
import jax.numpy as jnp

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


__lowerCAmelCase    :	List[Any]						=						logging.get_logger(__name__)

__lowerCAmelCase    :	Optional[Any]						=						"T5Config"


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  jnp.ndarray:
    __lowercase						:	Dict					=				jnp.zeros_like(SCREAMING_SNAKE_CASE__		)
    __lowercase						:	int					=				shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]		)
    __lowercase						:	Union[str, Any]					=				shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE__		)

    __lowercase						:	int					=				jnp.where(shifted_input_ids == -100		,    SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		)
    return shifted_input_ids


class   __lowerCAmelCase		(		_UpperCAmelCase					):



      """simple docstring"""







      A__		:	List[str]         =			'''mt5'''
      A__		:	List[Any]         =			MTaConfig






class   __lowerCAmelCase		(		_UpperCAmelCase					):



      """simple docstring"""







      A__		:	Dict         =			'''mt5'''
      A__		:	Any         =			MTaConfig






class   __lowerCAmelCase		(		_UpperCAmelCase					):



      """simple docstring"""







      A__		:	str         =			'''mt5'''
      A__		:	Dict         =			MTaConfig





from typing import Dict

from .base import GenericTensor, Pipeline


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        if truncation is not None:
            if "truncation" in tokenize_kwargs:
                raise ValueError(
                    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'		)
            A__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    Any:





        '''simple docstring'''







        if return_tensors:
            return model_outputs[0]
        if self.framework == "pt":
            return model_outputs[0].tolist()
        elif self.framework == "tf":
            return model_outputs[0].numpy().tolist()


    def __call__(      self :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







        return super().__call__(*lowercase_,**lowercase_		)

