


'''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      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					snake_case		):




   @register_to_config
   def __init__(      self					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase					,      lowercase = False					,      )			->						List[Any]:


        '''simple docstring'''






        super().__init__()

        __SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = nn.Linear(lowercase					,      lowercase					,      bias=lowercase       )

        __SCREAMING_SNAKE_CASE					:							List[Any]			   = nn.Embedding(lowercase					,      lowercase       )
        __SCREAMING_SNAKE_CASE					:							Any			   = False

        __SCREAMING_SNAKE_CASE					:							Dict			   = nn.Dropout(p=lowercase       )

        __SCREAMING_SNAKE_CASE					:							Optional[int]			   = TaConfig(
            d_model=lowercase					,      num_heads=lowercase					,      d_kv=lowercase					,      d_ff=lowercase					,      feed_forward_proj=lowercase					,      dropout_rate=lowercase					,      is_decoder=lowercase					,      is_encoder_decoder=lowercase					,      )
        __SCREAMING_SNAKE_CASE					:							List[str]			   = nn.ModuleList()
        for lyr_num in range(lowercase       ):
             __SCREAMING_SNAKE_CASE					:							Dict			   = TaBlock(lowercase       )
             self.encoders.append(lowercase       )

        __SCREAMING_SNAKE_CASE					:							int			   = TaLayerNorm(lowercase       )
        __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = nn.Dropout(p=lowercase       )




   def 			_snake_case   (      self					,      lowercase					,      lowercase       )			->						Union[str, Any]:


        '''simple docstring'''






        __SCREAMING_SNAKE_CASE					:							List[str]			   = self.input_proj(lowercase       )

        # terminal relative positional encodings
        __SCREAMING_SNAKE_CASE					:							List[str]			   = encoder_inputs.shape[1]
        __SCREAMING_SNAKE_CASE					:							str			   = torch.arange(lowercase					,      device=encoder_inputs.device       )

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

        __SCREAMING_SNAKE_CASE					:							Any			   = self.dropout_pre(lowercase       )

        # inverted the attention mask
        __SCREAMING_SNAKE_CASE					:							Optional[int]			   = encoder_inputs.size()
        __SCREAMING_SNAKE_CASE					:							int			   = self.get_extended_attention_mask(lowercase					,      lowercase       )

        for lyr in self.encoders:
             __SCREAMING_SNAKE_CASE					:							Optional[Any]			   = lyr(lowercase					,      lowercase       )[0]
        __SCREAMING_SNAKE_CASE					:							List[str]			   = self.layer_norm(lowercase       )

        return self.dropout_post(lowercase       ), encoder_inputs_mask






'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


_A      =     TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=snake_case		)
class      SCREAMING_SNAKE_CASE_	( abc.ABC		):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			__a					:      str
			__a					:      ClassVar[Features]
			__a					:      ClassVar[Features]




			def 			_snake_case   (      self					,      lowercase       )			->						T:


								'''simple docstring'''






								return copy.deepcopy(self       )




			@property
			def 			_snake_case   (      self       )			->						Features:


								'''simple docstring'''






								return Features(**self.input_schema					,      **self.label_schema       )




			@property
			@abc.abstractmethod
			def 			_snake_case   (      self       )			->						Dict[str, str]:


								'''simple docstring'''






								raise NotImplementedError




			@classmethod
			def 			_snake_case   (      cls					,      lowercase       )			->						T:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = {f.name for f in dataclasses.fields(cls       )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}       )


