


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__				       =			logging.get_logger(__name__)

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''
     UpperCAmelCase_         =  "mt5"
     UpperCAmelCase_         =  ["past_key_values"]




     def __init__( self       :       Tuple	,      UpperCamelCase       :       Optional[int]=25_01_12	,      UpperCamelCase       :       int=5_12	,      UpperCamelCase       :       int=64	,      UpperCamelCase       :       Dict=10_24	,      UpperCamelCase       :       str=8	,      UpperCamelCase       :       Dict=None	,      UpperCamelCase       :       Tuple=6	,      UpperCamelCase       :       List[str]=32	,      UpperCamelCase       :       Optional[int]=1_28	,      UpperCamelCase       :       str=0.1	,      UpperCamelCase       :       Optional[Any]=1E-6	,      UpperCamelCase       :       Tuple=1.0	,      UpperCamelCase       :       Any="gated-gelu"	,      UpperCamelCase       :       Tuple=True	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       List[Any]="T5Tokenizer"	,      UpperCamelCase       :       str=False	,      UpperCamelCase       :       Tuple=0	,      UpperCamelCase       :       Dict=1	,      UpperCamelCase       :       List[Any]=0	,      **UpperCamelCase       :       Union[str, Any]	,      ):





            """simple docstring"""



            super().__init__(
                is_encoder_decoder=UpperCAmelCase_	,      tokenizer_class=UpperCAmelCase_	,      tie_word_embeddings=UpperCAmelCase_	,      pad_token_id=UpperCAmelCase_	,      eos_token_id=UpperCAmelCase_	,      decoder_start_token_id=UpperCAmelCase_	,      **UpperCAmelCase_	,      )
            _lowercase					:				Optional[Any]           =			vocab_size
            _lowercase					:				List[Any]           =			d_model
            _lowercase					:				Tuple           =			d_kv
            _lowercase					:				str           =			d_ff
            _lowercase					:				Dict           =			num_layers
            _lowercase					:				Any           =			(
                num_decoder_layers if num_decoder_layers is not None else self.num_layers
            )  # default = symmetry
            _lowercase					:				Any           =			num_heads
            _lowercase					:				int           =			relative_attention_num_buckets
            _lowercase					:				List[Any]           =			relative_attention_max_distance
            _lowercase					:				List[str]           =			dropout_rate
            _lowercase					:				List[str]           =			layer_norm_epsilon
            _lowercase					:				Tuple           =			initializer_factor
            _lowercase					:				Union[str, Any]           =			feed_forward_proj
            _lowercase					:				Optional[Any]           =			use_cache

            _lowercase					:				Tuple           =			self.feed_forward_proj.split('''-'''		)
            _lowercase					:				Dict           =			act_info[-1]
            _lowercase					:				Dict           =			act_info[0] == 'gated'

            if len(UpperCAmelCase_		) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_		) > 2:
                   raise ValueError(
                       F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                       '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                       '''\'gated-gelu\' or \'relu\''''		)

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   _lowercase					:				Optional[int]           =			'gelu_new'




     @property
     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            return self.d_model




     @property
     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            return self.num_heads




     @property
     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            return self.num_layers




class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def   lowerCAmelCase_   ( self       :       str		):





            """simple docstring"""



            _lowercase					:				str           =			{
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   _lowercase					:				Optional[Any]           =			'past_encoder_sequence + sequence'
                   _lowercase					:				List[str]           =			{0: 'batch'}
                   _lowercase					:				Optional[int]           =			{0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   _lowercase					:				str           =			{0: 'batch', 1: 'decoder_sequence'}
                   _lowercase					:				int           =			{0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(UpperCAmelCase_	,      direction='''inputs'''		)

            return common_inputs




     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def   lowerCAmelCase_   ( self       :       Optional[int]		):





            """simple docstring"""



            return 13




     @property
     def   lowerCAmelCase_   ( self       :       int		):





            """simple docstring"""



            return 5E-4






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_		):



     '''simple docstring'''




     @register_to_config
     def __init__( self       :       Dict	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       float	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       int	,      UpperCamelCase       :       str	,      UpperCamelCase       :       bool = False	,      ):





            """simple docstring"""



            super().__init__()

            _lowercase					:				Any           =			nn.Linear(UpperCamelCase	,      UpperCamelCase	,      bias=UpperCamelCase		)

            _lowercase					:				List[Any]           =			nn.Embedding(UpperCamelCase	,      UpperCamelCase		)
            _lowercase					:				Any           =			False

            _lowercase					:				Tuple           =			nn.Dropout(p=UpperCamelCase		)

            _lowercase					:				Any           =			TaConfig(
                d_model=UpperCamelCase	,      num_heads=UpperCamelCase	,      d_kv=UpperCamelCase	,      d_ff=UpperCamelCase	,      feed_forward_proj=UpperCamelCase	,      dropout_rate=UpperCamelCase	,      is_decoder=UpperCamelCase	,      is_encoder_decoder=UpperCamelCase	,      )
            _lowercase					:				Any           =			nn.ModuleList()
            for lyr_num in range(UpperCamelCase		):
                   _lowercase					:				Tuple           =			TaBlock(UpperCamelCase		)
                   self.encoders.append(UpperCamelCase		)

            _lowercase					:				List[Any]           =			TaLayerNorm(UpperCamelCase		)
            _lowercase					:				Optional[Any]           =			nn.Dropout(p=UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       int	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[Any]		):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			self.input_proj(UpperCamelCase		)

            # terminal relative positional encodings
            _lowercase					:				Optional[int]           =			encoder_inputs.shape[1]
            _lowercase					:				Optional[int]           =			torch.arange(UpperCamelCase	,      device=encoder_inputs.device		)

            _lowercase					:				Any           =			encoder_inputs_mask.sum(-1		)
            _lowercase					:				int           =			torch.roll(input_positions.unsqueeze(0		)	,      tuple(seq_lens.tolist()		)	,      dims=0		)
            x += self.position_encoding(UpperCamelCase		)

            _lowercase					:				Optional[int]           =			self.dropout_pre(UpperCamelCase		)

            # inverted the attention mask
            _lowercase					:				Optional[int]           =			encoder_inputs.size()
            _lowercase					:				List[Any]           =			self.get_extended_attention_mask(UpperCamelCase	,      UpperCamelCase		)

            for lyr in self.encoders:
                   _lowercase					:				Optional[int]           =			lyr(UpperCamelCase	,      UpperCamelCase		)[0]
            _lowercase					:				List[str]           =			self.layer_norm(UpperCamelCase		)

            return self.dropout_post(UpperCamelCase		), encoder_inputs_mask