"""simple docstring"""







import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class 	UpperCamelCase    (					unittest.TestCase		):
				UpperCAmelCase       :   Union[str, Any]					=	MODEL_FOR_MASKED_LM_MAPPING
				UpperCAmelCase       :   int					=	TF_MODEL_FOR_MASKED_LM_MAPPING



				def       _lowercase  (self       :   List[Any])					->			str:
					super().tearDown()
					# clean-up as much as possible GPU memory occupied by PyTorch
					gc.collect()
					if is_torch_available():
						import torch

						torch.cuda.empty_cache()



				@require_tf
				def       _lowercase  (self       :   Dict)					->			Union[str, Any]:
					__snake_case    :							int   	=				pipeline(task='fill-mask'			,						model='sshleifer/tiny-distilroberta-base'			,						top_k=2			,						framework='tf')
					__snake_case    :							Optional[int]   	=				unmasker('My name is <mask>')
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {'sequence': 'My name is grouped', 'score': 2.1E-05, 'token': 3_80_15, 'token_str': ' grouped'},
					        {'sequence': 'My name is accuser', 'score': 2.1E-05, 'token': 2_55_06, 'token_str': ' accuser'},
					    ]			,						)

					__snake_case    :							Union[str, Any]   	=				unmasker('The largest city in France is <mask>')
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {
					            'sequence': 'The largest city in France is grouped',
					            'score': 2.1E-05,
					            'token': 3_80_15,
					            'token_str': ' grouped',
					        },
					        {
					            'sequence': 'The largest city in France is accuser',
					            'score': 2.1E-05,
					            'token': 2_55_06,
					            'token_str': ' accuser',
					        },
					    ]			,						)

					__snake_case    :							List[str]   	=				unmasker('My name is <mask>'			,						targets=[' Patrick', ' Clara', ' Teven']			,						top_k=3)
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 1_36_06, 'token_str': ' Clara'},
					        {'sequence': 'My name is Patrick', 'score': 2E-05, 'token': 34_99, 'token_str': ' Patrick'},
					        {'sequence': 'My name is Te', 'score': 1.9E-05, 'token': 29_41, 'token_str': ' Te'},
					    ]			,						)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					__snake_case    :							Tuple   	=				pipeline(task='fill-mask'			,						model='sshleifer/tiny-distilroberta-base'			,						top_k=2			,						framework='pt')

					__snake_case    :							int   	=				unmasker('My name is <mask>')
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {'sequence': 'My name is Maul', 'score': 2.2E-05, 'token': 3_56_76, 'token_str': ' Maul'},
					        {'sequence': 'My name isELS', 'score': 2.2E-05, 'token': 1_64_16, 'token_str': 'ELS'},
					    ]			,						)

					__snake_case    :							Optional[Any]   	=				unmasker('The largest city in France is <mask>')
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {
					            'sequence': 'The largest city in France is Maul',
					            'score': 2.2E-05,
					            'token': 3_56_76,
					            'token_str': ' Maul',
					        },
					        {'sequence': 'The largest city in France isELS', 'score': 2.2E-05, 'token': 1_64_16, 'token_str': 'ELS'},
					    ]			,						)

					__snake_case    :							List[Any]   	=				unmasker('My name is <mask>'			,						targets=[' Patrick', ' Clara', ' Teven']			,						top_k=3)
					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        {'sequence': 'My name is Patrick', 'score': 2.1E-05, 'token': 34_99, 'token_str': ' Patrick'},
					        {'sequence': 'My name is Te', 'score': 2E-05, 'token': 29_41, 'token_str': ' Te'},
					        {'sequence': 'My name is Clara', 'score': 2E-05, 'token': 1_36_06, 'token_str': ' Clara'},
					    ]			,						)

					__snake_case    :							str   	=				unmasker('My name is <mask> <mask>'			,						top_k=2)

					self.assertEqual(
					    nested_simplify(_A			,						decimals=6)			,						[
					        [
					            {
					                'score': 2.2E-05,
					                'token': 3_56_76,
					                'token_str': ' Maul',
					                'sequence': '<s>My name is Maul<mask></s>',
					            },
					            {'score': 2.2E-05, 'token': 1_64_16, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
					        ],
					        [
					            {
					                'score': 2.2E-05,
					                'token': 3_56_76,
					                'token_str': ' Maul',
					                'sequence': '<s>My name is<mask> Maul</s>',
					            },
					            {'score': 2.2E-05, 'token': 1_64_16, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
					        ],
					    ]			,						)



				@require_torch_gpu
				def       _lowercase  (self       :   Dict)					->			Optional[Any]:
					__snake_case    :							Union[str, Any]   	=				pipeline('fill-mask'			,						model='hf-internal-testing/tiny-random-distilbert'			,						device=0			,						framework='pt')

					# convert model to fp16
					pipe.model.half()

					__snake_case    :							List[str]   	=				pipe('Paris is the [MASK] of France.')
					# We actually don't care about the result, we just want to make sure
					# it works, meaning the float16 tensor got casted back to float32
					# for postprocessing.
					self.assertIsInstance(_A			,						_A)



				@slow
				@require_torch
				def       _lowercase  (self       :   Dict)					->			Any:
					__snake_case    :							Tuple   	=				pipeline(task='fill-mask'			,						model='distilroberta-base'			,						top_k=2			,						framework='pt')
					self.run_large_test(_A)



				@slow
				@require_tf
				def       _lowercase  (self       :   Union[str, Any])					->			List[Any]:
					__snake_case    :							Optional[int]   	=				pipeline(task='fill-mask'			,						model='distilroberta-base'			,						top_k=2			,						framework='tf')
					self.run_large_test(_A)



				def       _lowercase  (self       :   Optional[int]			,						_A       :   Any)					->			Union[str, Any]:
					__snake_case    :							Dict   	=				unmasker('My name is <mask>')
					self.assertEqual(
					    nested_simplify(_A)			,						[
					        {'sequence': 'My name is John', 'score': 0.008, 'token': 6_10, 'token_str': ' John'},
					        {'sequence': 'My name is Chris', 'score': 0.007, 'token': 15_73, 'token_str': ' Chris'},
					    ]			,						)
					__snake_case    :							int   	=				unmasker('The largest city in France is <mask>')
					self.assertEqual(
					    nested_simplify(_A)			,						[
					        {
					            'sequence': 'The largest city in France is Paris',
					            'score': 0.251,
					            'token': 22_01,
					            'token_str': ' Paris',
					        },
					        {
					            'sequence': 'The largest city in France is Lyon',
					            'score': 0.214,
					            'token': 1_27_90,
					            'token_str': ' Lyon',
					        },
					    ]			,						)

					__snake_case    :							str   	=				unmasker('My name is <mask>'			,						targets=[' Patrick', ' Clara', ' Teven']			,						top_k=3)
					self.assertEqual(
					    nested_simplify(_A)			,						[
					        {'sequence': 'My name is Patrick', 'score': 0.005, 'token': 34_99, 'token_str': ' Patrick'},
					        {'sequence': 'My name is Clara', 'score': 0.000, 'token': 1_36_06, 'token_str': ' Clara'},
					        {'sequence': 'My name is Te', 'score': 0.000, 'token': 29_41, 'token_str': ' Te'},
					    ]			,						)



				@require_torch
				def       _lowercase  (self       :   List[Any])					->			Optional[int]:
					__snake_case    :							Union[str, Any]   	=				pipeline(task='fill-mask'			,						model='sshleifer/tiny-distilroberta-base'			,						framework='pt')
					__snake_case    :							List[str]   	=				None
					__snake_case    :							int   	=				None
					self.run_pipeline_test(_A			,						[])



				@require_tf
				def       _lowercase  (self       :   List[Any])					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				pipeline(task='fill-mask'			,						model='sshleifer/tiny-distilroberta-base'			,						framework='tf')
					__snake_case    :							List[Any]   	=				None
					__snake_case    :							Optional[Any]   	=				None
					self.run_pipeline_test(_A			,						[])



				def       _lowercase  (self       :   Optional[int]			,						_A       :   int			,						_A       :   List[str]			,						_A       :   Union[str, Any])					->			Dict:
					if tokenizer is None or tokenizer.mask_token_id is None:
						self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)')

					__snake_case    :							Optional[Any]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)
					__snake_case    :							Optional[int]   	=				[
					    f"This is another {tokenizer.mask_token} test",
					]
					return fill_masker, examples



				def       _lowercase  (self       :   Any			,						_A       :   str			,						_A       :   Optional[Any])					->			Tuple:
					__snake_case    :							int   	=				fill_masker.tokenizer
					__snake_case    :							List[Any]   	=				fill_masker.model

					__snake_case    :							Optional[Any]   	=				fill_masker(
					    f"This is a {tokenizer.mask_token}"			,						)
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)

					__snake_case    :							Any   	=				fill_masker([f"This is a {tokenizer.mask_token}"])
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)

					__snake_case    :							Optional[int]   	=				fill_masker([f"This is a {tokenizer.mask_token}", f"Another {tokenizer.mask_token} great test."])
					self.assertEqual(
					    _A			,						[
					        [
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        ],
					        [
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        ],
					    ]			,						)

					with self.assertRaises(_A):
						fill_masker([None])
					# No mask_token is not supported
					with self.assertRaises(_A):
						fill_masker('This is')

					self.run_test_top_k(_A			,						_A)
					self.run_test_targets(_A			,						_A)
					self.run_test_top_k_targets(_A			,						_A)
					self.fill_mask_with_duplicate_targets_and_top_k(_A			,						_A)
					self.fill_mask_with_multiple_masks(_A			,						_A)



				def       _lowercase  (self       :   str			,						_A       :   int			,						_A       :   int)					->			Any:
					__snake_case    :							List[Any]   	=				tokenizer.get_vocab()
					__snake_case    :							Optional[Any]   	=				sorted(vocab.keys())[:2]
					# Pipeline argument
					__snake_case    :							str   	=				FillMaskPipeline(model=_A			,						tokenizer=_A			,						targets=_A)
					__snake_case    :							str   	=				fill_masker(f"This is a {tokenizer.mask_token}")
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)
					__snake_case    :							Union[str, Any]   	=				{vocab[el] for el in targets}
					self.assertEqual({el['token'] for el in outputs}			,						_A)
					__snake_case    :							str   	=				[tokenizer.decode([x]) for x in target_ids]
					self.assertEqual({el['token_str'] for el in outputs}			,						set(_A))

					# Call argument
					__snake_case    :							Dict   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)
					__snake_case    :							Optional[int]   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets=_A)
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)
					__snake_case    :							List[Any]   	=				{vocab[el] for el in targets}
					self.assertEqual({el['token'] for el in outputs}			,						_A)
					__snake_case    :							Optional[Any]   	=				[tokenizer.decode([x]) for x in target_ids]
					self.assertEqual({el['token_str'] for el in outputs}			,						set(_A))

					# Score equivalence
					__snake_case    :							Union[str, Any]   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets=_A)
					__snake_case    :							int   	=				[top_mask['token_str'] for top_mask in outputs]
					__snake_case    :							Tuple   	=				[top_mask['score'] for top_mask in outputs]

					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(_A) == set(_A):
						__snake_case    :							int   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets=_A)
						__snake_case    :							Tuple   	=				[top_mask['score'] for top_mask in unmasked_targets]
						self.assertEqual(nested_simplify(_A)			,						nested_simplify(_A))

					# Raises with invalid
					with self.assertRaises(_A):
						__snake_case    :							List[str]   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets=[])
					# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
					if "" not in tokenizer.get_vocab():
						with self.assertRaises(_A):
							__snake_case    :							List[str]   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets=[''])
						with self.assertRaises(_A):
							__snake_case    :							Any   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						targets='')



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   Dict			,						_A       :   Tuple)					->			Any:
					__snake_case    :							List[str]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A			,						top_k=2)
					__snake_case    :							str   	=				fill_masker(f"This is a {tokenizer.mask_token}")
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)

					__snake_case    :							Optional[int]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)
					__snake_case    :							str   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						top_k=2)
					self.assertEqual(
					    _A			,						[
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					    ]			,						)
					self.assertEqual(nested_simplify(_A)			,						nested_simplify(_A))



				def       _lowercase  (self       :   Tuple			,						_A       :   Optional[int]			,						_A       :   Any)					->			Tuple:
					__snake_case    :							List[Any]   	=				tokenizer.get_vocab()
					__snake_case    :							List[str]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)

					# top_k=2, ntargets=3
					__snake_case    :							Optional[int]   	=				sorted(vocab.keys())[:3]
					__snake_case    :							Dict   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						top_k=2			,						targets=_A)

					# If we use the most probably targets, and filter differently, we should still
					# have the same results
					__snake_case    :							Any   	=				[el['token_str'] for el in sorted(_A			,						key=lambda _A: x["score"]			,						reverse=_A)]
					# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
					if set(_A).issubset(_A):
						__snake_case    :							Union[str, Any]   	=				fill_masker(f"This is a {tokenizer.mask_token}"			,						top_k=3			,						targets=_A)
						# They should yield exactly the same result
						self.assertEqual(nested_simplify(_A)			,						nested_simplify(_A))



				def       _lowercase  (self       :   List[Any]			,						_A       :   Optional[int]			,						_A       :   Dict)					->			int:
					__snake_case    :							Optional[int]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)
					__snake_case    :							List[Any]   	=				tokenizer.get_vocab()
					# String duplicates + id duplicates
					__snake_case    :							Any   	=				sorted(vocab.keys())[:3]
					__snake_case    :							Union[str, Any]   	=				[targets[0], targets[1], targets[0], targets[2], targets[1]]
					__snake_case    :							str   	=				fill_masker(f"My name is {tokenizer.mask_token}"			,						targets=_A			,						top_k=10)

					# The target list contains duplicates, so we can't output more
					# than them
					self.assertEqual(len(_A)			,						3)






				def       _lowercase  (self       :   Any			,						_A       :   Any			,						_A       :   Optional[int])					->			Any:
					__snake_case    :							Union[str, Any]   	=				FillMaskPipeline(model=_A			,						tokenizer=_A)

					__snake_case    :							int   	=				fill_masker(
					    f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"			,						top_k=2)
					self.assertEqual(
					    _A			,						[
					        [
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        ],
					        [
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        ],
					        [
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					            {'sequence': ANY(_A), 'score': ANY(_A), 'token': ANY(_A), 'token_str': ANY(_A)},
					        ],
					    ]			,						)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





