
"""simple docstring"""







from math import factorial




class 				A__ :

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[Any]		    =   real
			if isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		List[str]		    =   [1] * rank
			else:
				__lowerCAmelCase    :		str		    =   rank

		def __repr__(   self				):
			return (
			    f"{self.real}+"
			    f"{'+'.join(str(_SCREAMING_SNAKE_CASE				)+'E'+str(n+1				)for n,dual in enumerate(self.duals				)				)}"
			)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.duals.copy()
			while cur[-1] == 0:
				cur.pop(-1				)
			return Dual(self.real ,	_SCREAMING_SNAKE_CASE				)

		def __add__(   self ,	_SCREAMING_SNAKE_CASE				):
			if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				return Dual(self.real + other ,	self.duals				)
			__lowerCAmelCase    :		Optional[Any]		    =   self.duals.copy()
			__lowerCAmelCase    :		int		    =   other.duals.copy()
			if len(_SCREAMING_SNAKE_CASE				) > len(_SCREAMING_SNAKE_CASE				):
				o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE				) - len(_SCREAMING_SNAKE_CASE				))				)
			elif len(_SCREAMING_SNAKE_CASE				) < len(_SCREAMING_SNAKE_CASE				):
				s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE				) - len(_SCREAMING_SNAKE_CASE				))				)
			__lowerCAmelCase    :		List[Any]		    =   []
			for i in range(len(_SCREAMING_SNAKE_CASE				)				):
				new_duals.append(s_dual[i] + o_dual[i]				)
			return Dual(self.real + other.real ,	_SCREAMING_SNAKE_CASE				)

		A_		: Tuple            =			__add__

		def __sub__(   self ,	_SCREAMING_SNAKE_CASE				):
			return self + other * -1

		def __mul__(   self ,	_SCREAMING_SNAKE_CASE				):
			if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		str		    =   []
				for i in self.duals:
					new_duals.append(i * other				)
				return Dual(self.real * other ,	_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		int		    =   [0] * (len(self.duals				) + len(other.duals				) + 1)
			for i, item in enumerate(self.duals				):
				for j, jtem in enumerate(other.duals				):
					new_duals[i + j + 1] += item * jtem
			for k in range(len(self.duals				)				):
				new_duals[k] += self.duals[k] * other.real
			for index in range(len(other.duals				)				):
				new_duals[index] += other.duals[index] * self.real
			return Dual(self.real * other.real ,	_SCREAMING_SNAKE_CASE				)

		A_		: str            =			__mul__

		def __truediv__(   self ,	_SCREAMING_SNAKE_CASE				):
			if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Optional[Any]		    =   []
				for i in self.duals:
					new_duals.append(i / other				)
				return Dual(self.real / other ,	_SCREAMING_SNAKE_CASE				)
			raise ValueError

		def __floordiv__(   self ,	_SCREAMING_SNAKE_CASE				):
			if not isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase    :		Any		    =   []
				for i in self.duals:
					new_duals.append(i // other				)
				return Dual(self.real // other ,	_SCREAMING_SNAKE_CASE				)
			raise ValueError







		def __pow__(   self ,	_SCREAMING_SNAKE_CASE				):
			if n < 0 or isinstance(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
				raise ValueError('power must be a positive integer'				)
			if n == 0:
				return 1
			if n == 1:
				return self
			__lowerCAmelCase    :		List[Any]		    =   self
			for _ in range(n - 1				):
				x *= self
			return x



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if not callable(_UpperCamelCase	):
		raise ValueError('differentiate() requires a function as input for func'	)
	if not isinstance(_UpperCamelCase ,						(float, int)	):
		raise ValueError('differentiate() requires a float as input for position'	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		raise ValueError('differentiate() requires an int as input for order'	)
	__lowerCAmelCase    :		List[str]		    =   Dual(_UpperCamelCase ,						1	)
	__lowerCAmelCase    :		Dict		    =   func(_UpperCamelCase	)
	if order == 0:
		return result.real
	return result.duals[order - 1] * factorial(_UpperCamelCase	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							def 					__lowerCAmelCase       (_UpperCamelCase	):
								return y**2 * y**4
							print(differentiate(f, 9, 2))

"""simple docstring"""







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


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """speechbrain/m-ctc-t-large""": """https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json""",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}




class 				A__ (			_lowerCamelCase):
		A_		: Any            =			'mctct'

		def __init__(   self ,	_SCREAMING_SNAKE_CASE=80_65 ,	_SCREAMING_SNAKE_CASE=15_36 ,	_SCREAMING_SNAKE_CASE=36 ,	_SCREAMING_SNAKE_CASE=61_44 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=3_84 ,	_SCREAMING_SNAKE_CASE=9_20 ,	_SCREAMING_SNAKE_CASE=1E-5 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE="relu" ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=0.3 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=(7,) ,	_SCREAMING_SNAKE_CASE=(3,) ,	_SCREAMING_SNAKE_CASE=80 ,	_SCREAMING_SNAKE_CASE=1 ,	_SCREAMING_SNAKE_CASE=None ,	_SCREAMING_SNAKE_CASE="sum" ,	_SCREAMING_SNAKE_CASE=False ,	**_SCREAMING_SNAKE_CASE ,	):
			super().__init__(**_SCREAMING_SNAKE_CASE ,	pad_token_id=_SCREAMING_SNAKE_CASE ,	bos_token_id=_SCREAMING_SNAKE_CASE ,	eos_token_id=_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		str		    =   vocab_size
			__lowerCAmelCase    :		Any		    =   hidden_size
			__lowerCAmelCase    :		int		    =   num_hidden_layers
			__lowerCAmelCase    :		Any		    =   intermediate_size
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		str		    =   attention_head_dim
			__lowerCAmelCase    :		int		    =   max_position_embeddings
			__lowerCAmelCase    :		List[Any]		    =   layer_norm_eps
			__lowerCAmelCase    :		Tuple		    =   layerdrop
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Dict		    =   initializer_range
			__lowerCAmelCase    :		Any		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		List[str]		    =   pad_token_id
			__lowerCAmelCase    :		Union[str, Any]		    =   bos_token_id
			__lowerCAmelCase    :		int		    =   eos_token_id
			__lowerCAmelCase    :		Optional[int]		    =   conv_glu_dim
			__lowerCAmelCase    :		List[Any]		    =   conv_dropout
			__lowerCAmelCase    :		Tuple		    =   num_conv_layers
			__lowerCAmelCase    :		Tuple		    =   input_feat_per_channel
			__lowerCAmelCase    :		Optional[int]		    =   input_channels
			__lowerCAmelCase    :		str		    =   conv_channels
			__lowerCAmelCase    :		str		    =   ctc_loss_reduction
			__lowerCAmelCase    :		Tuple		    =   ctc_zero_infinity

			# prevents config testing fail with exporting to json
			__lowerCAmelCase    :		List[str]		    =   list(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Dict		    =   list(_SCREAMING_SNAKE_CASE				)

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