






import tensorflow as tf

from ...tf_utils import shape_list


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






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=1		,		_UpperCAmelCase=False		,		**_UpperCAmelCase )			-> Dict:
     super().__init__(**_UpperCAmelCase )

     UpperCamelCase_									=     vocab_size
     UpperCamelCase_									=     d_embed
     UpperCamelCase_									=     d_proj

     UpperCamelCase_									=     cutoffs + [vocab_size]
     UpperCamelCase_									=     [0] + self.cutoffs
     UpperCamelCase_									=     div_val

     UpperCamelCase_									=     self.cutoffs[0]
     UpperCamelCase_									=     len(self.cutoffs ) - 1
     UpperCamelCase_									=     self.shortlist_size + self.n_clusters
     UpperCamelCase_									=     keep_order

     UpperCamelCase_									=     []
     UpperCamelCase_									=     []




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> str:
     if self.n_clusters > 0:
        UpperCamelCase_									=     self.add_weight(
            shape=(self.n_clusters, self.d_embed)		,		initializer='zeros'		,		trainable=_UpperCAmelCase		,		name='cluster_weight' )
        UpperCamelCase_									=     self.add_weight(
            shape=(self.n_clusters,)		,		initializer='zeros'		,		trainable=_UpperCAmelCase		,		name='cluster_bias' )

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

           UpperCamelCase_									=     self.add_weight(
               shape=(d_emb_i, self.d_proj)		,		initializer='zeros'		,		trainable=_UpperCAmelCase		,		name=f"""out_projs_._{i}""" )
           self.out_projs.append(_UpperCAmelCase )
           UpperCamelCase_									=     self.add_weight(
               shape=(r_idx - l_idx, d_emb_i)		,		initializer='zeros'		,		trainable=_UpperCAmelCase		,		name=f"""out_layers_._{i}_._weight"""		,		)
           UpperCamelCase_									=     self.add_weight(
               shape=(r_idx - l_idx,)		,		initializer='zeros'		,		trainable=_UpperCAmelCase		,		name=f"""out_layers_._{i}_._bias"""		,		)
           self.out_layers.append((weight, bias) )
     super().build(_UpperCAmelCase )




  @staticmethod
  def    _UpperCAmelCase     (     _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=None )			-> Optional[Any]:
     UpperCamelCase_									=     x
     if proj is not None:
        UpperCamelCase_									=     tf.einsum('ibd,ed->ibe'		,		_UpperCAmelCase		,		_UpperCAmelCase )
     return tf.einsum('ibd,nd->ibn'		,		_UpperCAmelCase		,		_UpperCAmelCase ) + b




  @staticmethod
  def    _UpperCAmelCase     (     _UpperCAmelCase		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     shape_list(_UpperCAmelCase )
     UpperCamelCase_									=     tf.range(lp_size[0]		,		dtype=target.dtype )
     UpperCamelCase_									=     tf.stack([r, target]		,		1 )
     return tf.gather_nd(_UpperCAmelCase		,		_UpperCAmelCase )




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

           if self.div_val == 1:
              UpperCamelCase_									=     self.out_layers[0][0][l_idx:r_idx]
              UpperCamelCase_									=     self.out_layers[0][1][l_idx:r_idx]
           else:
              UpperCamelCase_									=     self.out_layers[i][0]
              UpperCamelCase_									=     self.out_layers[i][1]

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

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

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

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

     return out













import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


snake_case__      :	Optional[int]       	=      pytest.mark.integration






@pytest.mark.parametrize('path'    ,      ['paws', 'csv'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_dataset(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning')
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning')
@pytest.mark.parametrize('path'    ,      ['accuracy'])
def     _snake_case    (__lowercase    ,      __lowercase):
   inspect_metric(__lowercase    ,      __lowercase)
   UpperCamelCase_									=     path + '.py'
   assert script_name in os.listdir(__lowercase)
   assert "__pycache__" not in os.listdir(__lowercase)






@pytest.mark.parametrize(
    'path, config_name, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_info(__lowercase    ,      config_name=__lowercase)
   assert info.config_name == config_name
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_config_info(__lowercase    ,      config_name=__lowercase)






@pytest.mark.parametrize(
    'path, expected'    ,      [
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_config_names(__lowercase)
   assert expected in config_names






@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'    ,      [
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert list(infos.keys()) == expected_configs
   UpperCamelCase_									=     expected_configs[0]
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits_in_first_config






@pytest.mark.parametrize(
    'path, expected_config, expected_splits'    ,      [
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   UpperCamelCase_									=     get_dataset_infos(__lowercase)
   assert expected_config in infos
   UpperCamelCase_									=     infos[expected_config]
   assert info.config_name == expected_config
   assert list(info.splits.keys()) == expected_splits






@pytest.mark.parametrize(
    'path, config_name, expected_exception'    ,      [
        ('paws', None, ValueError),
    ]    ,      )
def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   with pytest.raises(__lowercase):
      get_dataset_split_names(__lowercase    ,      config_name=__lowercase)





