




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     ):

    '''simple docstring'''



    lowerCamelCase_      :	Optional[Any]	     =      MODEL_FOR_MASKED_LM_MAPPING
    lowerCamelCase_      :	List[Any]	     =      TF_MODEL_FOR_MASKED_LM_MAPPING







    def     _lowercase     (    self     )     ->      List[Any]:
           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     )     ->      List[str]:
           lowerCamelCase   :      str			 =					pipeline(task="fill-mask"     ,  model="sshleifer/tiny-distilroberta-base"     ,  top_k=2     ,  framework="tf"     )
           lowerCamelCase   :      Dict			 =					unmasker("My name is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {"sequence": "My name is grouped", "score": 2.1e-05, "token": 3_8015, "token_str": " grouped"},
                   {"sequence": "My name is accuser", "score": 2.1e-05, "token": 2_5506, "token_str": " accuser"},
               ]     ,  )

           lowerCamelCase   :      int			 =					unmasker("The largest city in France is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {
                       "sequence": "The largest city in France is grouped",
                       "score": 2.1e-05,
                       "token": 3_8015,
                       "token_str": " grouped",
                   },
                   {
                       "sequence": "The largest city in France is accuser",
                       "score": 2.1e-05,
                       "token": 2_5506,
                       "token_str": " accuser",
                   },
               ]     ,  )

           lowerCamelCase   :      List[str]			 =					unmasker("My name is <mask>"     ,  targets=[" Patrick", " Clara", " Teven"]     ,  top_k=3     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {"sequence": "My name is Clara", "score": 2e-05, "token": 1_3606, "token_str": " Clara"},
                   {"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
                   {"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
               ]     ,  )







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

           lowerCamelCase   :      Tuple			 =					unmasker("My name is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {"sequence": "My name is Maul", "score": 2.2e-05, "token": 3_5676, "token_str": " Maul"},
                   {"sequence": "My name isELS", "score": 2.2e-05, "token": 1_6416, "token_str": "ELS"},
               ]     ,  )

           lowerCamelCase   :      Union[str, Any]			 =					unmasker("The largest city in France is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {
                       "sequence": "The largest city in France is Maul",
                       "score": 2.2e-05,
                       "token": 3_5676,
                       "token_str": " Maul",
                   },
                   {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 1_6416, "token_str": "ELS"},
               ]     ,  )

           lowerCamelCase   :      int			 =					unmasker("My name is <mask>"     ,  targets=[" Patrick", " Clara", " Teven"]     ,  top_k=3     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
                   {"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
                   {"sequence": "My name is Clara", "score": 2e-05, "token": 1_3606, "token_str": " Clara"},
               ]     ,  )

           lowerCamelCase   :      int			 =					unmasker("My name is <mask> <mask>"     ,  top_k=2     )

           self.assertEqual(
               nested_simplify(UpperCamelCase__     ,  decimals=6     )     ,  [
                   [
                       {
                           "score": 2.2e-05,
                           "token": 3_5676,
                           "token_str": " Maul",
                           "sequence": "<s>My name is Maul<mask></s>",
                       },
                       {"score": 2.2e-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
                   ],
                   [
                       {
                           "score": 2.2e-05,
                           "token": 3_5676,
                           "token_str": " Maul",
                           "sequence": "<s>My name is<mask> Maul</s>",
                       },
                       {"score": 2.2e-05, "token": 1_6416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
                   ],
               ]     ,  )







    @require_torch_gpu
    def     _lowercase     (    self     )     ->      Dict:
           lowerCamelCase   :      Any			 =					pipeline("fill-mask"     ,  model="hf-internal-testing/tiny-random-distilbert"     ,  device=0     ,  framework="pt"     )

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

           lowerCamelCase   :      Tuple			 =					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(UpperCamelCase__     ,  UpperCamelCase__     )







    @slow
    @require_torch
    def     _lowercase     (    self     )     ->      List[Any]:
           lowerCamelCase   :      Tuple			 =					pipeline(task="fill-mask"     ,  model="distilroberta-base"     ,  top_k=2     ,  framework="pt"     )
           self.run_large_test(UpperCamelCase__     )







    @slow
    @require_tf
    def     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      Tuple			 =					pipeline(task="fill-mask"     ,  model="distilroberta-base"     ,  top_k=2     ,  framework="tf"     )
           self.run_large_test(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      List[Any]			 =					unmasker("My name is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
                   {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
               ]     ,  )
           lowerCamelCase   :      List[Any]			 =					unmasker("The largest city in France is <mask>"     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   {
                       "sequence": "The largest city in France is Paris",
                       "score": 0.251,
                       "token": 2201,
                       "token_str": " Paris",
                   },
                   {
                       "sequence": "The largest city in France is Lyon",
                       "score": 0.214,
                       "token": 1_2790,
                       "token_str": " Lyon",
                   },
               ]     ,  )

           lowerCamelCase   :      int			 =					unmasker("My name is <mask>"     ,  targets=[" Patrick", " Clara", " Teven"]     ,  top_k=3     )
           self.assertEqual(
               nested_simplify(UpperCamelCase__     )     ,  [
                   {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
                   {"sequence": "My name is Clara", "score": 0.000, "token": 1_3606, "token_str": " Clara"},
                   {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
               ]     ,  )







    @require_torch
    def     _lowercase     (    self     )     ->      List[str]:
           lowerCamelCase   :      Union[str, Any]			 =					pipeline(task="fill-mask"     ,  model="sshleifer/tiny-distilroberta-base"     ,  framework="pt"     )
           lowerCamelCase   :      List[str]			 =					None
           lowerCamelCase   :      Dict			 =					None
           self.run_pipeline_test(UpperCamelCase__     ,  []     )







    @require_tf
    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Optional[int]			 =					pipeline(task="fill-mask"     ,  model="sshleifer/tiny-distilroberta-base"     ,  framework="tf"     )
           lowerCamelCase   :      int			 =					None
           lowerCamelCase   :      List[Any]			 =					None
           self.run_pipeline_test(UpperCamelCase__     ,  []     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[Any]:
           if tokenizer is None or tokenizer.mask_token_id is None:
                  self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"     )

           lowerCamelCase   :      Union[str, Any]			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					[
               F'''This is another {tokenizer.mask_token} test''',
           ]
           return fill_masker, examples







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Tuple			 =					fill_masker.tokenizer
           lowerCamelCase   :      Optional[Any]			 =					fill_masker.model

           lowerCamelCase   :      Optional[int]			 =					fill_masker(
               F'''This is a {tokenizer.mask_token}'''     ,  )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
               ]     ,  )

           lowerCamelCase   :      int			 =					fill_masker([F'''This is a {tokenizer.mask_token}''']     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
               ]     ,  )

           lowerCamelCase   :      Any			 =					fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.''']     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   [
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   ],
               ]     ,  )

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

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







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

           # Call argument
           lowerCamelCase   :      Tuple			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  targets=UpperCamelCase__     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
               ]     ,  )
           lowerCamelCase   :      List[str]			 =					{vocab[el] for el in targets}
           self.assertEqual({el["token"] for el in outputs}     ,  UpperCamelCase__     )
           lowerCamelCase   :      List[str]			 =					[tokenizer.decode([x]     ) for x in target_ids]
           self.assertEqual({el["token_str"] for el in outputs}     ,  set(UpperCamelCase__     )     )

           # Score equivalence
           lowerCamelCase   :      Optional[Any]			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  targets=UpperCamelCase__     )
           lowerCamelCase   :      Union[str, Any]			 =					[top_mask["token_str"] for top_mask in outputs]
           lowerCamelCase   :      Union[str, Any]			 =					[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(UpperCamelCase__     ) == set(UpperCamelCase__     ):
                  lowerCamelCase   :      Any			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  targets=UpperCamelCase__     )
                  lowerCamelCase   :      List[str]			 =					[top_mask["score"] for top_mask in unmasked_targets]
                  self.assertEqual(nested_simplify(UpperCamelCase__     )     ,  nested_simplify(UpperCamelCase__     )     )

           # Raises with invalid
           with self.assertRaises(UpperCamelCase__     ):
                  lowerCamelCase   :      Any			 =					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(UpperCamelCase__     ):
                         lowerCamelCase   :      int			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  targets=[""]     )
                  with self.assertRaises(UpperCamelCase__     ):
                         lowerCamelCase   :      int			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  targets=""     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      Union[str, Any]			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     ,  top_k=2     )
           lowerCamelCase   :      Union[str, Any]			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
               ]     ,  )

           lowerCamelCase   :      Union[str, Any]			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )
           lowerCamelCase   :      int			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  top_k=2     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
               ]     ,  )
           self.assertEqual(nested_simplify(UpperCamelCase__     )     ,  nested_simplify(UpperCamelCase__     )     )







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      int:
           lowerCamelCase   :      Tuple			 =					tokenizer.get_vocab()
           lowerCamelCase   :      Any			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )

           # top_k=2, ntargets=3
           lowerCamelCase   :      Any			 =					sorted(vocab.keys()     )[:3]
           lowerCamelCase   :      Dict			 =					fill_masker(F'''This is a {tokenizer.mask_token}'''     ,  top_k=2     ,  targets=UpperCamelCase__     )

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      str:
           lowerCamelCase   :      int			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )
           lowerCamelCase   :      str			 =					tokenizer.get_vocab()
           # String duplicates + id duplicates
           lowerCamelCase   :      Tuple			 =					sorted(vocab.keys()     )[:3]
           lowerCamelCase   :      List[str]			 =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
           lowerCamelCase   :      int			 =					fill_masker(F'''My name is {tokenizer.mask_token}'''     ,  targets=UpperCamelCase__     ,  top_k=10     )

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







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Dict:
           lowerCamelCase   :      int			 =					FillMaskPipeline(model=UpperCamelCase__     ,  tokenizer=UpperCamelCase__     )

           lowerCamelCase   :      Tuple			 =					fill_masker(
               F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''     ,  top_k=2     )
           self.assertEqual(
               UpperCamelCase__     ,  [
                   [
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   ],
                   [
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                       {"sequence": ANY(UpperCamelCase__     ), "score": ANY(UpperCamelCase__     ), "token": ANY(UpperCamelCase__     ), "token_str": ANY(UpperCamelCase__     )},
                   ],
               ]     ,  )












import math

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * power_factor

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> float:
       if (
           not isinstance(_SCREAMING_SNAKE_CASE ,(int, float)    )
           or power_factor < -1
           or power_factor > 1
       ):
              raise ValueError("power_factor must be a valid float value between -1 and 1."    )
       return apparent_power * math.sqrt(1 - power_factor**2    )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






