'''simple docstring'''






class lowercase_       :







   """simple docstring"""

   def __init__(      self      :			Optional[int]     )  ->			None:
        _A    		=  {}  # Mapping from char to TrieNode
        _A    		=  False
   def 	__UpperCAmelCase   (      self      :			List[str], UpperCamelCase__      :			list[str]     )  ->			None:
        for word in words:
             self.insert(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int], UpperCamelCase__      :			str     )  ->			None:
        _A    		=  self
        for char in word:
             if char not in curr.nodes:
                  _A    		=  TrieNode()
             _A    		=  curr.nodes[char]
        _A    		=  True
   def 	__UpperCAmelCase   (      self      :			str, UpperCamelCase__      :			str     )  ->			bool:
        _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      :			Union[str, Any], UpperCamelCase__      :			str     )  ->			None:

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

        _delete(self, UpperCamelCase__, 0     )






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			TrieNode   ,		__snake_case							:			str				):
     if node.is_leaf:
          print(__snake_case   ,		end=' '				)

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





def  _SCREAMING_SNAKE_CASE		(      ):
     _A    		=  'banana bananas bandana band apple all beast'.split()
     _A    		=  TrieNode()
     root.insert_many(__snake_case				)
     # print_words(root, "")
     assert all(root.find(__snake_case				) 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  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			bool				):
     print(str(__snake_case				)   ,		'works!' if passes else 'doesn\'t work :('				)





def  _SCREAMING_SNAKE_CASE		(      ):
     assert test_trie()





def  _SCREAMING_SNAKE_CASE		(      ):
     print_results('Testing trie functionality'   ,		test_trie()				)


if __name__ == "__main__":
   main()




'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
   import torch







@is_pipeline_test
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

        _A    		=  generator(['This is great !', 'Something else'], num_return_sequences=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        _A    		=  generator(
            ['This is great !', 'Something else'], num_return_sequences=2, batch_size=2, do_sample=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
                [{'generated_text': ANY(UpperCamelCase__     )}, {'generated_text': ANY(UpperCamelCase__     )}],
            ], )

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
            {'generated_text': ''},
        ]
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )

        _A    		=  generator('This is a test', do_sample=UpperCamelCase__, num_return_sequences=2, return_tensors=UpperCamelCase__     )
        self.assertEqual(
            UpperCamelCase__, [
                {'generated_token_ids': ANY(torch.Tensor     )},
                {'generated_token_ids': ANY(torch.Tensor     )},
            ], )
        _A    		=  generator.model.config.eos_token_id
        _A    		=  '<pad>'
        _A    		=  generator(
            ['This is a test', 'This is a second test'], do_sample=UpperCamelCase__, num_return_sequences=2, batch_size=2, return_tensors=UpperCamelCase__, )
        self.assertEqual(
            UpperCamelCase__, [
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
                [
                    {'generated_token_ids': ANY(torch.Tensor     )},
                    {'generated_token_ids': ANY(torch.Tensor     )},
                ],
            ], )

   @require_tf
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='tf'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )



