'''simple docstring'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()







'''simple docstring'''

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 							lowercase						:


   def __init__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Any=14   ,		__lowerCAmelCase				:	Optional[Any]=7   ,		__lowerCAmelCase				:	List[str]=True   ,		__lowerCAmelCase				:	Optional[int]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Optional[Any]=True   ,		__lowerCAmelCase				:	Any=99   ,		__lowerCAmelCase				:	Tuple=32   ,		__lowerCAmelCase				:	Union[str, Any]=5   ,		__lowerCAmelCase				:	str=4   ,		__lowerCAmelCase				:	Dict=37   ,		__lowerCAmelCase				:	Optional[int]="gelu"   ,		__lowerCAmelCase				:	int=0.1   ,		__lowerCAmelCase				:	Tuple=0.1   ,		__lowerCAmelCase				:	str=512   ,		__lowerCAmelCase				:	Optional[Any]=16   ,		__lowerCAmelCase				:	Optional[int]=2   ,		__lowerCAmelCase				:	str=0.02   ,		__lowerCAmelCase				:	str=3   ,		__lowerCAmelCase				:	Tuple=4   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		)      ->					List[Any]:
         lowercase_     =      parent
         lowercase_     =      batch_size
         lowercase_     =      seq_length
         lowercase_     =      is_training
         lowercase_     =      use_token_type_ids
         lowercase_     =      use_input_mask
         lowercase_     =      use_labels
         lowercase_     =      use_mc_token_ids
         lowercase_     =      vocab_size
         lowercase_     =      hidden_size
         lowercase_     =      num_hidden_layers
         lowercase_     =      num_attention_heads
         lowercase_     =      intermediate_size
         lowercase_     =      hidden_act
         lowercase_     =      hidden_dropout_prob
         lowercase_     =      attention_probs_dropout_prob
         lowercase_     =      max_position_embeddings
         lowercase_     =      type_vocab_size
         lowercase_     =      type_sequence_label_size
         lowercase_     =      initializer_range
         lowercase_     =      num_labels
         lowercase_     =      num_choices
         lowercase_     =      scope
         lowercase_     =      self.vocab_size - 1


   def __UpperCAmelCase       (   self				:	Tuple)      ->					int:
         lowercase_     =      ids_tensor([self.batch_size, self.seq_length]   ,		self.vocab_size)

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

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

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

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

         lowercase_     =      self.get_config()

         lowercase_     =      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				:	Union[str, Any])      ->					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				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Tuple   ,		__lowerCAmelCase				:	Union[str, Any]   ,		*__lowerCAmelCase				:	Tuple)      ->					List[str]:
         lowercase_     =      CTRLModel(config=__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		head_mask=__lowerCAmelCase)
         model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase)
         lowercase_     =      model(__lowerCAmelCase)
         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]   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Dict   ,		*__lowerCAmelCase				:	int)      ->					int:
         lowercase_     =      CTRLLMHeadModel(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()

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


   def __UpperCAmelCase       (   self				:	Dict)      ->					int:
         lowercase_     =      self.prepare_config_and_inputs()

         (
             (
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							(
             lowercase_
         ) ,							
         )     =      config_and_inputs

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

         return config, inputs_dict


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Optional[int]   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	List[str]   ,		*__lowerCAmelCase				:	List[Any])      ->					int:
         lowercase_     =      self.num_labels
         lowercase_     =      CTRLForSequenceClassification(__lowerCAmelCase)
         model.to(__lowerCAmelCase)
         model.eval()
         lowercase_     =      ids_tensor([self.batch_size]   ,		self.type_sequence_label_size)
         lowercase_     =      model(__lowerCAmelCase   ,		token_type_ids=__lowerCAmelCase   ,		labels=__lowerCAmelCase)
         self.parent.assertEqual(result.logits.shape   ,		(self.batch_size, self.num_labels))





@require_torch
class 							lowercase						(						__lowerCamelCase ,				__lowerCamelCase ,				__lowerCamelCase ,				unittest.TestCase ):
   lowerCamelCase_    					=(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   lowerCamelCase_    					=(CTRLLMHeadModel,) if is_torch_available() else ()
   lowerCamelCase_    					=(
       {
           'feature-extraction': CTRLModel,
           'text-classification': CTRLForSequenceClassification,
           'text-generation': CTRLLMHeadModel,
           'zero-shot': CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_    					=True
   lowerCamelCase_    					=False
   lowerCamelCase_    					=False


   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	int)      ->					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				:	str)      ->					Tuple:
         lowercase_     =      CTRLModelTester(self)
         lowercase_     =      ConfigTester(self   ,		config_class=__lowerCAmelCase   ,		n_embd=37)


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


   def __UpperCAmelCase       (   self				:	str)      ->					List[Any]:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase       (   self				:	Any)      ->					List[Any]:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_ctrl_model(*__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Tuple)      ->					Dict:
         lowercase_     =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase)


   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					Optional[Any]:
         pass


   @slow
   def __UpperCAmelCase       (   self				:	Dict)      ->					List[str]:
         for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     =      CTRLModel.from_pretrained(__lowerCAmelCase)
               self.assertIsNotNone(__lowerCAmelCase)


   @unittest.skip("The model doesn't support left padding")  # and it's not used enough to be worth fixing :)
   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Dict:
         pass





@require_torch
class 							lowercase						(						unittest.TestCase ):


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


   @slow
   def __UpperCAmelCase       (   self				:	int)      ->					Any:
         lowercase_     =      CTRLLMHeadModel.from_pretrained("ctrl")
         model.to(__lowerCAmelCase)
         lowercase_     =      torch.tensor(
             [[1_1859, 0, 1611, 8]]   ,		dtype=torch.long   ,		device=__lowerCAmelCase)  # Legal the president is
         lowercase_     =      [
             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

         lowercase_     =      model.generate(__lowerCAmelCase   ,		do_sample=__lowerCAmelCase)
         self.assertListEqual(output_ids[0].tolist()   ,		__lowerCAmelCase)






