




import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


a													=			logging.get_logger(__name__)

a													=			"T5Config"




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case       )						->				jnp.ndarray:
						_UpperCAmelCase      =			jnp.zeros_like(snake_case       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]       )
						_UpperCAmelCase      =			shifted_input_ids.at[:, 0].set(snake_case       )

						_UpperCAmelCase      =			jnp.where(shifted_input_ids == -1_0_0			,     snake_case			,     snake_case       )
						return shifted_input_ids




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig




class       _A				(		__lowercase     ):
		__a					    =       """mt5"""
		__a					    =       MTaConfig





import math




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * power_factor




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case       )						->				float:
						if (
						    not isinstance(snake_case			,     (int, float)       )
						    or power_factor < -1
						    or power_factor > 1
						):
												raise ValueError("""power_factor must be a valid float value between -1 and 1."""       )
						return apparent_power * math.sqrt(1 - power_factor**2       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()