



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class     _UpperCAmelCase			(       A__			,      A__			,      A__     ):




     @register_to_config
     def __init__(				self ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ ,						a__ = False ,						):
         super().__init__()

         A__		 =					nn.Embedding(a__ ,						a__)

         A__		 =					nn.Embedding(a__ ,						a__)
         A__		 =					False

         A__		 =					nn.Dropout(p=a__)

         A__		 =					TaConfig(
             vocab_size=a__ ,						d_model=a__ ,						num_heads=a__ ,						d_kv=a__ ,						d_ff=a__ ,						dropout_rate=a__ ,						feed_forward_proj=a__ ,						is_decoder=a__ ,						is_encoder_decoder=a__ ,						)

         A__		 =					nn.ModuleList()
         for lyr_num in range(a__):
             A__		 =					TaBlock(a__)
             self.encoders.append(a__)

         A__		 =					TaLayerNorm(a__)
         A__		 =					nn.Dropout(p=a__)




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					self.token_embedder(a__)

         A__		 =					encoder_input_tokens.shape[1]
         A__		 =					torch.arange(a__ ,						device=encoder_input_tokens.device)
         x += self.position_encoding(a__)

         A__		 =					self.dropout_pre(a__)

         # inverted the attention mask
         A__		 =					encoder_input_tokens.size()
         A__		 =					self.get_extended_attention_mask(a__ ,						a__)

         for lyr in self.encoders:
             A__		 =					lyr(a__ ,						a__)[0]
         A__		 =					self.layer_norm(a__)

         return self.dropout_post(a__), encoder_inputs_mask





import jax.numpy as jnp

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


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"T5Config"



def 		lowerCAmelCase__    (					UpperCamelCase_				:       jnp.array ,       UpperCamelCase_				:       int ,       UpperCamelCase_				:       int	)->  jnp.ndarray:
    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_0_0 ,       UpperCamelCase_ ,       UpperCamelCase_	)
    return shifted_input_ids



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig







class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''mt5'''
     UpperCamelCase__		    =				MTaConfig
