




import tensorflow as tf

from ...tf_utils import shape_list




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




      """simple docstring"""





      def __init__(				self    ,				A    ,				A    ,				A    ,				A    ,				A=1    ,				A=False    ,				**A     )		->    List[Any]:
         super().__init__(**_a     )

         snake_case				:    List[str]           =	vocab_size
         snake_case				:    List[str]           =	d_embed
         snake_case				:    str           =	d_proj

         snake_case				:    Tuple           =	cutoffs + [vocab_size]
         snake_case				:    Tuple           =	[0] + self.cutoffs
         snake_case				:    str           =	div_val

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

         snake_case				:    Tuple           =	[]
         snake_case				:    Union[str, Any]           =	[]




      def      UpperCAmelCase				(				self    ,				A     )		->    Dict:
         if self.n_clusters > 0:
            snake_case				:    List[Any]           =	self.add_weight(
                shape=(self.n_clusters, self.d_embed)    ,				initializer="""zeros"""    ,				trainable=_a    ,				name="""cluster_weight"""     )
            snake_case				:    Optional[int]           =	self.add_weight(
                shape=(self.n_clusters,)    ,				initializer="""zeros"""    ,				trainable=_a    ,				name="""cluster_bias"""     )

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

               snake_case				:    Optional[Any]           =	self.add_weight(
                   shape=(d_emb_i, self.d_proj)    ,				initializer="""zeros"""    ,				trainable=_a    ,				name=f"""out_projs_._{i}"""     )
               self.out_projs.append(_a     )
               snake_case				:    str           =	self.add_weight(
                   shape=(r_idx - l_idx, d_emb_i)    ,				initializer="""zeros"""    ,				trainable=_a    ,				name=f"""out_layers_._{i}_._weight"""    ,				)
               snake_case				:    Tuple           =	self.add_weight(
                   shape=(r_idx - l_idx,)    ,				initializer="""zeros"""    ,				trainable=_a    ,				name=f"""out_layers_._{i}_._bias"""    ,				)
               self.out_layers.append((weight, bias)     )
         super().build(_a     )




      @staticmethod
      def      UpperCAmelCase				(				A    ,				A    ,				A    ,				A=None     )		->    Tuple:
         snake_case				:    List[str]           =	x
         if proj is not None:
            snake_case				:    List[str]           =	tf.einsum("""ibd,ed->ibe"""    ,				_a    ,				_a     )
         return tf.einsum("""ibd,nd->ibn"""    ,				_a    ,				_a     ) + b




      @staticmethod
      def      UpperCAmelCase				(				A    ,				A     )		->    Any:
         snake_case				:    Any           =	shape_list(_a     )
         snake_case				:    Optional[int]           =	tf.range(lp_size[0]    ,				dtype=target.dtype     )
         snake_case				:    List[Any]           =	tf.stack([r, target]    ,				1     )
         return tf.gather_nd(_a    ,				_a     )




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

               if self.div_val == 1:
                  snake_case				:    int           =	self.out_layers[0][0][l_idx:r_idx]
                  snake_case				:    str           =	self.out_layers[0][1][l_idx:r_idx]
               else:
                  snake_case				:    List[Any]           =	self.out_layers[i][0]
                  snake_case				:    List[Any]           =	self.out_layers[i][1]

               if i == 0:
                  snake_case				:    int           =	tf.concat([cur_W, self.cluster_weight]    ,				0     )
                  snake_case				:    Dict           =	tf.concat([cur_b, self.cluster_bias]    ,				0     )

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

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

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

         return out











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

   if len(lowercase					) != 3 or not all(isinstance(lowercase						,lowercase					) for cost in costs					):
      raise ValueError("""The parameter costs should be a list of three integers"""					)

   if len(lowercase					) == 0:
      return 0

   if min(lowercase					) <= 0:
      raise ValueError("""All days elements should be greater than 0"""					)

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         return 0

      if index not in days_set:
         return dynamic_programming(index + 1					)

      return min(
          costs[0] + dynamic_programming(index + 1					)						,costs[1] + dynamic_programming(index + 7					)						,costs[2] + dynamic_programming(index + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





