


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 					lowerCAmelCase__	(    a					,   a					,   a):




     '''simple docstring'''


     @register_to_config
     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = False			,						)      ->    Dict:
            super().__init__()

            _A				:	Tuple       =			nn.Linear(__lowerCamelCase			,						__lowerCamelCase			,						bias=__lowerCamelCase)

            _A				:	Any       =			nn.Embedding(__lowerCamelCase			,						__lowerCamelCase)
            _A				:	Optional[Any]       =			False

            _A				:	Optional[int]       =			nn.Dropout(p=__lowerCamelCase)

            _A				:	Tuple       =			TaConfig(
                d_model=__lowerCamelCase			,						num_heads=__lowerCamelCase			,						d_kv=__lowerCamelCase			,						d_ff=__lowerCamelCase			,						feed_forward_proj=__lowerCamelCase			,						dropout_rate=__lowerCamelCase			,						is_decoder=__lowerCamelCase			,						is_encoder_decoder=__lowerCamelCase			,						)
            _A				:	List[str]       =			nn.ModuleList()
            for lyr_num in range(__lowerCamelCase):
                   _A				:	str       =			TaBlock(__lowerCamelCase)
                   self.encoders.append(__lowerCamelCase)

            _A				:	List[str]       =			TaLayerNorm(__lowerCamelCase)
            _A				:	Union[str, Any]       =			nn.Dropout(p=__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            _A				:	Optional[int]       =			self.input_proj(__lowerCamelCase)

            # terminal relative positional encodings
            _A				:	int       =			encoder_inputs.shape[1]
            _A				:	str       =			torch.arange(__lowerCamelCase			,						device=encoder_inputs.device)

            _A				:	Union[str, Any]       =			encoder_inputs_mask.sum(-1)
            _A				:	int       =			torch.roll(input_positions.unsqueeze(0)			,						tuple(seq_lens.tolist())			,						dims=0)
            x += self.position_encoding(__lowerCamelCase)

            _A				:	int       =			self.dropout_pre(__lowerCamelCase)

            # inverted the attention mask
            _A				:	List[Any]       =			encoder_inputs.size()
            _A				:	Any       =			self.get_extended_attention_mask(__lowerCamelCase			,						__lowerCamelCase)

            for lyr in self.encoders:
                   _A				:	Dict       =			lyr(__lowerCamelCase			,						__lowerCamelCase)[0]
            _A				:	List[str]       =			self.layer_norm(__lowerCamelCase)

            return self.dropout_post(__lowerCamelCase), encoder_inputs_mask




from __future__ import annotations

from typing import Any







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    None:  # noqa: A002
            _A				:	List[str]       =			item
            _A				:	List[Any]       =			next







class 					lowerCAmelCase__	:




     '''simple docstring'''


     def __init__(						self)      ->    None:
            _A				:	Node | None       =			None
            _A				:	List[Any]       =			0


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	Union[str, Any]       =			Node(__lowerCamelCase			,						self.head)
            self.size += 1


     def 			_lowerCamelCase			(						self)      ->    Any:
            # Switched 'self.is_empty()' to 'self.head is None'
            # because mypy was considering the possibility that 'self.head'
            # can be None in below else part and giving error
            if self.head is None:
                   return None
            else:
                   _A				:	str       =			self.head.item
                   _A				:	List[Any]       =			self.head.next
                   self.size -= 1
                   return item


     def 			_lowerCamelCase			(						self)      ->    bool:
            return self.head is None


     def __str__(						self)      ->    str:
            if self.is_empty():
                   return ""
            else:
                   _A				:	List[str]       =			self.head
                   _A				:	Optional[int]       =			""
                   _A				:	list[str]       =			[]
                   while iterate:
                          item_list.append(str(iterate.item))
                          _A				:	Optional[Any]       =			iterate.next

                   _A				:	List[str]       =			" --> ".join(__lowerCamelCase)

                   return item_str


     def __len__(						self)      ->    int:
            return self.size
