


'''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 ):
      __UpperCamelCase			:    Optional[int]      			=				MODEL_FOR_MASKED_LM_MAPPING
      __UpperCamelCase			:    List[str]      			=				TF_MODEL_FOR_MASKED_LM_MAPPING






      def __UpperCAmelCase			(					self     :Dict      )			->   Optional[Any]:




             '''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 __UpperCAmelCase			(					self     :Dict      )			->   int:




             '''simple docstring'''
             _a       :					List[Any]	   =pipeline(task="""fill-mask"""	,		model="""sshleifer/tiny-distilroberta-base"""	,		top_k=2	,		framework="""tf"""      )
             _a       :					Optional[Any]	   =unmasker("""My name is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8_0_1_5, """token_str""": """ grouped"""},
                     {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5_5_0_6, """token_str""": """ accuser"""},
                 ]	,		)

             _a       :					Tuple	   =unmasker("""The largest city in France is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {
                         """sequence""": """The largest city in France is grouped""",
                         """score""": 2.1e-05,
                         """token""": 3_8_0_1_5,
                         """token_str""": """ grouped""",
                     },
                     {
                         """sequence""": """The largest city in France is accuser""",
                         """score""": 2.1e-05,
                         """token""": 2_5_5_0_6,
                         """token_str""": """ accuser""",
                     },
                 ]	,		)

             _a       :					int	   =unmasker("""My name is <mask>"""	,		targets=[""" Patrick""", """ Clara""", """ Teven"""]	,		top_k=3      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
                     {"""sequence""": """My name is Patrick""", """score""": 2e-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
                     {"""sequence""": """My name is Te""", """score""": 1.9e-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
                 ]	,		)






      @require_torch
      def __UpperCAmelCase			(					self     :Dict      )			->   List[str]:




             '''simple docstring'''
             _a       :					int	   =pipeline(task="""fill-mask"""	,		model="""sshleifer/tiny-distilroberta-base"""	,		top_k=2	,		framework="""pt"""      )

             _a       :					Dict	   =unmasker("""My name is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5_6_7_6, """token_str""": """ Maul"""},
                     {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
                 ]	,		)

             _a       :					Tuple	   =unmasker("""The largest city in France is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {
                         """sequence""": """The largest city in France is Maul""",
                         """score""": 2.2e-05,
                         """token""": 3_5_6_7_6,
                         """token_str""": """ Maul""",
                     },
                     {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS"""},
                 ]	,		)

             _a       :					List[Any]	   =unmasker("""My name is <mask>"""	,		targets=[""" Patrick""", """ Clara""", """ Teven"""]	,		top_k=3      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     {"""sequence""": """My name is Patrick""", """score""": 2.1e-05, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
                     {"""sequence""": """My name is Te""", """score""": 2e-05, """token""": 2_9_4_1, """token_str""": """ Te"""},
                     {"""sequence""": """My name is Clara""", """score""": 2e-05, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
                 ]	,		)

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

             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE	,		decimals=6      )	,		[
                     [
                         {
                             """score""": 2.2e-05,
                             """token""": 3_5_6_7_6,
                             """token_str""": """ Maul""",
                             """sequence""": """<s>My name is Maul<mask></s>""",
                         },
                         {"""score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
                     ],
                     [
                         {
                             """score""": 2.2e-05,
                             """token""": 3_5_6_7_6,
                             """token_str""": """ Maul""",
                             """sequence""": """<s>My name is<mask> Maul</s>""",
                         },
                         {"""score""": 2.2e-05, """token""": 1_6_4_1_6, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
                     ],
                 ]	,		)






      @require_torch_gpu
      def __UpperCAmelCase			(					self     :List[Any]      )			->   Tuple:




             '''simple docstring'''
             _a       :					Tuple	   =pipeline("""fill-mask"""	,		model="""hf-internal-testing/tiny-random-distilbert"""	,		device=0	,		framework="""pt"""      )

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

             _a       :					List[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(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE      )






      @slow
      @require_torch
      def __UpperCAmelCase			(					self     :Optional[int]      )			->   Tuple:




             '''simple docstring'''
             _a       :					Any	   =pipeline(task="""fill-mask"""	,		model="""distilroberta-base"""	,		top_k=2	,		framework="""pt"""      )
             self.run_large_test(_SCREAMING_SNAKE_CASE      )






      @slow
      @require_tf
      def __UpperCAmelCase			(					self     :Union[str, Any]      )			->   List[str]:




             '''simple docstring'''
             _a       :					Optional[int]	   =pipeline(task="""fill-mask"""	,		model="""distilroberta-base"""	,		top_k=2	,		framework="""tf"""      )
             self.run_large_test(_SCREAMING_SNAKE_CASE      )






      def __UpperCAmelCase			(					self     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :str      )			->   Any:




             '''simple docstring'''
             _a       :					Dict	   =unmasker("""My name is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE      )	,		[
                     {"""sequence""": """My name is John""", """score""": 0.008, """token""": 6_1_0, """token_str""": """ John"""},
                     {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 1_5_7_3, """token_str""": """ Chris"""},
                 ]	,		)
             _a       :					Dict	   =unmasker("""The largest city in France is <mask>"""      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE      )	,		[
                     {
                         """sequence""": """The largest city in France is Paris""",
                         """score""": 0.251,
                         """token""": 2_2_0_1,
                         """token_str""": """ Paris""",
                     },
                     {
                         """sequence""": """The largest city in France is Lyon""",
                         """score""": 0.214,
                         """token""": 1_2_7_9_0,
                         """token_str""": """ Lyon""",
                     },
                 ]	,		)

             _a       :					Optional[int]	   =unmasker("""My name is <mask>"""	,		targets=[""" Patrick""", """ Clara""", """ Teven"""]	,		top_k=3      )
             self.assertEqual(
                 nested_simplify(_SCREAMING_SNAKE_CASE      )	,		[
                     {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 3_4_9_9, """token_str""": """ Patrick"""},
                     {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 1_3_6_0_6, """token_str""": """ Clara"""},
                     {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2_9_4_1, """token_str""": """ Te"""},
                 ]	,		)






      @require_torch
      def __UpperCAmelCase			(					self     :List[Any]      )			->   Optional[int]:




             '''simple docstring'''
             _a       :					Any	   =pipeline(task="""fill-mask"""	,		model="""sshleifer/tiny-distilroberta-base"""	,		framework="""pt"""      )
             _a       :					List[Any]	   =None
             _a       :					Union[str, Any]	   =None
             self.run_pipeline_test(_SCREAMING_SNAKE_CASE	,		[]      )






      @require_tf
      def __UpperCAmelCase			(					self     :List[str]      )			->   Optional[Any]:




             '''simple docstring'''
             _a       :					Union[str, Any]	   =pipeline(task="""fill-mask"""	,		model="""sshleifer/tiny-distilroberta-base"""	,		framework="""tf"""      )
             _a       :					Optional[Any]	   =None
             _a       :					str	   =None
             self.run_pipeline_test(_SCREAMING_SNAKE_CASE	,		[]      )






      def __UpperCAmelCase			(					self     :Dict	,		SCREAMING_SNAKE_CASE     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :str	,		SCREAMING_SNAKE_CASE     :int      )			->   Optional[Any]:




             '''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)"""      )

             _a       :					Tuple	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE      )
             _a       :					Dict	   =[
                 f"This is another {tokenizer.mask_token} test",
             ]
             return fill_masker, examples






      def __UpperCAmelCase			(					self     :Tuple	,		SCREAMING_SNAKE_CASE     :List[Any]	,		SCREAMING_SNAKE_CASE     :Dict      )			->   Dict:




             '''simple docstring'''
             _a       :					int	   =fill_masker.tokenizer
             _a       :					Optional[Any]	   =fill_masker.model

             _a       :					List[str]	   =fill_masker(
                 f"This is a {tokenizer.mask_token}"	,		)
             self.assertEqual(
                 _SCREAMING_SNAKE_CASE	,		[
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                 ]	,		)

             _a       :					List[str]	   =fill_masker([f"This is a {tokenizer.mask_token}"]      )
             self.assertEqual(
                 _SCREAMING_SNAKE_CASE	,		[
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                 ]	,		)

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

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

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






      def __UpperCAmelCase			(					self     :List[str]	,		SCREAMING_SNAKE_CASE     :Dict	,		SCREAMING_SNAKE_CASE     :Union[str, Any]      )			->   Optional[int]:




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

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

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

             # Raises with invalid
             with self.assertRaises(_SCREAMING_SNAKE_CASE      ):
                    _a       :					Optional[int]	   =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(_SCREAMING_SNAKE_CASE      ):
                           _a       :					Any	   =fill_masker(f"This is a {tokenizer.mask_token}"	,		targets=[""""""]      )
                    with self.assertRaises(_SCREAMING_SNAKE_CASE      ):
                           _a       :					List[Any]	   =fill_masker(f"This is a {tokenizer.mask_token}"	,		targets=""""""      )






      def __UpperCAmelCase			(					self     :Any	,		SCREAMING_SNAKE_CASE     :Optional[Any]	,		SCREAMING_SNAKE_CASE     :List[str]      )			->   List[Any]:




             '''simple docstring'''
             _a       :					List[str]	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE	,		top_k=2      )
             _a       :					str	   =fill_masker(f"This is a {tokenizer.mask_token}"      )
             self.assertEqual(
                 _SCREAMING_SNAKE_CASE	,		[
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                 ]	,		)

             _a       :					Tuple	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE      )
             _a       :					str	   =fill_masker(f"This is a {tokenizer.mask_token}"	,		top_k=2      )
             self.assertEqual(
                 _SCREAMING_SNAKE_CASE	,		[
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                 ]	,		)
             self.assertEqual(nested_simplify(_SCREAMING_SNAKE_CASE      )	,		nested_simplify(_SCREAMING_SNAKE_CASE      )      )






      def __UpperCAmelCase			(					self     :Tuple	,		SCREAMING_SNAKE_CASE     :Union[str, Any]	,		SCREAMING_SNAKE_CASE     :Any      )			->   Dict:




             '''simple docstring'''
             _a       :					Optional[int]	   =tokenizer.get_vocab()
             _a       :					str	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE      )

             # top_k=2, ntargets=3
             _a       :					str	   =sorted(vocab.keys()      )[:3]
             _a       :					Dict	   =fill_masker(f"This is a {tokenizer.mask_token}"	,		top_k=2	,		targets=_SCREAMING_SNAKE_CASE      )

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






      def __UpperCAmelCase			(					self     :Any	,		SCREAMING_SNAKE_CASE     :str	,		SCREAMING_SNAKE_CASE     :Dict      )			->   str:




             '''simple docstring'''
             _a       :					Tuple	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE      )
             _a       :					int	   =tokenizer.get_vocab()
             # String duplicates + id duplicates
             _a       :					str	   =sorted(vocab.keys()      )[:3]
             _a       :					Union[str, Any]	   =[targets[0], targets[1], targets[0], targets[2], targets[1]]
             _a       :					List[Any]	   =fill_masker(f"My name is {tokenizer.mask_token}"	,		targets=_SCREAMING_SNAKE_CASE	,		top_k=1_0      )

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






      def __UpperCAmelCase			(					self     :int	,		SCREAMING_SNAKE_CASE     :Optional[Any]	,		SCREAMING_SNAKE_CASE     :Union[str, Any]      )			->   List[str]:




             '''simple docstring'''
             _a       :					str	   =FillMaskPipeline(model=_SCREAMING_SNAKE_CASE	,		tokenizer=_SCREAMING_SNAKE_CASE      )

             _a       :					List[str]	   =fill_masker(
                 f"This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"	,		top_k=2      )
             self.assertEqual(
                 _SCREAMING_SNAKE_CASE	,		[
                     [
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     ],
                     [
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     ],
                     [
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                         {"""sequence""": ANY(_SCREAMING_SNAKE_CASE      ), """score""": ANY(_SCREAMING_SNAKE_CASE      ), """token""": ANY(_SCREAMING_SNAKE_CASE      ), """token_str""": ANY(_SCREAMING_SNAKE_CASE      )},
                     ],
                 ]	,		)










def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  []
      UpperCamelCase					       =  []
      UpperCamelCase					       =  {
          '^': 3,
          '*': 2,
          '/': 2,
          '%': 2,
          '+': 1,
          '-': 1,
      }  # Priority of each operator
      UpperCamelCase					       =  len(_UpperCamelCase) if (len(_UpperCamelCase) > 7) else 7

      # Print table header for output
      print(
          'Symbol'.center(8)       ,							'Stack'.center(_UpperCamelCase)       ,							'Postfix'.center(_UpperCamelCase)       ,							sep=' | '       ,							)
      print('-' * (print_width * 3 + 7))

      for x in infix:
            if x.isalpha() or x.isdigit():
                  post_fix.append(_UpperCamelCase)  # if x is Alphabet / Digit, add it to Postfix
            elif x == "(":
                  stack.append(_UpperCamelCase)  # if x is "(" push to Stack
            elif x == ")":  # if x is ")" pop stack until "(" is encountered
                  while stack[-1] != "(":
                        post_fix.append(stack.pop())  # Pop stack & add the content to Postfix
                  stack.pop()
            else:
                  if len(_UpperCamelCase) == 0:
                        stack.append(_UpperCamelCase)  # If stack is empty, push x to stack
                  else:  # while priority of x is not > priority of element in the stack
                        while len(_UpperCamelCase) > 0 and priority[x] <= priority[stack[-1]]:
                              post_fix.append(stack.pop())  # pop stack & add to Postfix
                        stack.append(_UpperCamelCase)  # push x to stack

            print(
                x.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      while len(_UpperCamelCase) > 0:  # while stack is not empty
            post_fix.append(stack.pop())  # pop stack & add to Postfix
            print(
                ' '.center(8)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							(''.join(_UpperCamelCase)).ljust(_UpperCamelCase)       ,							sep=' | '       ,							)  # Output in tabular format

      return "".join(_UpperCamelCase)  # return Postfix as str
def 		lowercase__  (				_UpperCamelCase) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  list(infix[::-1])  # reverse the infix equation

      for i in range(len(_UpperCamelCase)):
            if infix[i] == "(":
                  UpperCamelCase					       =  ')'  # change "(" to ")"
            elif infix[i] == ")":
                  UpperCamelCase					       =  '('  # change ")" to "("

      return (infix_2_postfix(''.join(_UpperCamelCase)))[
          ::-1
      ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
       __magic_name__   :  int        =							input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
       __magic_name__   :  Optional[Any]        =							''''''.join(Infix.split())  # Remove spaces from the input
       print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')



