercase       :							str									=				init_std
          lowercase       :							Any									=				decoder_layerdrop
          lowercase       :							Any									=				use_cache
          lowercase       :							Optional[int]									=				decoder_layers
          lowercase       :							Any									=				scale_embedding  # scale factor will be sqrt(d_model) if True
          lowercase       :							List[str]									=				max_target_positions

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




"""simple docstring"""




from __future__ import annotations

from math import pow, sqrt


def     __magic_name__      ( __snake_case							:			float	,					__snake_case							:			float	,					__snake_case							:			float    )					->							dict[str, float]:
       if (resistance, reactance, impedance).count(0    ) != 1:
              raise ValueError("One and only one argument must be 0"    )
       if resistance == 0:
              return {"resistance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif reactance == 0:
              return {"reactance": sqrt(pow(__snake_case	,					2    ) - pow(__snake_case	,					2    )    )}
       elif impedance == 0:
              return {"impedance": sqrt(pow(__snake_case	,					2    ) + pow(__snake_case	,					2    )    )}
       else:
              raise ValueError("Exactly one argument must be 0"    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()


