"""simple docstring"""




import tensorflow as tf

from ...tf_utils import shape_list
class 		__A							(				tf.keras.layers.Layer	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=1       ,  a__=False       ,  **a__						):
       super().__init__(**a__						)

       _lowerCAmelCase		: List[Any]						    =  vocab_size
       _lowerCAmelCase		: Dict						    =  d_embed
       _lowerCAmelCase		: str						    =  d_proj

       _lowerCAmelCase		: Dict						    =  cutoffs + [vocab_size]
       _lowerCAmelCase		: List[Any]						    =  [0] + self.cutoffs
       _lowerCAmelCase		: List[str]						    =  div_val

       _lowerCAmelCase		: List[str]						    =  self.cutoffs[0]
       _lowerCAmelCase		: Any						    =  len(self.cutoffs						) - 1
       _lowerCAmelCase		: Tuple						    =  self.shortlist_size + self.n_clusters
       _lowerCAmelCase		: Dict						    =  keep_order

       _lowerCAmelCase		: Tuple						    =  []
       _lowerCAmelCase		: Optional[int]						    =  []






     def 					__A						(	self       ,  a__						):
       if self.n_clusters > 0:
         _lowerCAmelCase		: Union[str, Any]						    =  self.add_weight(
             shape=(self.n_clusters, self.d_embed)       ,  initializer="""zeros"""       ,  trainable=a__       ,  name="""cluster_weight"""						)
         _lowerCAmelCase		: Optional[Any]						    =  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:
             _lowerCAmelCase		: Dict						    =  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__						)
           _lowerCAmelCase		: List[str]						    =  self.add_weight(
               shape=(self.vocab_size, self.d_embed)       ,  initializer="""zeros"""       ,  trainable=a__       ,  name=F"out_layers_._{i}_._weight"       ,  )
           _lowerCAmelCase		: Any						    =  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						)						):
           _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  self.cutoff_ends[i], self.cutoff_ends[i + 1]
           _lowerCAmelCase		: Dict						    =  self.d_embed // (self.div_val**i)

           _lowerCAmelCase		: List[str]						    =  self.add_weight(
               shape=(d_emb_i, self.d_proj)       ,  initializer="""zeros"""       ,  trainable=a__       ,  name=F"out_projs_._{i}"						)
           self.out_projs.append(a__						)
           _lowerCAmelCase		: List[str]						    =  self.add_weight(
               shape=(r_idx - l_idx, d_emb_i)       ,  initializer="""zeros"""       ,  trainable=a__       ,  name=F"out_layers_._{i}_._weight"       ,  )
           _lowerCAmelCase		: Any						    =  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 					__A						(	a__       ,  a__       ,  a__       ,  a__=None						):
       _lowerCAmelCase		: Optional[int]						    =  x
       if proj is not None:
         _lowerCAmelCase		: Tuple						    =  tf.einsum("""ibd,ed->ibe"""       ,  a__       ,  a__						)
       return tf.einsum("""ibd,nd->ibn"""       ,  a__       ,  a__						) + b






     @staticmethod
     def 					__A						(	a__       ,  a__						):
       _lowerCAmelCase		: int						    =  shape_list(a__						)
       _lowerCAmelCase		: Dict						    =  tf.range(lp_size[0]       ,  dtype=target.dtype						)
       _lowerCAmelCase		: str						    =  tf.stack([r, target]       ,  1						)
       return tf.gather_nd(a__       ,  a__						)






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

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

           if i == 0:
             _lowerCAmelCase		: Optional[Any]						    =  tf.concat([cur_W, self.cluster_weight]       ,  0						)
             _lowerCAmelCase		: Optional[Any]						    =  tf.concat([cur_b, self.cluster_bias]       ,  0						)

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

       if target is not None:
         if return_mean:
           _lowerCAmelCase		: Optional[int]						    =  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





"""simple docstring"""




from PIL import Image





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Image    ,_lowerCamelCase		: int			) ->   Image:
  _lowerCAmelCase		: Any						    =  (259 * (level + 255)) / (255 * (259 - level))

  def contrast(_lowerCamelCase		: int			) -> int:
    return int(128 + factor * (c - 128)			)

  return img.point(_lowerCamelCase			)


if __name__ == "__main__":
     # Load image
     with Image.open('image_data/lena.jpg') as img:
          # Change contrast to 170
          _a   :	str 					=     change_contrast(img, 170)
          cont_img.save('image_data/lena_high_contrast.png', format='png')




