





"""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"""




from ...processing_utils import ProcessorMixin




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   ['''image_processor''', '''feature_extractor''']
				UpperCamelCase								=   '''TvltImageProcessor'''
				UpperCamelCase								=   '''TvltFeatureExtractor'''






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   List[str]							)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(image_processor=lowerCAmelCase__,			feature_extractor=lowerCAmelCase__							)

										_UpperCamelCase      : List[str]         =   image_processor
										_UpperCamelCase      : int         =   feature_extractor






				def __call__(			self		:   List[str],			lowerCAmelCase__		:   Optional[int]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Optional[int]=False,			lowerCAmelCase__		:   str=False,			*lowerCAmelCase__		:   List[str],			**lowerCAmelCase__		:   Optional[int],			)     ->				Dict:




										'''simple docstring'''



										if images is None and audio is None:
																raise ValueError('''You need to specify either an `images` or `audio` input to process.'''							)

										_UpperCamelCase      : Optional[int]         =   None
										if images is not None:
																_UpperCamelCase      : Optional[int]         =   self.image_processor(lowerCAmelCase__,			mask_pixel=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if images_mixed is not None:
																_UpperCamelCase      : str         =   self.image_processor(lowerCAmelCase__,			is_mixed=lowerCAmelCase__,			*lowerCAmelCase__,			**lowerCAmelCase__							)
										if audio is not None:
																_UpperCamelCase      : Union[str, Any]         =   self.feature_extractor(
																    lowerCAmelCase__,			*lowerCAmelCase__,			sampling_rate=lowerCAmelCase__,			mask_audio=lowerCAmelCase__,			**lowerCAmelCase__							)

										_UpperCamelCase      : str         =   {}
										if audio is not None:
																output_dict.update(lowerCAmelCase__							)
										if images is not None:
																output_dict.update(lowerCAmelCase__							)
										if images_mixed_dict is not None:
																output_dict.update(lowerCAmelCase__							)
										return output_dict






				@property
				def     snake_case     (			self		:   List[str]							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[str]         =   self.image_processor.model_input_names
										_UpperCamelCase      : List[Any]         =   self.feature_extractor.model_input_names
										return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names							)							)



