


'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.ctrl.modeling_tf_ctrl import (
         TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFCTRLForSequenceClassification,
         TFCTRLLMHeadModel,
         TFCTRLModel,
     )





class  snake_case   (   lowercase      ):





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  parent
   lowerCamelCase_								=  13
   lowerCamelCase_								=  7
   lowerCamelCase_								=  True
   lowerCamelCase_								=  True
   lowerCamelCase_								=  True
   lowerCamelCase_								=  True
   lowerCamelCase_								=  True
   lowerCamelCase_								=  99
   lowerCamelCase_								=  32
   lowerCamelCase_								=  2
   lowerCamelCase_								=  4
   lowerCamelCase_								=  37
   lowerCamelCase_								=  "gelu"
   lowerCamelCase_								=  0.1
   lowerCamelCase_								=  0.1
   lowerCamelCase_								=  512
   lowerCamelCase_								=  16
   lowerCamelCase_								=  2
   lowerCamelCase_								=  0.02
   lowerCamelCase_								=  3
   lowerCamelCase_								=  4
   lowerCamelCase_								=  None
   lowerCamelCase_								=  self.vocab_size - 1






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size					)

   lowerCamelCase_								=  None
   if self.use_input_mask:
    lowerCamelCase_								=  random_attention_mask([self.batch_size, self.seq_length]					)

   lowerCamelCase_								=  None
   if self.use_token_type_ids:
    lowerCamelCase_								=  ids_tensor([self.batch_size, self.seq_length]      ,  self.type_vocab_size					)

   lowerCamelCase_								=  None
   if self.use_mc_token_ids:
    lowerCamelCase_								=  ids_tensor([self.batch_size, self.num_choices]      ,  self.seq_length					)

   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   if self.use_labels:
    lowerCamelCase_								=  ids_tensor([self.batch_size]      ,  self.type_sequence_label_size					)
    lowerCamelCase_								=  ids_tensor([self.batch_size, self.seq_length]      ,  self.num_labels					)
    lowerCamelCase_								=  ids_tensor([self.batch_size]      ,  self.num_choices					)

   lowerCamelCase_								=  CTRLConfig(
       vocab_size=self.vocab_size      ,  n_embd=self.hidden_size      ,  n_layer=self.num_hidden_layers      ,  n_head=self.num_attention_heads      ,  n_positions=self.max_position_embeddings      ,  pad_token_id=self.pad_token_id      ,  )

   lowerCamelCase_								=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]      ,  2					)

   return (
       config,
       input_ids,
       input_mask,
       head_mask,
       token_type_ids,
       mc_token_ids,
       sequence_labels,
       token_labels,
       choice_labels,
   )






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  *UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  TFCTRLModel(config=UpperCamelCase					)
   lowerCamelCase_								=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
   lowerCamelCase_								=  model(UpperCamelCase					)

   lowerCamelCase_								=  [input_ids, None, input_mask]  # None is the input for 'past'
   lowerCamelCase_								=  model(UpperCamelCase					)

   lowerCamelCase_								=  model(UpperCamelCase					)

   self.parent.assertEqual(result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length, self.hidden_size)					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  *UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  TFCTRLLMHeadModel(config=UpperCamelCase					)
   lowerCamelCase_								=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
   lowerCamelCase_								=  model(UpperCamelCase					)
   self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.seq_length, self.vocab_size)					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  *UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  self.num_labels
   lowerCamelCase_								=  ids_tensor([self.batch_size]      ,  self.type_sequence_label_size					)
   lowerCamelCase_								=  {
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "labels": sequence_labels,
   }
   lowerCamelCase_								=  TFCTRLForSequenceClassification(UpperCamelCase					)
   lowerCamelCase_								=  model(UpperCamelCase					)
   self.parent.assertEqual(result.logits.shape      ,  (self.batch_size, self.num_labels)					)







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.prepare_config_and_inputs()

   (
       (
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,(
       lowerCamelCase_
   )			,
   )								=  config_and_inputs

   lowerCamelCase_								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
   return config, inputs_dict





@require_tf
class  snake_case   (   lowercase      ,						lowercase      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
  _lowerCamelCase		   =  (TFCTRLLMHeadModel,) if is_tf_available() else ()
  _lowerCamelCase		   =  (
      {
          "feature-extraction": TFCTRLModel,
          "text-classification": TFCTRLForSequenceClassification,
          "text-generation": TFCTRLLMHeadModel,
          "zero-shot": TFCTRLForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  _lowerCamelCase		   =  False
  _lowerCamelCase		   =  False






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
    # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
    # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
    # config could not be created.
    return True

   return False






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  TFCTRLModelTester(self					)
   lowerCamelCase_								=  ConfigTester(self      ,  config_class=UpperCamelCase      ,  n_embd=37					)






  def   snake_case		(   self					):







   """simple docstring"""
   self.config_tester.run_common_tests()






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_model(*UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_			,lowerCamelCase_								=  self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_								=  [TFCTRLLMHeadModel]
   lowerCamelCase_								=  [TFCTRLForSequenceClassification]

   for model_class in self.all_model_classes:
    lowerCamelCase_								=  model_class(UpperCamelCase					)
    model.build()  # may be needed for the get_bias() call below
    assert isinstance(model.get_input_embeddings()      ,  tf.keras.layers.Layer					)

    if model_class in list_lm_models:
     lowerCamelCase_								=  model.get_output_embeddings()
     assert isinstance(UpperCamelCase      ,  tf.keras.layers.Layer					)
     lowerCamelCase_								=  model.get_bias()
     assert isinstance(UpperCamelCase      ,  UpperCamelCase					)
     for k, v in name.items():
      assert isinstance(UpperCamelCase      ,  tf.Variable					)
    elif model_class in list_other_models_with_output_ebd:
     lowerCamelCase_								=  model.get_output_embeddings()
     assert isinstance(UpperCamelCase      ,  tf.keras.layers.Layer					)
     lowerCamelCase_								=  model.get_bias()
     assert name is None
    else:
     lowerCamelCase_								=  model.get_output_embeddings()
     assert x is None
     lowerCamelCase_								=  model.get_bias()
     assert name is None







  @slow
  def   snake_case		(   self					):







   """simple docstring"""
   for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    lowerCamelCase_								=  TFCTRLModel.from_pretrained(UpperCamelCase					)
    self.assertIsNotNone(UpperCamelCase					)





@require_tf
class  snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  @slow
  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  TFCTRLLMHeadModel.from_pretrained("ctrl"					)
   lowerCamelCase_								=  tf.convert_to_tensor([[1_1859, 0, 1611, 8]]      ,  dtype=tf.intaa					)  # Legal the president is
   lowerCamelCase_								=  [
       1_1859,
       0,
       1611,
       8,
       5,
       150,
       2_6449,
       2,
       19,
       348,
       469,
       3,
       2595,
       48,
       2_0740,
       24_6533,
       24_6533,
       19,
       30,
       5,
   ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

   lowerCamelCase_								=  model.generate(UpperCamelCase      ,  do_sample=UpperCamelCase					)
   self.assertListEqual(output_ids[0].numpy().tolist()      ,  UpperCamelCase					)







'''simple docstring'''

class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  data
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None





def __snake_case (				UpperCAmelCase_						:						BinaryTreeNode | None ,    UpperCAmelCase_						:						int		):
 if node is None:
  lowerCamelCase_								=  BinaryTreeNode(UpperCAmelCase_		)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase_								=  insert(node.left_child ,    UpperCAmelCase_		)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase_								=  insert(node.right_child ,    UpperCAmelCase_		)
 return node





def __snake_case (				UpperCAmelCase_						:						None | BinaryTreeNode		):  # if node is None,return
 if node:
  lowerCamelCase_								=  inorder(node.left_child		)
  lowerCamelCase_								=  [*inorder_array, node.data]
  lowerCamelCase_								=  inorder_array + inorder(node.right_child		)
 else:
  lowerCamelCase_								=  []
 return inorder_array





def __snake_case (				):
 lowerCamelCase_								=  insert(UpperCAmelCase_ ,    15		)
 insert(UpperCAmelCase_ ,    10		)
 insert(UpperCAmelCase_ ,    25		)
 insert(UpperCAmelCase_ ,    6		)
 insert(UpperCAmelCase_ ,    14		)
 insert(UpperCAmelCase_ ,    20		)
 insert(UpperCAmelCase_ ,    60		)
 return root





def __snake_case (				):
 # main function
 lowerCamelCase_								=  make_tree()
 print("Printing values of binary search tree in Inorder Traversal."		)
 inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



