


'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_ :     Optional[Any]								=      logging.get_logger(__name__)

UpperCAmelCase_ :     List[Any]								=      {
    '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 						_lowerCamelCase					(    snake_case_				):




	'''simple docstring'''
	__lowercase				:		Tuple    							=						'''speech_to_text_2'''
	__lowercase				:		Tuple    							=						['''past_key_values''']
	__lowercase				:		Tuple    							=						{'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}


	def __init__(      self      ,      __lowercase=10_000      ,      __lowercase=6      ,      __lowercase=2_048      ,      __lowercase=4      ,      __lowercase=0.0      ,      __lowercase=True      ,      __lowercase="relu"      ,      __lowercase=256      ,      __lowercase=0.1      ,      __lowercase=0.0      ,      __lowercase=0.0      ,      __lowercase=0.0_2      ,      __lowercase=2      ,      __lowercase=True      ,      __lowercase=1      ,      __lowercase=0      ,      __lowercase=2      ,      __lowercase=1_024      ,      **__lowercase      ,      ):

		"""simple docstring"""







		__A  :		Optional[int]       =			vocab_size
		__A  :		List[str]       =			d_model
		__A  :		List[str]       =			decoder_ffn_dim
		__A  :		Any       =			decoder_layers
		__A  :		List[Any]       =			decoder_attention_heads
		__A  :		str       =			dropout
		__A  :		Dict       =			attention_dropout
		__A  :		Union[str, Any]       =			activation_dropout
		__A  :		List[str]       =			activation_function
		__A  :		Dict       =			init_std
		__A  :		Union[str, Any]       =			decoder_layerdrop
		__A  :		Optional[Any]       =			use_cache
		__A  :		Any       =			decoder_layers
		__A  :		Any       =			scale_embedding  # scale factor will be sqrt(d_model) if True
		__A  :		Optional[Any]       =			max_target_positions

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










'''simple docstring'''
from collections.abc import Sequence


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 return sum(c * (x**i) for i, c in enumerate(UpperCamelCase__			)			)


def 			_lowercase       (				UpperCamelCase__       :					Sequence[float],				UpperCamelCase__       :					float			):
 __A  :		Optional[Any]       =			0.0
 for coeff in reversed(UpperCamelCase__			):
  __A  :		List[str]       =			result * x + coeff
 return result


if __name__ == "__main__":
 UpperCAmelCase_ :     Any								=      (0.0, 0.0, 5.0, 9.3, 7.0)
 UpperCAmelCase_ :     Any								=      10.0
 print(evaluate_poly(poly, x))
 print(horner(poly, x))






