import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      CTRLForSequenceClassification,
      CTRLLMHeadModel,
      CTRLModel,
  )
class    A_ :

  def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]=14     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=7     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      int=True     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=99     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=32     ,							__SCREAMING_SNAKE_CASE :      Tuple=5     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=4     ,							__SCREAMING_SNAKE_CASE :      List[Any]=37     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]="gelu"     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=0.1     ,							__SCREAMING_SNAKE_CASE :      Dict=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Dict=16     ,							__SCREAMING_SNAKE_CASE :      Tuple=2     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=3     ,							__SCREAMING_SNAKE_CASE :      int=4     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  seq_length
   __a       =  is_training
   __a       =  use_token_type_ids
   __a       =  use_input_mask
   __a       =  use_labels
   __a       =  use_mc_token_ids
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  intermediate_size
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  type_sequence_label_size
   __a       =  initializer_range
   __a       =  num_labels
   __a       =  num_choices
   __a       =  scope
   __a       =  self.vocab_size - 1

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size       )

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

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

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

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

   __a       =  self.get_config()

   __a       =  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 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return 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     ,							)

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							*__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  CTRLModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   model(__SCREAMING_SNAKE_CASE     ,							token_type_ids=__SCREAMING_SNAKE_CASE     ,							head_mask=__SCREAMING_SNAKE_CASE       )
   model(__SCREAMING_SNAKE_CASE     ,							token_type_ids=__SCREAMING_SNAKE_CASE       )
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )
   self.parent.assertEqual(len(result.past_key_values       )     ,							config.n_layer       )

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							*__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  CTRLLMHeadModel(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()

   __a       =  model(__SCREAMING_SNAKE_CASE     ,							token_type_ids=__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.loss.shape     ,							()       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  self.prepare_config_and_inputs()

   (
       (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     (
       __a
   )       ,     
   )       =  config_and_inputs

   __a       =  {"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

   return config, inputs_dict

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							*__SCREAMING_SNAKE_CASE :      Tuple       ):
   __a       =  self.num_labels
   __a       =  CTRLForSequenceClassification(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							token_type_ids=__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)       )
@require_torch
class    A_ (       a_			,       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			(CTRLLMHeadModel,) if is_torch_available() else ()
  _SCREAMING_SNAKE_CASE    				=			(
      {
          """feature-extraction""": CTRLModel,
          """text-classification""": CTRLForSequenceClassification,
          """text-generation""": CTRLLMHeadModel,
          """zero-shot""": CTRLForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   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 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  CTRLModelTester(self       )
   __a       =  ConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE     ,							n_embd=37       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   super().tearDown()
   # clean-up as much as possible GPU memory occupied by PyTorch
   gc.collect()
   torch.cuda.empty_cache()

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_ctrl_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_lm_head_model(*__SCREAMING_SNAKE_CASE       )

  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   pass

  @slow
  def 		_UpperCAmelCase   (		self :      str       ):
   for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  CTRLModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )

  @unittest.skip("The model doesn't support left padding"       )  # and it's not used enough to be worth fixing :)
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass
@require_torch
class    A_ (       unittest.TestCase ):

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   super().tearDown()
   # clean-up as much as possible GPU memory occupied by PyTorch
   gc.collect()
   torch.cuda.empty_cache()

  @slow
  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  CTRLLMHeadModel.from_pretrained("ctrl"       )
   model.to(__SCREAMING_SNAKE_CASE       )
   __a       =  torch.tensor(
       [[1_18_59, 0, 16_11, 8]]     ,							dtype=torch.long     ,							device=__SCREAMING_SNAKE_CASE       )  # Legal the president is
   __a       =  [
       1_18_59,
       0,
       16_11,
       8,
       5,
       1_50,
       2_64_49,
       2,
       19,
       3_48,
       4_69,
       3,
       25_95,
       48,
       2_07_40,
       24_65_33,
       24_65_33,
       19,
       30,
       5,
   ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

   __a       =  model.generate(__SCREAMING_SNAKE_CASE     ,							do_sample=__SCREAMING_SNAKE_CASE       )
   self.assertListEqual(output_ids[0].tolist()     ,							__SCREAMING_SNAKE_CASE       )



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


