from __future__ import annotations
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  order

   # a_{0} ... a_{k}
   __a       =  [1.0] + [0.0] * order
   # b_{0} ... b_{k}
   __a       =  [1.0] + [0.0] * order

   # x[n-1] ... x[n-k]
   __a       =  [0.0] * self.order
   # y[n-1] ... y[n-k]
   __a       =  [0.0] * self.order

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      list[float]     ,							__SCREAMING_SNAKE_CASE :      list[float]       ):
   if len(__SCREAMING_SNAKE_CASE       ) < self.order:
    __a       =  [1.0, *a_coeffs]

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected a_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   if len(__SCREAMING_SNAKE_CASE       ) != self.order + 1:
    __a       =  (
        f"""Expected b_coeffs to have {self.order + 1} elements """
        f"""for {self.order}-order filter, got {len(__SCREAMING_SNAKE_CASE       )}"""
    )
    raise ValueError(__SCREAMING_SNAKE_CASE       )

   __a       =  a_coeffs
   __a       =  b_coeffs

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      float       ):
   __a       =  0.0

   # Start at index 1 and do index 0 at the end.
   for i in range(1     ,							self.order + 1       ):
    result += (
        self.b_coeffs[i] * self.input_history[i - 1]
        - self.a_coeffs[i] * self.output_history[i - 1]
    )

   __a       =  (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

   __a       =  self.input_history[:-1]
   __a       =  self.output_history[:-1]

   __a       =  sample
   __a       =  result

   return result



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    A_ (       a_			,       a_			,       a_ ):

  @register_to_config
  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      float     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
   super().__init__()

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  nn.Embedding(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   __a       =  False

   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

   __a       =  TaConfig(
       vocab_size=__SCREAMING_SNAKE_CASE     ,							d_model=__SCREAMING_SNAKE_CASE     ,							num_heads=__SCREAMING_SNAKE_CASE     ,							d_kv=__SCREAMING_SNAKE_CASE     ,							d_ff=__SCREAMING_SNAKE_CASE     ,							dropout_rate=__SCREAMING_SNAKE_CASE     ,							feed_forward_proj=__SCREAMING_SNAKE_CASE     ,							is_decoder=__SCREAMING_SNAKE_CASE     ,							is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,							)

   __a       =  nn.ModuleList()
   for lyr_num in range(__SCREAMING_SNAKE_CASE       ):
    __a       =  TaBlock(__SCREAMING_SNAKE_CASE       )
    self.encoders.append(__SCREAMING_SNAKE_CASE       )

   __a       =  TaLayerNorm(__SCREAMING_SNAKE_CASE       )
   __a       =  nn.Dropout(p=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   __a       =  self.token_embedder(__SCREAMING_SNAKE_CASE       )

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

   __a       =  self.dropout_pre(__SCREAMING_SNAKE_CASE       )

   # inverted the attention mask
   __a       =  encoder_input_tokens.size()
   __a       =  self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

   for lyr in self.encoders:
    __a       =  lyr(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )[0]
   __a       =  self.layer_norm(__SCREAMING_SNAKE_CASE       )

   return self.dropout_post(__SCREAMING_SNAKE_CASE       ), encoder_inputs_mask


