





import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows





def     _UpperCamelCase     (a__   :List[Any]       ):  # picklable for multiprocessing






							"""simple docstring"""





							return i + 1





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def     _UpperCamelCase     ():






							"""simple docstring"""





							with parallel_backend("""spark"""       ):
														assert ParallelBackendConfig.backend_name == "spark"

							UpperCamelCase__				     =			[1, 2, 3]
							with pytest.raises(_lowercase       ):
														with parallel_backend("""unsupported backend"""       ):
																					map_nested(_lowercase   ,			_lowercase   ,			num_proc=2       )

							with pytest.raises(_lowercase       ):
														with parallel_backend("""unsupported backend"""       ):
																					map_nested(_lowercase   ,			_lowercase   ,			num_proc=-1       )





@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc"""   ,			[2, -1]       )
def     _UpperCamelCase     (a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[1, 2]
							UpperCamelCase__				     =			{"a": 1, "b": 2}
							UpperCamelCase__				     =			{"a": [1, 2], "b": [3, 4]}
							UpperCamelCase__				     =			{"a": {"1": 1}, "b": 2}
							UpperCamelCase__				     =			{"a": 1, "b": 2, "c": 3, "d": 4}
							UpperCamelCase__				     =			[2, 3]
							UpperCamelCase__				     =			{"a": 2, "b": 3}
							UpperCamelCase__				     =			{"a": [2, 3], "b": [4, 5]}
							UpperCamelCase__				     =			{"a": {"1": 2}, "b": 3}
							UpperCamelCase__				     =			{"a": 2, "b": 3, "c": 4, "d": 5}

							with parallel_backend("""spark"""       ):
														assert map_nested(_lowercase   ,			_lowercase   ,			num_proc=_lowercase       ) == expected_map_nested_sa
														assert map_nested(_lowercase   ,			_lowercase   ,			num_proc=_lowercase       ) == expected_map_nested_sa
														assert map_nested(_lowercase   ,			_lowercase   ,			num_proc=_lowercase       ) == expected_map_nested_sa
														assert map_nested(_lowercase   ,			_lowercase   ,			num_proc=_lowercase       ) == expected_map_nested_sa
														assert map_nested(_lowercase   ,			_lowercase   ,			num_proc=_lowercase       ) == expected_map_nested_sa











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        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      Any						     =       MODEL_FOR_MASKED_LM_MAPPING
							snake_case     :      Dict						     =       TF_MODEL_FOR_MASKED_LM_MAPPING






							def 				_lowerCamelCase		(						self  ):
														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  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , top_k=2  , framework="""tf"""  )
														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 38015, """token_str""": """ grouped"""},
														        {"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 25506, """token_str""": """ accuser"""},
														    ]  , )

														UpperCamelCase__				     =			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""": 38015,
														            """token_str""": """ grouped""",
														        },
														        {
														            """sequence""": """The largest city in France is accuser""",
														            """score""": 2.1E-05,
														            """token""": 25506,
														            """token_str""": """ accuser""",
														        },
														    ]  , )

														UpperCamelCase__				     =			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""": 13606, """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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , top_k=2  , framework="""pt"""  )

														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        {"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 35676, """token_str""": """ Maul"""},
														        {"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 16416, """token_str""": """ELS"""},
														    ]  , )

														UpperCamelCase__				     =			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""": 35676,
														            """token_str""": """ Maul""",
														        },
														        {"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 16416, """token_str""": """ELS"""},
														    ]  , )

														UpperCamelCase__				     =			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""": 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""": 13606, """token_str""": """ Clara"""},
														    ]  , )

														UpperCamelCase__				     =			unmasker("""My name is <mask> <mask>"""  , top_k=2  )

														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  , decimals=6  )  , [
														        [
														            {
														                """score""": 2.2E-05,
														                """token""": 35676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is Maul<mask></s>""",
														            },
														            {"""score""": 2.2E-05, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
														        ],
														        [
														            {
														                """score""": 2.2E-05,
														                """token""": 35676,
														                """token_str""": """ Maul""",
														                """sequence""": """<s>My name is<mask> Maul</s>""",
														            },
														            {"""score""": 2.2E-05, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
														        ],
														    ]  , )






							@require_torch_gpu
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline("""fill-mask"""  , model="""hf-internal-testing/tiny-random-distilbert"""  , device=0  , framework="""pt"""  )

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

														UpperCamelCase__				     =			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  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""distilroberta-base"""  , top_k=2  , framework="""pt"""  )
														self.run_large_test(__lowerCAmelCase  )






							@slow
							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""distilroberta-base"""  , top_k=2  , framework="""tf"""  )
														self.run_large_test(__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			unmasker("""My name is <mask>"""  )
														self.assertEqual(
														    nested_simplify(__lowerCAmelCase  )  , [
														        {"""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"""},
														    ]  , )
														UpperCamelCase__				     =			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""": 2201,
														            """token_str""": """ Paris""",
														        },
														        {
														            """sequence""": """The largest city in France is Lyon""",
														            """score""": 0.214,
														            """token""": 12790,
														            """token_str""": """ Lyon""",
														        },
														    ]  , )

														UpperCamelCase__				     =			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""": 3499, """token_str""": """ Patrick"""},
														        {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 13606, """token_str""": """ Clara"""},
														        {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2941, """token_str""": """ Te"""},
														    ]  , )






							@require_torch
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , framework="""pt"""  )
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			None
														self.run_pipeline_test(__lowerCAmelCase  , []  )






							@require_tf
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			pipeline(task="""fill-mask"""  , model="""sshleifer/tiny-distilroberta-base"""  , framework="""tf"""  )
														UpperCamelCase__				     =			None
														UpperCamelCase__				     =			None
														self.run_pipeline_test(__lowerCAmelCase  , []  )






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

														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			[
														    f"""This is another {tokenizer.mask_token} test""",
														]
														return fill_masker, examples






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			fill_masker.tokenizer
														UpperCamelCase__				     =			fill_masker.model

														UpperCamelCase__				     =			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  )},
														    ]  , )

														UpperCamelCase__				     =			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  )},
														    ]  , )

														UpperCamelCase__				     =			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  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			tokenizer.get_vocab()
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:2]
														# Pipeline argument
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , targets=__lowerCAmelCase  )
														UpperCamelCase__				     =			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  )},
														    ]  , )
														UpperCamelCase__				     =			{vocab[el] for el in targets}
														self.assertEqual({el["""token"""] for el in outputs}  , __lowerCAmelCase  )
														UpperCamelCase__				     =			[tokenizer.decode([x]  ) for x in target_ids]
														self.assertEqual({el["""token_str"""] for el in outputs}  , set(__lowerCAmelCase  )  )

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

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

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






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  , top_k=2  )
														UpperCamelCase__				     =			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  )},
														    ]  , )

														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			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  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			tokenizer.get_vocab()
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )

														# top_k=2, ntargets=3
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:3]
														UpperCamelCase__				     =			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
														UpperCamelCase__				     =			[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  ):
																					UpperCamelCase__				     =			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  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )
														UpperCamelCase__				     =			tokenizer.get_vocab()
														# String duplicates + id duplicates
														UpperCamelCase__				     =			sorted(vocab.keys()  )[:3]
														UpperCamelCase__				     =			[targets[0], targets[1], targets[0], targets[2], targets[1]]
														UpperCamelCase__				     =			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  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FillMaskPipeline(model=__lowerCAmelCase  , tokenizer=__lowerCAmelCase  )

														UpperCamelCase__				     =			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  )},
														        ],
														    ]  , )




