





"""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								=   MODEL_FOR_MASKED_LM_MAPPING
				UpperCamelCase								=   TF_MODEL_FOR_MASKED_LM_MAPPING






				def     snake_case     (			self		:   Union[str, Any]							)     ->				Optional[int]:




										'''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     snake_case     (			self		:   Any							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   pipeline(task='''fill-mask''',			model='''sshleifer/tiny-distilroberta-base''',			top_k=2,			framework='''tf'''							)
										_UpperCamelCase      : Optional[Any]         =   unmasker('''My name is <mask>'''							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__,			decimals=6							),			[
										        {'''sequence''': '''My name is grouped''', '''score''': 2.1e-0_5, '''token''': 3_8_0_1_5, '''token_str''': ''' grouped'''},
										        {'''sequence''': '''My name is accuser''', '''score''': 2.1e-0_5, '''token''': 2_5_5_0_6, '''token_str''': ''' accuser'''},
										    ],			)

										_UpperCamelCase      : Optional[int]         =   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-0_5,
										            '''token''': 3_8_0_1_5,
										            '''token_str''': ''' grouped''',
										        },
										        {
										            '''sequence''': '''The largest city in France is accuser''',
										            '''score''': 2.1e-0_5,
										            '''token''': 2_5_5_0_6,
										            '''token_str''': ''' accuser''',
										        },
										    ],			)

										_UpperCamelCase      : Dict         =   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-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
										        {'''sequence''': '''My name is Patrick''', '''score''': 2e-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
										        {'''sequence''': '''My name is Te''', '''score''': 1.9e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
										    ],			)






				@require_torch
				def     snake_case     (			self		:   Any							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   pipeline(task='''fill-mask''',			model='''sshleifer/tiny-distilroberta-base''',			top_k=2,			framework='''pt'''							)

										_UpperCamelCase      : List[str]         =   unmasker('''My name is <mask>'''							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__,			decimals=6							),			[
										        {'''sequence''': '''My name is Maul''', '''score''': 2.2e-0_5, '''token''': 3_5_6_7_6, '''token_str''': ''' Maul'''},
										        {'''sequence''': '''My name isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
										    ],			)

										_UpperCamelCase      : 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-0_5,
										            '''token''': 3_5_6_7_6,
										            '''token_str''': ''' Maul''',
										        },
										        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS'''},
										    ],			)

										_UpperCamelCase      : str         =   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-0_5, '''token''': 3_4_9_9, '''token_str''': ''' Patrick'''},
										        {'''sequence''': '''My name is Te''', '''score''': 2e-0_5, '''token''': 2_9_4_1, '''token_str''': ''' Te'''},
										        {'''sequence''': '''My name is Clara''', '''score''': 2e-0_5, '''token''': 1_3_6_0_6, '''token_str''': ''' Clara'''},
										    ],			)

										_UpperCamelCase      : Union[str, Any]         =   unmasker('''My name is <mask> <mask>''',			top_k=2							)

										self.assertEqual(
										    nested_simplify(lowerCAmelCase__,			decimals=6							),			[
										        [
										            {
										                '''score''': 2.2e-0_5,
										                '''token''': 3_5_6_7_6,
										                '''token_str''': ''' Maul''',
										                '''sequence''': '''<s>My name is Maul<mask></s>''',
										            },
										            {'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
										        ],
										        [
										            {
										                '''score''': 2.2e-0_5,
										                '''token''': 3_5_6_7_6,
										                '''token_str''': ''' Maul''',
										                '''sequence''': '''<s>My name is<mask> Maul</s>''',
										            },
										            {'''score''': 2.2e-0_5, '''token''': 1_6_4_1_6, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
										        ],
										    ],			)






				@require_torch_gpu
				def     snake_case     (			self		:   Optional[Any]							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   pipeline('''fill-mask''',			model='''hf-internal-testing/tiny-random-distilbert''',			device=0,			framework='''pt'''							)

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

										_UpperCamelCase      : List[str]         =   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     snake_case     (			self		:   Tuple							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : Any         =   pipeline(task='''fill-mask''',			model='''distilroberta-base''',			top_k=2,			framework='''pt'''							)
										self.run_large_test(lowerCAmelCase__							)






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




										'''simple docstring'''



										_UpperCamelCase      : Dict         =   pipeline(task='''fill-mask''',			model='''distilroberta-base''',			top_k=2,			framework='''tf'''							)
										self.run_large_test(lowerCAmelCase__							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Optional[Any]							)     ->				List[str]:




										'''simple docstring'''



										_UpperCamelCase      : int         =   unmasker('''My name is <mask>'''							)
										self.assertEqual(
										    nested_simplify(lowerCAmelCase__							),			[
										        {'''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'''},
										    ],			)
										_UpperCamelCase      : Optional[int]         =   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''': 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''',
										        },
										    ],			)

										_UpperCamelCase      : Union[str, 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''': 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     snake_case     (			self		:   int							)     ->				Optional[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   pipeline(task='''fill-mask''',			model='''sshleifer/tiny-distilroberta-base''',			framework='''pt'''							)
										_UpperCamelCase      : int         =   None
										_UpperCamelCase      : str         =   None
										self.run_pipeline_test(lowerCAmelCase__,			[]							)






				@require_tf
				def     snake_case     (			self		:   int							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   pipeline(task='''fill-mask''',			model='''sshleifer/tiny-distilroberta-base''',			framework='''tf'''							)
										_UpperCamelCase      : Union[str, Any]         =   None
										_UpperCamelCase      : Dict         =   None
										self.run_pipeline_test(lowerCAmelCase__,			[]							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Tuple							)     ->				Optional[int]:




										'''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)'''							)

										_UpperCamelCase      : Dict         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   [
										    f"""This is another {tokenizer.mask_token} test""",
										]
										return fill_masker, examples






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   str							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   fill_masker.tokenizer
										_UpperCamelCase      : Optional[int]         =   fill_masker.model

										_UpperCamelCase      : 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__							)},
										    ],			)

										_UpperCamelCase      : List[str]         =   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      : Optional[int]         =   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     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Union[str, Any]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   tokenizer.get_vocab()
										_UpperCamelCase      : int         =   sorted(vocab.keys()							)[:2]
										# Pipeline argument
										_UpperCamelCase      : List[Any]         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__,			targets=lowerCAmelCase__							)
										_UpperCamelCase      : 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__							)},
										    ],			)
										_UpperCamelCase      : Union[str, Any]         =   {vocab[el] for el in targets}
										self.assertEqual({el['''token'''] for el in outputs},			lowerCAmelCase__							)
										_UpperCamelCase      : str         =   [tokenizer.decode([x]							) for x in target_ids]
										self.assertEqual({el['''token_str'''] for el in outputs},			set(lowerCAmelCase__							)							)

										# Call argument
										_UpperCamelCase      : List[Any]         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__							)
										_UpperCamelCase      : 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__							)},
										    ],			)
										_UpperCamelCase      : Optional[int]         =   {vocab[el] for el in targets}
										self.assertEqual({el['''token'''] for el in outputs},			lowerCAmelCase__							)
										_UpperCamelCase      : Optional[int]         =   [tokenizer.decode([x]							) for x in target_ids]
										self.assertEqual({el['''token_str'''] for el in outputs},			set(lowerCAmelCase__							)							)

										# Score equivalence
										_UpperCamelCase      : str         =   fill_masker(f"""This is a {tokenizer.mask_token}""",			targets=lowerCAmelCase__							)
										_UpperCamelCase      : List[Any]         =   [top_mask['''token_str'''] for top_mask in outputs]
										_UpperCamelCase      : 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(lowerCAmelCase__							) == set(lowerCAmelCase__							):
																_UpperCamelCase      : Dict         =   fill_masker(f"""This is a {tokenizer.mask_token}""",			targets=lowerCAmelCase__							)
																_UpperCamelCase      : List[str]         =   [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      : Union[str, 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__							):
																						_UpperCamelCase      : str         =   fill_masker(f"""This is a {tokenizer.mask_token}""",			targets=['''''']							)
																with self.assertRaises(lowerCAmelCase__							):
																						_UpperCamelCase      : Any         =   fill_masker(f"""This is a {tokenizer.mask_token}""",			targets=''''''							)






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   Any,			lowerCAmelCase__		:   Dict							)     ->				int:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__,			top_k=2							)
										_UpperCamelCase      : Dict         =   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      : Any         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__							)
										_UpperCamelCase      : 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     snake_case     (			self		:   List[Any],			lowerCAmelCase__		:   Union[str, Any],			lowerCAmelCase__		:   Union[str, Any]							)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   tokenizer.get_vocab()
										_UpperCamelCase      : int         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__							)

										# top_k=2, ntargets=3
										_UpperCamelCase      : Tuple         =   sorted(vocab.keys()							)[:3]
										_UpperCamelCase      : Optional[int]         =   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      : Any         =   [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      : Optional[int]         =   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     snake_case     (			self		:   str,			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   int							)     ->				List[str]:




										'''simple docstring'''



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

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






				def     snake_case     (			self		:   List[str],			lowerCAmelCase__		:   List[str],			lowerCAmelCase__		:   Any							)     ->				Dict:




										'''simple docstring'''



										_UpperCamelCase      : str         =   FillMaskPipeline(model=lowerCAmelCase__,			tokenizer=lowerCAmelCase__							)

										_UpperCamelCase      : int         =   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 argparse
import json
from typing import List

from ltp import LTP

from transformers.models.bert.tokenization_bert import BertTokenizer





def 	a_    (							_lowercase       ):
						# This defines a "chinese character" as anything in the CJK Unicode block:
						#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
						#
						# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
						# despite its name. The modern Korean Hangul alphabet is a different block,
						# as is Japanese Hiragana and Katakana. Those alphabets are used to write
						# space-separated words, so they are not treated specially and handled
						# like the all of the other languages.
						if (
						    (cp >= 0X4E00 and cp <= 0X9FFF)
						    or (cp >= 0X3400 and cp <= 0X4DBF)  #
						    or (cp >= 0X20000 and cp <= 0X2A6DF)  #
						    or (cp >= 0X2A700 and cp <= 0X2B73F)  #
						    or (cp >= 0X2B740 and cp <= 0X2B81F)  #
						    or (cp >= 0X2B820 and cp <= 0X2CEAF)  #
						    or (cp >= 0XF900 and cp <= 0XFAFF)
						    or (cp >= 0X2F800 and cp <= 0X2FA1F)  #
						):  #
												return True

						return False





def 	a_    (							_lowercase       ):
						# word like '180' or '身高' or '神'
						for char in word:
												_UpperCamelCase      : Dict         =   ord(_lowercase       )
												if not _is_chinese_char(_lowercase       ):
																		return 0
						return 1





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[str]         =   set()

						for token in tokens:
												_UpperCamelCase      : int         =   len(_lowercase       ) > 1 and is_chinese(_lowercase       )
												if chinese_word:
																		word_set.add(_lowercase       )
						_UpperCamelCase      : Optional[int]         =   list(_lowercase       )
						return word_list





def 	a_    (							_lowercase	,						_lowercase       ):
						if not chinese_word_set:
												return bert_tokens
						_UpperCamelCase      : Tuple         =   max([len(_lowercase       ) for w in chinese_word_set]       )

						_UpperCamelCase      : int         =   bert_tokens
						_UpperCamelCase       ,							_UpperCamelCase      : Union[str, Any]         =   0, len(_lowercase       )
						while start < end:
												_UpperCamelCase      : Union[str, Any]         =   True
												if is_chinese(bert_word[start]       ):
																		_UpperCamelCase      : List[Any]         =   min(end - start	,						_lowercase       )
																		for i in range(_lowercase	,						1	,						-1       ):
																								_UpperCamelCase      : str         =   ''''''.join(bert_word[start : start + i]       )
																								if whole_word in chinese_word_set:
																														for j in range(start + 1	,						start + i       ):
																																				_UpperCamelCase      : int         =   '''##''' + bert_word[j]
																														_UpperCamelCase      : int         =   start + i
																														_UpperCamelCase      : Union[str, Any]         =   False
																														break
												if single_word:
																		start += 1
						return bert_word





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : List[Any]         =   []

						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   ltp_tokenizer.pipeline(lines[i : i + 100]	,						tasks=['''cws''']       ).cws
												_UpperCamelCase      : Optional[int]         =   [get_chinese_word(_lowercase       ) for r in res]
												ltp_res.extend(_lowercase       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Dict         =   []
						for i in range(0	,						len(_lowercase       )	,						100       ):
												_UpperCamelCase      : Optional[int]         =   bert_tokenizer(lines[i : i + 100]	,						add_special_tokens=_lowercase	,						truncation=_lowercase	,						max_length=512       )
												bert_res.extend(res['''input_ids''']       )
						assert len(_lowercase       ) == len(_lowercase       )

						_UpperCamelCase      : Optional[Any]         =   []
						for input_ids, chinese_word in zip(_lowercase	,						_lowercase       ):
												_UpperCamelCase      : str         =   []
												for id in input_ids:
																		_UpperCamelCase      : Dict         =   bert_tokenizer._convert_id_to_token(_lowercase       )
																		input_tokens.append(_lowercase       )
												_UpperCamelCase      : str         =   add_sub_symbol(_lowercase	,						_lowercase       )
												_UpperCamelCase      : List[str]         =   []
												# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
												for i, token in enumerate(_lowercase       ):
																		if token[:2] == "##":
																								_UpperCamelCase      : int         =   token[2:]
																								# save chinese tokens' pos
																								if len(_lowercase       ) == 1 and _is_chinese_char(ord(_lowercase       )       ):
																														ref_id.append(_lowercase       )
												ref_ids.append(_lowercase       )

						assert len(_lowercase       ) == len(_lowercase       )

						return ref_ids





def 	a_    (							_lowercase       ):
						# For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
						# If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
						with open(args.file_name	,						'''r'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : Union[str, Any]         =   f.readlines()
						_UpperCamelCase      : Tuple         =   [line.strip() for line in data if len(_lowercase       ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
						_UpperCamelCase      : List[Any]         =   LTP(args.ltp       )  # faster in GPU device
						_UpperCamelCase      : int         =   BertTokenizer.from_pretrained(args.bert       )

						_UpperCamelCase      : List[str]         =   prepare_ref(_lowercase	,						_lowercase	,						_lowercase       )

						with open(args.save_path	,						'''w'''	,						encoding='''utf-8'''       ) as f:
												_UpperCamelCase      : List[Any]         =   [json.dumps(_lowercase       ) + '''\n''' for ref in ref_ids]
												f.writelines(_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=argparse.ArgumentParser(description="""prepare_chinese_ref""")
						parser.add_argument(
						    """--file_name""",
						    required=False,
						    type=str,
						    default="""./resources/chinese-demo.txt""",
						    help="""file need process, same as training data in lm""",
						)
						parser.add_argument(
						    """--ltp""",
						    required=False,
						    type=str,
						    default="""./resources/ltp""",
						    help="""resources for LTP tokenizer, usually a path""",
						)
						parser.add_argument(
						    """--bert""",
						    required=False,
						    type=str,
						    default="""./resources/robert""",
						    help="""resources for Bert tokenizer""",
						)
						parser.add_argument(
						    """--save_path""",
						    required=False,
						    type=str,
						    default="""./resources/ref.txt""",
						    help="""path to save res""",
						)

						UpperCamelCase_									=parser.parse_args()
						main(args)



