





'''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			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	MODEL_FOR_MASKED_LM_MAPPING
					SCREAMING_SNAKE_CASE__				=	TF_MODEL_FOR_MASKED_LM_MAPPING


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							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 lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			pipeline(task="""fill-mask"""      ,						model="""sshleifer/tiny-distilroberta-base"""      ,						top_k=2      ,						framework="""tf"""						)
							SCREAMING_SNAKE_CASE :				Any		=			unmasker("""My name is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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"""},
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				str		=			unmasker("""The largest city in France is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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""",
							        },
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				str		=			unmasker("""My name is <mask>"""      ,						targets=[""" Patrick""", """ Clara""", """ Teven"""]      ,						top_k=3						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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 lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			pipeline(task="""fill-mask"""      ,						model="""sshleifer/tiny-distilroberta-base"""      ,						top_k=2      ,						framework="""pt"""						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			unmasker("""My name is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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"""},
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				Any		=			unmasker("""The largest city in France is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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"""},
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				int		=			unmasker("""My name is <mask>"""      ,						targets=[""" Patrick""", """ Clara""", """ Teven"""]      ,						top_k=3						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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"""},
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				Dict		=			unmasker("""My name is <mask> <mask>"""      ,						top_k=2						)

							self.assertEqual(
							    nested_simplify(lowerCamelCase_      ,						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 lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							SCREAMING_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()

							SCREAMING_SNAKE_CASE :				int		=			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(lowerCamelCase_      ,						lowerCamelCase_						)


					@slow
					@require_torch
					def lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			pipeline(task="""fill-mask"""      ,						model="""distilroberta-base"""      ,						top_k=2      ,						framework="""pt"""						)
							self.run_large_test(lowerCamelCase_						)


					@slow
					@require_tf
					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			pipeline(task="""fill-mask"""      ,						model="""distilroberta-base"""      ,						top_k=2      ,						framework="""tf"""						)
							self.run_large_test(lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			unmasker("""My name is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_						)      ,						[
							        {"""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"""},
							    ]      ,						)
							SCREAMING_SNAKE_CASE :				List[str]		=			unmasker("""The largest city in France is <mask>"""						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_						)      ,						[
							        {
							            """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""",
							        },
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			unmasker("""My name is <mask>"""      ,						targets=[""" Patrick""", """ Clara""", """ Teven"""]      ,						top_k=3						)
							self.assertEqual(
							    nested_simplify(lowerCamelCase_						)      ,						[
							        {"""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 lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			pipeline(task="""fill-mask"""      ,						model="""sshleifer/tiny-distilroberta-base"""      ,						framework="""pt"""						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
							SCREAMING_SNAKE_CASE :				int		=			None
							self.run_pipeline_test(lowerCamelCase_      ,						[]						)


					@require_tf
					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			pipeline(task="""fill-mask"""      ,						model="""sshleifer/tiny-distilroberta-base"""      ,						framework="""tf"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			None
							SCREAMING_SNAKE_CASE :				Optional[int]		=			None
							self.run_pipeline_test(lowerCamelCase_      ,						[]						)


					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			Tuple      ,						lowerCamelCase_				:			List[Any]      ,						lowerCamelCase_				:			List[str]						):

							'''simple docstring'''



							if tokenizer is None or tokenizer.mask_token_id is None:
									self.skipTest("""The provided tokenizer has no mask token, (probably reformer or wav2vec2)"""						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			[
							    f'''This is another {tokenizer.mask_token} test''',
							]
							return fill_masker, examples


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			fill_masker.tokenizer
							SCREAMING_SNAKE_CASE :				Any		=			fill_masker.model

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

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

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

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

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


					def lowerCamelCase_			(						self				:			Union[str, Any]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



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

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

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

							# Raises with invalid
							with self.assertRaises(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Optional[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(lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Tuple		=			fill_masker(f'''This is a {tokenizer.mask_token}'''      ,						targets=[""""""]						)
									with self.assertRaises(lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Optional[int]		=			fill_masker(f'''This is a {tokenizer.mask_token}'''      ,						targets=""""""						)


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			Dict      ,						lowerCamelCase_				:			Any						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_      ,						top_k=2						)
							SCREAMING_SNAKE_CASE :				Tuple		=			fill_masker(f'''This is a {tokenizer.mask_token}'''						)
							self.assertEqual(
							    lowerCamelCase_      ,						[
							        {"""sequence""": ANY(lowerCamelCase_						), """score""": ANY(lowerCamelCase_						), """token""": ANY(lowerCamelCase_						), """token_str""": ANY(lowerCamelCase_						)},
							        {"""sequence""": ANY(lowerCamelCase_						), """score""": ANY(lowerCamelCase_						), """token""": ANY(lowerCamelCase_						), """token_str""": ANY(lowerCamelCase_						)},
							    ]      ,						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			fill_masker(f'''This is a {tokenizer.mask_token}'''      ,						top_k=2						)
							self.assertEqual(
							    lowerCamelCase_      ,						[
							        {"""sequence""": ANY(lowerCamelCase_						), """score""": ANY(lowerCamelCase_						), """token""": ANY(lowerCamelCase_						), """token_str""": ANY(lowerCamelCase_						)},
							        {"""sequence""": ANY(lowerCamelCase_						), """score""": ANY(lowerCamelCase_						), """token""": ANY(lowerCamelCase_						), """token_str""": ANY(lowerCamelCase_						)},
							    ]      ,						)
							self.assertEqual(nested_simplify(lowerCamelCase_						)      ,						nested_simplify(lowerCamelCase_						)						)


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			Tuple      ,						lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			tokenizer.get_vocab()
							SCREAMING_SNAKE_CASE :				str		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)

							# top_k=2, ntargets=3
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			sorted(vocab.keys()						)[:3]
							SCREAMING_SNAKE_CASE :				str		=			fill_masker(f'''This is a {tokenizer.mask_token}'''      ,						top_k=2      ,						targets=lowerCamelCase_						)

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


					def lowerCamelCase_			(						self				:			int      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			tokenizer.get_vocab()
							# String duplicates + id duplicates
							SCREAMING_SNAKE_CASE :				Tuple		=			sorted(vocab.keys()						)[:3]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[targets[0], targets[1], targets[0], targets[2], targets[1]]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			fill_masker(f'''My name is {tokenizer.mask_token}'''      ,						targets=lowerCamelCase_      ,						top_k=10						)

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



					def lowerCamelCase_			(						self				:			List[str]      ,						lowerCamelCase_				:			List[str]      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			FillMaskPipeline(model=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)

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










'''simple docstring'''




import random



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				str		=			num - 1
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0

		while s % 2 == 0:
				SCREAMING_SNAKE_CASE :				List[str]		=			s // 2
				t += 1

		for _ in range(5	):
				SCREAMING_SNAKE_CASE :				List[Any]		=			random.randrange(2			,			num - 1	)
				SCREAMING_SNAKE_CASE :				List[Any]		=			pow(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
				if v != 1:
						SCREAMING_SNAKE_CASE :				Tuple		=			0
						while v != (num - 1):
								if i == t - 1:
										return False
								else:
										SCREAMING_SNAKE_CASE :				Any		=			i + 1
										SCREAMING_SNAKE_CASE :				str		=			(v**2) % num
		return True



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		if num < 2:
				return False

		SCREAMING_SNAKE_CASE :				List[str]		=			[
		    2,
		    3,
		    5,
		    7,
		    11,
		    13,
		    17,
		    19,
		    23,
		    29,
		    31,
		    37,
		    41,
		    43,
		    47,
		    53,
		    59,
		    61,
		    67,
		    71,
		    73,
		    79,
		    83,
		    89,
		    97,
		    1_01,
		    1_03,
		    1_07,
		    1_09,
		    1_13,
		    1_27,
		    1_31,
		    1_37,
		    1_39,
		    1_49,
		    1_51,
		    1_57,
		    1_63,
		    1_67,
		    1_73,
		    1_79,
		    1_81,
		    1_91,
		    1_93,
		    1_97,
		    1_99,
		    2_11,
		    2_23,
		    2_27,
		    2_29,
		    2_33,
		    2_39,
		    2_41,
		    2_51,
		    2_57,
		    2_63,
		    2_69,
		    2_71,
		    2_77,
		    2_81,
		    2_83,
		    2_93,
		    3_07,
		    3_11,
		    3_13,
		    3_17,
		    3_31,
		    3_37,
		    3_47,
		    3_49,
		    3_53,
		    3_59,
		    3_67,
		    3_73,
		    3_79,
		    3_83,
		    3_89,
		    3_97,
		    4_01,
		    4_09,
		    4_19,
		    4_21,
		    4_31,
		    4_33,
		    4_39,
		    4_43,
		    4_49,
		    4_57,
		    4_61,
		    4_63,
		    4_67,
		    4_79,
		    4_87,
		    4_91,
		    4_99,
		    5_03,
		    5_09,
		    5_21,
		    5_23,
		    5_41,
		    5_47,
		    5_57,
		    5_63,
		    5_69,
		    5_71,
		    5_77,
		    5_87,
		    5_93,
		    5_99,
		    6_01,
		    6_07,
		    6_13,
		    6_17,
		    6_19,
		    6_31,
		    6_41,
		    6_43,
		    6_47,
		    6_53,
		    6_59,
		    6_61,
		    6_73,
		    6_77,
		    6_83,
		    6_91,
		    7_01,
		    7_09,
		    7_19,
		    7_27,
		    7_33,
		    7_39,
		    7_43,
		    7_51,
		    7_57,
		    7_61,
		    7_69,
		    7_73,
		    7_87,
		    7_97,
		    8_09,
		    8_11,
		    8_21,
		    8_23,
		    8_27,
		    8_29,
		    8_39,
		    8_53,
		    8_57,
		    8_59,
		    8_63,
		    8_77,
		    8_81,
		    8_83,
		    8_87,
		    9_07,
		    9_11,
		    9_19,
		    9_29,
		    9_37,
		    9_41,
		    9_47,
		    9_53,
		    9_67,
		    9_71,
		    9_77,
		    9_83,
		    9_91,
		    9_97,
		]

		if num in low_primes:
				return True

		for prime in low_primes:
				if (num % prime) == 0:
						return False

		return rabin_miller(lowerCamelCase_	)



def 		__A		(  lowerCamelCase_ = 10_24	):


		"""simple docstring"""



		while True:
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			random.randrange(2 ** (keysize - 1)			,			2 ** (keysize)	)
				if is_prime_low_num(lowerCamelCase_	):
						return num


if __name__ == "__main__":
							__UpperCAmelCase            =       generate_large_prime()
							print(("""Prime number:""", num))
							print(("""is_prime_low_num:""", is_prime_low_num(num)))



