


"""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 				__A    (	unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  MODEL_FOR_MASKED_LM_MAPPING
 __lowerCAmelCase			 =  TF_MODEL_FOR_MASKED_LM_MAPPING


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     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 				SCREAMING_SNAKE_CASE							(		self						) ->					Union[str, Any]:
     a					=pipeline(task='''fill-mask'''			,   model='''sshleifer/tiny-distilroberta-base'''			,   top_k=2			,   framework='''tf'''						)
     a					=unmasker('''My name is <mask>'''						)
     self.assertEqual(
         nested_simplify(__A			,   decimals=6						)			,   [
             {'''sequence''': '''My name is grouped''', '''score''': 2.1E-0_5, '''token''': 3_8015, '''token_str''': ''' grouped'''},
             {'''sequence''': '''My name is accuser''', '''score''': 2.1E-0_5, '''token''': 2_5506, '''token_str''': ''' accuser'''},
         ]			,   )

     a					=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-0_5,
                 '''token''': 3_8015,
                 '''token_str''': ''' grouped''',
             },
             {
                 '''sequence''': '''The largest city in France is accuser''',
                 '''score''': 2.1E-0_5,
                 '''token''': 2_5506,
                 '''token_str''': ''' accuser''',
             },
         ]			,   )

     a					=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-0_5, '''token''': 1_3606, '''token_str''': ''' Clara'''},
             {'''sequence''': '''My name is Patrick''', '''score''': 2E-0_5, '''token''': 3499, '''token_str''': ''' Patrick'''},
             {'''sequence''': '''My name is Te''', '''score''': 1.9E-0_5, '''token''': 2941, '''token_str''': ''' Te'''},
         ]			,   )


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

     a					=unmasker('''My name is <mask>'''						)
     self.assertEqual(
         nested_simplify(__A			,   decimals=6						)			,   [
             {'''sequence''': '''My name is Maul''', '''score''': 2.2E-0_5, '''token''': 3_5676, '''token_str''': ''' Maul'''},
             {'''sequence''': '''My name isELS''', '''score''': 2.2E-0_5, '''token''': 1_6416, '''token_str''': '''ELS'''},
         ]			,   )

     a					=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-0_5,
                 '''token''': 3_5676,
                 '''token_str''': ''' Maul''',
             },
             {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2E-0_5, '''token''': 1_6416, '''token_str''': '''ELS'''},
         ]			,   )

     a					=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-0_5, '''token''': 3499, '''token_str''': ''' Patrick'''},
             {'''sequence''': '''My name is Te''', '''score''': 2E-0_5, '''token''': 2941, '''token_str''': ''' Te'''},
             {'''sequence''': '''My name is Clara''', '''score''': 2E-0_5, '''token''': 1_3606, '''token_str''': ''' Clara'''},
         ]			,   )

     a					=unmasker('''My name is <mask> <mask>'''			,   top_k=2						)

     self.assertEqual(
         nested_simplify(__A			,   decimals=6						)			,   [
             [
                 {
                     '''score''': 2.2E-0_5,
                     '''token''': 3_5676,
                     '''token_str''': ''' Maul''',
                     '''sequence''': '''<s>My name is Maul<mask></s>''',
                 },
                 {'''score''': 2.2E-0_5, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
             ],
             [
                 {
                     '''score''': 2.2E-0_5,
                     '''token''': 3_5676,
                     '''token_str''': ''' Maul''',
                     '''sequence''': '''<s>My name is<mask> Maul</s>''',
                 },
                 {'''score''': 2.2E-0_5, '''token''': 1_6416, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
             ],
         ]			,   )


 @require_torch_gpu
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=pipeline('''fill-mask'''			,   model='''hf-internal-testing/tiny-random-distilbert'''			,   device=0			,   framework='''pt'''						)

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

     a					=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 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a					=pipeline(task='''fill-mask'''			,   model='''distilroberta-base'''			,   top_k=2			,   framework='''pt'''						)
     self.run_large_test(__A						)


 @slow
 @require_tf
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a					=pipeline(task='''fill-mask'''			,   model='''distilroberta-base'''			,   top_k=2			,   framework='''tf'''						)
     self.run_large_test(__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Tuple:
     a					=unmasker('''My name is <mask>'''						)
     self.assertEqual(
         nested_simplify(__A						)			,   [
             {'''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'''},
         ]			,   )
     a					=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''': 2201,
                 '''token_str''': ''' Paris''',
             },
             {
                 '''sequence''': '''The largest city in France is Lyon''',
                 '''score''': 0.214,
                 '''token''': 1_2790,
                 '''token_str''': ''' Lyon''',
             },
         ]			,   )

     a					=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''': 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 				SCREAMING_SNAKE_CASE							(		self						) ->					List[Any]:
     a					=pipeline(task='''fill-mask'''			,   model='''sshleifer/tiny-distilroberta-base'''			,   framework='''pt'''						)
     a					=None
     a					=None
     self.run_pipeline_test(__A			,   []						)


 @require_tf
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=pipeline(task='''fill-mask'''			,   model='''sshleifer/tiny-distilroberta-base'''			,   framework='''tf'''						)
     a					=None
     a					=None
     self.run_pipeline_test(__A			,   []						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A			,   __A						) ->					Optional[int]:
     if tokenizer is None or tokenizer.mask_token_id is None:
         self.skipTest('''The provided tokenizer has no mask token, (probably reformer or wav2vec2)'''						)

     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)
     a					=[
         f'''This is another {tokenizer.mask_token} test''',
     ]
     return fill_masker, examples


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Dict:
     a					=fill_masker.tokenizer
     a					=fill_masker.model

     a					=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						)},
         ]			,   )

     a					=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						)},
         ]			,   )

     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Tuple:
     a					=tokenizer.get_vocab()
     a					=sorted(vocab.keys()						)[:2]
     # Pipeline argument
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A			,   targets=__A						)
     a					=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						)},
         ]			,   )
     a					={vocab[el] for el in targets}
     self.assertEqual({el['''token'''] for el in outputs}			,   __A						)
     a					=[tokenizer.decode([x]						) for x in target_ids]
     self.assertEqual({el['''token_str'''] for el in outputs}			,   set(__A						)						)

     # Call argument
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)
     a					=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						)},
         ]			,   )
     a					={vocab[el] for el in targets}
     self.assertEqual({el['''token'''] for el in outputs}			,   __A						)
     a					=[tokenizer.decode([x]						) for x in target_ids]
     self.assertEqual({el['''token_str'''] for el in outputs}			,   set(__A						)						)

     # Score equivalence
     a					=fill_masker(f'''This is a {tokenizer.mask_token}'''			,   targets=__A						)
     a					=[top_mask['''token_str'''] for top_mask in outputs]
     a					=[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						):
         a					=fill_masker(f'''This is a {tokenizer.mask_token}'''			,   targets=__A						)
         a					=[top_mask['''score'''] for top_mask in unmasked_targets]
         self.assertEqual(nested_simplify(__A						)			,   nested_simplify(__A						)						)

     # Raises with invalid
     with self.assertRaises(__A						):
         a					=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						):
             a					=fill_masker(f'''This is a {tokenizer.mask_token}'''			,   targets=['''''']						)
         with self.assertRaises(__A						):
             a					=fill_masker(f'''This is a {tokenizer.mask_token}'''			,   targets=''''''						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Tuple:
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A			,   top_k=2						)
     a					=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						)},
         ]			,   )

     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					List[Any]:
     a					=tokenizer.get_vocab()
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)

     # top_k=2, ntargets=3
     a					=sorted(vocab.keys()						)[:3]
     a					=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
     a					=[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						):
         a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					Dict:
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)
     a					=tokenizer.get_vocab()
     # String duplicates + id duplicates
     a					=sorted(vocab.keys()						)[:3]
     a					=[targets[0], targets[1], targets[0], targets[2], targets[1]]
     a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A						) ->					str:
     a					=FillMaskPipeline(model=__A			,   tokenizer=__A						)

     a					=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"""







def _A						(						lowercase = 2_00_00_00		):







    """simple docstring"""
    a					=[0 for i in range(n + 1		)]
    a					=1
    a					=1

    for i in range(2	,   int(n**0.5		) + 1		):
        if primality_list[i] == 0:
            for j in range(i * i	,   n + 1	,   lowercase		):
                a					=1
    a					=0
    for i in range(lowercase		):
        if primality_list[i] == 0:
            sum_of_primes += i
    return sum_of_primes


if __name__ == "__main__":
       print(F'{solution() = }')