'''simple docstring'''





import jax.numpy as jnp

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							'''T5Config'''

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_       ,					lowerCAmelCase_   )		->						jnp.ndarray:




       '''simple docstring'''






       _A=	jnp.zeros_like(__UpperCamelCase   )
       _A=	shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]   )
       _A=	shifted_input_ids.at[:, 0].set(__UpperCamelCase   )

       _A=	jnp.where(shifted_input_ids == -1_00       ,					__UpperCamelCase       ,					__UpperCamelCase   )
       return shifted_input_ids





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any		="""mt5"""
     _SCREAMING_SNAKE_CASE     :     List[Any]		=MTaConfig





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		="""mt5"""
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=MTaConfig





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     Any		="""mt5"""
     _SCREAMING_SNAKE_CASE     :     Dict		=MTaConfig
from ..utils import DummyObject, requires_backends





class      lowerCAmelCase						(					metaclass=_a			):
     _SCREAMING_SNAKE_CASE     :     Optional[int]		=["""transformers""", """torch""", """note_seq"""]



     def __init__(					self				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(self				,      ['transformers', 'torch', 'note_seq']       )



     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )





     @classmethod
     def 			a__  (					cls				,      *lowerCAmelCase__				,      **lowerCAmelCase__       ):
            requires_backends(cls				,      ['transformers', 'torch', 'note_seq']       )