






'''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 _lowercase	(				unittest.TestCase		):
							a				      =       MODEL_FOR_MASKED_LM_MAPPING
							a				      =       TF_MODEL_FOR_MASKED_LM_MAPPING




							def 		lowerCamelCase_				(							self:				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 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	int         =       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""": 38_015, """token_str""": """ grouped"""},
														        {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 25_506, """token_str""": """ accuser"""},
														    ]			,							)

														lowerCamelCase__					:	Optional[Any]         =       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""": 38_015,
														            """token_str""": """ grouped""",
														        },
														        {
														            """sequence""": """The largest city in France is accuser""",
														            """score""": 2.1e-05,
														            """token""": 25_506,
														            """token_str""": """ accuser""",
														        },
														    ]			,							)

														lowerCamelCase__					:	List[Any]         =       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""": 13_606, """token_str""": """ Clara"""},
														        {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3_499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2_941, """token_str""": """ Te"""},
														    ]			,							)




							@require_torch
							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Tuple         =       pipeline(task="""fill-mask"""			,							model="""sshleifer/tiny-distilroberta-base"""			,							top_k=2			,							framework="""pt"""     )

														lowerCamelCase__					:	List[Any]         =       unmasker("""My name is <mask>"""     )
														self.assertEqual(
														    nested_simplify(UpperCamelCase__			,							decimals=6     )			,							[
														        {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 35_676, """token_str""": """ Maul"""},
														        {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 16_416, """token_str""": """ELS"""},
														    ]			,							)

														lowerCamelCase__					:	int         =       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""": 35_676,
														            """token_str""": """ Maul""",
														        },
														        {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 16_416, """token_str""": """ELS"""},
														    ]			,							)

														lowerCamelCase__					:	Tuple         =       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""": 3_499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2_941, """token_str""": """ Te"""},
														        {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 13_606, """token_str""": """ Clara"""},
														    ]			,							)

														lowerCamelCase__					:	List[Any]         =       unmasker("""My name is <mask> <mask>"""			,							top_k=2     )

														self.assertEqual(
														    nested_simplify(UpperCamelCase__			,							decimals=6     )			,							[
														        [
														            {
														                """score""": 2.2e-05,
														                """token""": 35_676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is Maul<mask></s>""",
														            },
														            {"""score""": 2.2e-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
														        ],
														        [
														            {
														                """score""": 2.2e-05,
														                """token""": 35_676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is<mask> Maul</s>""",
														            },
														            {"""score""": 2.2e-05, """token""": 16_416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
														        ],
														    ]			,							)




							@require_torch_gpu
							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       pipeline("""fill-mask"""			,							model="""hf-internal-testing/tiny-random-distilbert"""			,							device=0			,							framework="""pt"""     )

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

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




							@slow
							@require_tf
							def 		lowerCamelCase_				(							self:				List[Any]     ):
														lowerCamelCase__					:	Union[str, Any]         =       pipeline(task="""fill-mask"""			,							model="""distilroberta-base"""			,							top_k=2			,							framework="""tf"""     )
														self.run_large_test(UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				int			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	int         =       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""": 1_573, """token_str""": """ Chris"""},
														    ]			,							)
														lowerCamelCase__					:	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""": 2_201,
														            """token_str""": """ Paris""",
														        },
														        {
														            """sequence""": """The largest city in France is Lyon""",
														            """score""": 0.214,
														            """token""": 12_790,
														            """token_str""": """ Lyon""",
														        },
														    ]			,							)

														lowerCamelCase__					:	Optional[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""": 3_499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13_606, """token_str""": """ Clara"""},
														        {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_941, """token_str""": """ Te"""},
														    ]			,							)




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




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




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				List[str]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				List[str]     ):
														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__					:	Optional[Any]         =       [
														    F'''This is another {tokenizer.mask_token} test''',
														]
														return fill_masker, examples




							def 		lowerCamelCase_				(							self:				Optional[Any]			,							UpperCamelCase__:				int			,							UpperCamelCase__:				List[str]     ):
														lowerCamelCase__					:	List[str]         =       fill_masker.tokenizer
														lowerCamelCase__					:	Optional[int]         =       fill_masker.model

														lowerCamelCase__					:	Tuple         =       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__					:	str         =       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__					:	Union[str, 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 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	Optional[int]         =       tokenizer.get_vocab()
														lowerCamelCase__					:	str         =       sorted(vocab.keys()     )[:2]
														# Pipeline argument
														lowerCamelCase__					:	Any         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__			,							targets=UpperCamelCase__     )
														lowerCamelCase__					:	str         =       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__					:	Optional[Any]         =       [tokenizer.decode([x]     ) for x in target_ids]
														self.assertEqual({el["""token_str"""] for el in outputs}			,							set(UpperCamelCase__     )     )

														# Call argument
														lowerCamelCase__					:	str         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__     )
														lowerCamelCase__					:	Optional[Any]         =       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__					:	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__     )     )

														# Score equivalence
														lowerCamelCase__					:	Optional[int]         =       fill_masker(F'''This is a {tokenizer.mask_token}'''			,							targets=UpperCamelCase__     )
														lowerCamelCase__					:	int         =       [top_mask["""token_str"""] for top_mask in outputs]
														lowerCamelCase__					:	Optional[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(UpperCamelCase__     ) == set(UpperCamelCase__     ):
																					lowerCamelCase__					:	Dict         =       fill_masker(F'''This is a {tokenizer.mask_token}'''			,							targets=UpperCamelCase__     )
																					lowerCamelCase__					:	Union[str, Any]         =       [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__					:	Tuple         =       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 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Union[str, Any]         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__			,							top_k=2     )
														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__					:	Dict         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__     )
														lowerCamelCase__					:	Optional[Any]         =       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 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	str         =       tokenizer.get_vocab()
														lowerCamelCase__					:	Optional[Any]         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__     )

														# top_k=2, ntargets=3
														lowerCamelCase__					:	Optional[int]         =       sorted(vocab.keys()     )[:3]
														lowerCamelCase__					:	Tuple         =       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__					:	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__					:	Tuple         =       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 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				Tuple     ):
														lowerCamelCase__					:	int         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__     )
														lowerCamelCase__					:	Dict         =       tokenizer.get_vocab()
														# String duplicates + id duplicates
														lowerCamelCase__					:	Union[str, Any]         =       sorted(vocab.keys()     )[:3]
														lowerCamelCase__					:	Optional[Any]         =       [targets[0], targets[1], targets[0], targets[2], targets[1]]
														lowerCamelCase__					:	Dict         =       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 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	Union[str, Any]         =       FillMaskPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__     )

														lowerCamelCase__					:	str         =       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__     )},
														        ],
														    ]			,							)



"""simple docstring"""





from __future__ import annotations

import math







def 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Any			     =    u
   for i in range(1,		__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    temp * (u - i)
   return temp







def 	__a     (		):
   UpperCAmelCase_					:					str			     =    int(input("enter the numbers of values: "  )  )
   UpperCAmelCase_					:					list[list[float]]			     =    []
   for _ in range(__lowerCamelCase  ):
      y.append([]  )
   for i in range(__lowerCamelCase  ):
      for j in range(__lowerCamelCase  ):
         y[i].append(__lowerCamelCase  )
         UpperCAmelCase_					:					Tuple			     =    0

   print("enter the values of parameters in a list: "  )
   UpperCAmelCase_					:					Union[str, Any]			     =    list(map(__lowerCamelCase,		input().split()  )  )

   print("enter the values of corresponding parameters: "  )
   for i in range(__lowerCamelCase  ):
      UpperCAmelCase_					:					int			     =    float(input()  )

   UpperCAmelCase_					:					Tuple			     =    int(input("enter the value to interpolate: "  )  )
   UpperCAmelCase_					:					Tuple			     =    (value - x[0]) / (x[1] - x[0])

   # for calculating forward difference table

   for i in range(1,		__lowerCamelCase  ):
      for j in range(n - i  ):
         UpperCAmelCase_					:					Union[str, Any]			     =    y[j + 1][i - 1] - y[j][i - 1]

   UpperCAmelCase_					:					Optional[int]			     =    y[0][0]
   for i in range(1,		__lowerCamelCase  ):
      summ += (ucal(__lowerCamelCase,		__lowerCamelCase  ) * y[0][i]) / math.factorial(__lowerCamelCase  )

   print(f"""the value at {value} is {summ}"""  )


if __name__ == "__main__":
    main()


