






"""simple docstring"""
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   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					float      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					bool = False      ,      )		->     Dict:
				super().__init__()

				UpperCAmelCase_	:					Any     	=   nn.Embedding(lowerCAmelCase_      ,      lowerCAmelCase_		)

				UpperCAmelCase_	:					Tuple     	=   nn.Embedding(lowerCAmelCase_      ,      lowerCAmelCase_		)
				UpperCAmelCase_	:					List[Any]     	=   False

				UpperCAmelCase_	:					Dict     	=   nn.Dropout(p=lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   TaConfig(
				    vocab_size=lowerCAmelCase_      ,      d_model=lowerCAmelCase_      ,      num_heads=lowerCAmelCase_      ,      d_kv=lowerCAmelCase_      ,      d_ff=lowerCAmelCase_      ,      dropout_rate=lowerCAmelCase_      ,      feed_forward_proj=lowerCAmelCase_      ,      is_decoder=lowerCAmelCase_      ,      is_encoder_decoder=lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   nn.ModuleList()
				for lyr_num in range(lowerCAmelCase_		):
							UpperCAmelCase_	:					Any     	=   TaBlock(lowerCAmelCase_		)
							self.encoders.append(lowerCAmelCase_		)

				UpperCAmelCase_	:					Union[str, Any]     	=   TaLayerNorm(lowerCAmelCase_		)
				UpperCAmelCase_	:					Any     	=   nn.Dropout(p=lowerCAmelCase_		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Any		)		->     Any:
				UpperCAmelCase_	:					Any     	=   self.token_embedder(lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   encoder_input_tokens.shape[1]
				UpperCAmelCase_	:					str     	=   torch.arange(lowerCAmelCase_      ,      device=encoder_input_tokens.device		)
				x += self.position_encoding(lowerCAmelCase_		)

				UpperCAmelCase_	:					int     	=   self.dropout_pre(lowerCAmelCase_		)

				# inverted the attention mask
				UpperCAmelCase_	:					List[Any]     	=   encoder_input_tokens.size()
				UpperCAmelCase_	:					int     	=   self.get_extended_attention_mask(lowerCAmelCase_      ,      lowerCAmelCase_		)

				for lyr in self.encoders:
							UpperCAmelCase_	:					Dict     	=   lyr(lowerCAmelCase_      ,      lowerCAmelCase_		)[0]
				UpperCAmelCase_	:					Dict     	=   self.layer_norm(lowerCAmelCase_		)

				return self.dropout_post(lowerCAmelCase_		), encoder_inputs_mask











"""simple docstring"""
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = '''T5Config'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''mt5'''
	__magic_name__       =							MTaConfig



