


'''simple docstring'''
import tensorflow as tf

from ...tf_utils import shape_list

class 						_lowerCamelCase					(    tf.keras.layers.Layer				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=1      ,      __lowercase=False      ,      **__lowercase						):

  """simple docstring"""







  super().__init__(**__lowercase						)

  __A  :		int       =			vocab_size
  __A  :		str       =			d_embed
  __A  :		Dict       =			d_proj

  __A  :		Optional[Any]       =			cutoffs + [vocab_size]
  __A  :		Union[str, Any]       =			[0] + self.cutoffs
  __A  :		List[Any]       =			div_val

  __A  :		Optional[int]       =			self.cutoffs[0]
  __A  :		Tuple       =			len(self.cutoffs						) - 1
  __A  :		str       =			self.shortlist_size + self.n_clusters
  __A  :		Any       =			keep_order

  __A  :		List[str]       =			[]
  __A  :		Optional[Any]       =			[]


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  if self.n_clusters > 0:
   __A  :		Optional[int]       =			self.add_weight(
       shape=(self.n_clusters, self.d_embed)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name='cluster_weight'						)
   __A  :		Union[str, Any]       =			self.add_weight(
       shape=(self.n_clusters,)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name='cluster_bias'						)

  if self.div_val == 1:
   for i in range(len(self.cutoffs						)						):
    if self.d_proj != self.d_embed:
     __A  :		Optional[int]       =			self.add_weight(
         shape=(self.d_embed, self.d_proj)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_projs_._{i}"""      ,      )
     self.out_projs.append(__lowercase						)
    else:
     self.out_projs.append(__lowercase						)
    __A  :		Any       =			self.add_weight(
        shape=(self.vocab_size, self.d_embed)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_layers_._{i}_._weight"""      ,      )
    __A  :		Optional[int]       =			self.add_weight(
        shape=(self.vocab_size,)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_layers_._{i}_._bias"""      ,      )
    self.out_layers.append((weight, bias)						)
  else:
   for i in range(len(self.cutoffs						)						):
    __A     ,__A  :		List[str]       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
    __A  :		List[str]       =			self.d_embed // (self.div_val**i)

    __A  :		str       =			self.add_weight(
        shape=(d_emb_i, self.d_proj)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_projs_._{i}"""						)
    self.out_projs.append(__lowercase						)
    __A  :		Dict       =			self.add_weight(
        shape=(r_idx - l_idx, d_emb_i)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_layers_._{i}_._weight"""      ,      )
    __A  :		int       =			self.add_weight(
        shape=(r_idx - l_idx,)      ,      initializer='zeros'      ,      trainable=__lowercase      ,      name=F"""out_layers_._{i}_._bias"""      ,      )
    self.out_layers.append((weight, bias)						)
  super().build(__lowercase						)


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		List[Any]       =			x
  if proj is not None:
   __A  :		int       =			tf.einsum('ibd,ed->ibe'      ,      __lowercase      ,      __lowercase						)
  return tf.einsum('ibd,nd->ibn'      ,      __lowercase      ,      __lowercase						) + b


 @staticmethod
 def  snake_case__   (      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		str       =			shape_list(__lowercase						)
  __A  :		List[str]       =			tf.range(lp_size[0]      ,      dtype=target.dtype						)
  __A  :		int       =			tf.stack([r, target]      ,      1						)
  return tf.gather_nd(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase=True      ,      __lowercase=False						):

  """simple docstring"""







  __A  :		List[Any]       =			0
  if self.n_clusters == 0:
   __A  :		Any       =			self._logit(__lowercase      ,      self.out_layers[0][0]      ,      self.out_layers[0][1]      ,      self.out_projs[0]						)
   if target is not None:
    __A  :		Tuple       =			tf.nn.sparse_softmax_cross_entropy_with_logits(labels=__lowercase      ,      logits=__lowercase						)
   __A  :		List[str]       =			tf.nn.log_softmax(__lowercase      ,      axis=-1						)
  else:
   __A  :		Union[str, Any]       =			shape_list(__lowercase						)
   __A  :		Dict       =			[]
   __A  :		str       =			tf.zeros(hidden_sizes[:2]						)
   for i in range(len(self.cutoffs						)						):
    __A     ,__A  :		Dict       =			self.cutoff_ends[i], self.cutoff_ends[i + 1]
    if target is not None:
     __A  :		Tuple       =			(target >= l_idx) & (target < r_idx)
     __A  :		Tuple       =			tf.where(__lowercase						)
     __A  :		Dict       =			tf.boolean_mask(__lowercase      ,      __lowercase						) - l_idx

    if self.div_val == 1:
     __A  :		Dict       =			self.out_layers[0][0][l_idx:r_idx]
     __A  :		List[Any]       =			self.out_layers[0][1][l_idx:r_idx]
    else:
     __A  :		Optional[Any]       =			self.out_layers[i][0]
     __A  :		Any       =			self.out_layers[i][1]

    if i == 0:
     __A  :		int       =			tf.concat([cur_W, self.cluster_weight]      ,      0						)
     __A  :		List[Any]       =			tf.concat([cur_b, self.cluster_bias]      ,      0						)

     __A  :		str       =			self._logit(__lowercase      ,      __lowercase      ,      __lowercase      ,      self.out_projs[0]						)
     __A  :		Tuple       =			tf.nn.log_softmax(__lowercase						)
     out.append(head_logprob[..., : self.cutoffs[0]]						)
     if target is not None:
      __A  :		List[str]       =			tf.boolean_mask(__lowercase      ,      __lowercase						)
      __A  :		Optional[Any]       =			self._gather_logprob(__lowercase      ,      __lowercase						)
    else:
     __A  :		Tuple       =			self._logit(__lowercase      ,      __lowercase      ,      __lowercase      ,      self.out_projs[i]						)
     __A  :		int       =			tf.nn.log_softmax(__lowercase						)
     __A  :		Dict       =			self.cutoffs[0] + i - 1  # No probability for the head cluster
     __A  :		Any       =			head_logprob[..., cluster_prob_idx, None] + tail_logprob
     out.append(__lowercase						)
     if target is not None:
      __A  :		List[Any]       =			tf.boolean_mask(__lowercase      ,      __lowercase						)
      __A  :		List[str]       =			tf.boolean_mask(__lowercase      ,      __lowercase						)
      __A  :		Union[str, Any]       =			self._gather_logprob(__lowercase      ,      __lowercase						)
      cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
    if target is not None:
     loss += tf.scatter_nd(__lowercase      ,      -cur_logprob      ,      shape_list(__lowercase						)						)
   __A  :		int       =			tf.concat(__lowercase      ,      axis=-1						)

  if target is not None:
   if return_mean:
    __A  :		str       =			tf.reduce_mean(__lowercase						)
   # Add the training-time loss value to the layer using `self.add_loss()`.
   self.add_loss(__lowercase						)

   # Log the loss as a metric (we could log arbitrary metrics,
   # including different metrics for training and inference.
   self.add_metric(__lowercase      ,      name=self.name      ,      aggregation='mean' if return_mean else ''						)

  return out










'''simple docstring'''
from __future__ import annotations

class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Dict       =			order

  # a_{0} ... a_{k}
  __A  :		str       =			[1.0] + [0.0] * order
  # b_{0} ... b_{k}
  __A  :		Any       =			[1.0] + [0.0] * order

  # x[n-1] ... x[n-k]
  __A  :		Optional[int]       =			[0.0] * self.order
  # y[n-1] ... y[n-k]
  __A  :		Dict       =			[0.0] * self.order


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  if len(__lowercase						) < self.order:
   __A  :		List[Any]       =			[1.0, *a_coeffs]

  if len(__lowercase						) != self.order + 1:
   __A  :		Tuple       =			(
       F"""Expected a_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  if len(__lowercase						) != self.order + 1:
   __A  :		List[Any]       =			(
       F"""Expected b_coeffs to have {self.order + 1} elements """
       F"""for {self.order}-order filter, got {len(__lowercase						)}"""
   )
   raise ValueError(__lowercase						)

  __A  :		Optional[int]       =			a_coeffs
  __A  :		Optional[int]       =			b_coeffs







 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			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  :		Optional[int]       =			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

  __A  :		List[str]       =			self.input_history[:-1]
  __A  :		Optional[int]       =			self.output_history[:-1]

  __A  :		Optional[int]       =			sample
  __A  :		Dict       =			result

  return result






