
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    _UpperCAmelCase(   unittest.TestCase     ):
		lowercase__      =				MODEL_FOR_MASKED_LM_MAPPING
		lowercase__      =				TF_MODEL_FOR_MASKED_LM_MAPPING



		def 						UpperCAmelCase							( self)	->	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)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		top_k=2    ,		framework='''tf''')
							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is grouped''', '''score''': 2.1e-05, '''token''': 3_80_15, '''token_str''': ''' grouped'''},
							        {'''sequence''': '''My name is accuser''', '''score''': 2.1e-05, '''token''': 2_55_06, '''token_str''': ''' accuser'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {
							            '''sequence''': '''The largest city in France is grouped''',
							            '''score''': 2.1e-05,
							            '''token''': 3_80_15,
							            '''token_str''': ''' grouped''',
							        },
							        {
							            '''sequence''': '''The largest city in France is accuser''',
							            '''score''': 2.1e-05,
							            '''token''': 2_55_06,
							            '''token_str''': ''' accuser''',
							        },
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							        {'''sequence''': '''My name is Patrick''', '''score''': 2e-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Te''', '''score''': 1.9e-05, '''token''': 29_41, '''token_str''': ''' Te'''},
							    ]    ,		)



		@require_torch
		def 						UpperCAmelCase							( self)	->	Optional[int]:






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

							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Maul''', '''score''': 2.2e-05, '''token''': 3_56_76, '''token_str''': ''' Maul'''},
							        {'''sequence''': '''My name isELS''', '''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {
							            '''sequence''': '''The largest city in France is Maul''',
							            '''score''': 2.2e-05,
							            '''token''': 3_56_76,
							            '''token_str''': ''' Maul''',
							        },
							        {'''sequence''': '''The largest city in France isELS''', '''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        {'''sequence''': '''My name is Patrick''', '''score''': 2.1e-05, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Te''', '''score''': 2e-05, '''token''': 29_41, '''token_str''': ''' Te'''},
							        {'''sequence''': '''My name is Clara''', '''score''': 2e-05, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask> <mask>'''    ,		top_k=2)

							self.assertEqual(
							    nested_simplify(__a    ,		decimals=6)    ,		[
							        [
							            {
							                '''score''': 2.2e-05,
							                '''token''': 3_56_76,
							                '''token_str''': ''' Maul''',
							                '''sequence''': '''<s>My name is Maul<mask></s>''',
							            },
							            {'''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name isELS<mask></s>'''},
							        ],
							        [
							            {
							                '''score''': 2.2e-05,
							                '''token''': 3_56_76,
							                '''token_str''': ''' Maul''',
							                '''sequence''': '''<s>My name is<mask> Maul</s>''',
							            },
							            {'''score''': 2.2e-05, '''token''': 1_64_16, '''token_str''': '''ELS''', '''sequence''': '''<s>My name is<mask>ELS</s>'''},
							        ],
							    ]    ,		)



		@require_torch_gpu
		def 						UpperCAmelCase							( self)	->	int:






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

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

							_UpperCamelCase										=  pipe('''Paris is the [MASK] of France.''')
							# We actually don't care about the result, we just want to make sure
							# it works, meaning the float16 tensor got casted back to float32
							# for postprocessing.
							self.assertIsInstance(__a    ,		__a)



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






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''distilroberta-base'''    ,		top_k=2    ,		framework='''pt''')
							self.run_large_test(__a)



		@slow
		@require_tf
		def 						UpperCAmelCase							( self)	->	List[str]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''distilroberta-base'''    ,		top_k=2    ,		framework='''tf''')
							self.run_large_test(__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  unmasker('''My name is <mask>''')
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {'''sequence''': '''My name is John''', '''score''': 0.008, '''token''': 6_10, '''token_str''': ''' John'''},
							        {'''sequence''': '''My name is Chris''', '''score''': 0.007, '''token''': 15_73, '''token_str''': ''' Chris'''},
							    ]    ,		)
							_UpperCamelCase										=  unmasker('''The largest city in France is <mask>''')
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {
							            '''sequence''': '''The largest city in France is Paris''',
							            '''score''': 0.251,
							            '''token''': 22_01,
							            '''token_str''': ''' Paris''',
							        },
							        {
							            '''sequence''': '''The largest city in France is Lyon''',
							            '''score''': 0.214,
							            '''token''': 1_27_90,
							            '''token_str''': ''' Lyon''',
							        },
							    ]    ,		)

							_UpperCamelCase										=  unmasker('''My name is <mask>'''    ,		targets=[''' Patrick''', ''' Clara''', ''' Teven''']    ,		top_k=3)
							self.assertEqual(
							    nested_simplify(__a)    ,		[
							        {'''sequence''': '''My name is Patrick''', '''score''': 0.005, '''token''': 34_99, '''token_str''': ''' Patrick'''},
							        {'''sequence''': '''My name is Clara''', '''score''': 0.000, '''token''': 1_36_06, '''token_str''': ''' Clara'''},
							        {'''sequence''': '''My name is Te''', '''score''': 0.000, '''token''': 29_41, '''token_str''': ''' Te'''},
							    ]    ,		)



		@require_torch
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		framework='''pt''')
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							self.run_pipeline_test(__a    ,		[])



		@require_tf
		def 						UpperCAmelCase							( self)	->	Optional[int]:






							'''simple docstring'''
							_UpperCamelCase										=  pipeline(task='''fill-mask'''    ,		model='''sshleifer/tiny-distilroberta-base'''    ,		framework='''tf''')
							_UpperCamelCase										=  None
							_UpperCamelCase										=  None
							self.run_pipeline_test(__a    ,		[])



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Tuple:






							'''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										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  [
							    F'''This is another {tokenizer.mask_token} test''',
							]
							return fill_masker, examples



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  fill_masker.tokenizer
							_UpperCamelCase										=  fill_masker.model

							_UpperCamelCase										=  fill_masker(
							    F'''This is a {tokenizer.mask_token}'''    ,		)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  fill_masker([F'''This is a {tokenizer.mask_token}'''])
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  fill_masker([F'''This is a {tokenizer.mask_token}''', F'''Another {tokenizer.mask_token} great test.'''])
							self.assertEqual(
							    __a    ,		[
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							    ]    ,		)

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

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[Any]:






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

							# Call argument
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)
							_UpperCamelCase										=  {vocab[el] for el in targets}
							self.assertEqual({el['''token'''] for el in outputs}    ,		__a)
							_UpperCamelCase										=  [tokenizer.decode([x]) for x in target_ids]
							self.assertEqual({el['''token_str'''] for el in outputs}    ,		set(__a))

							# Score equivalence
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
							_UpperCamelCase										=  [top_mask['''token_str'''] for top_mask in outputs]
							_UpperCamelCase										=  [top_mask['''score'''] for top_mask in outputs]

							# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
							if set(__a) == set(__a):
												_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=__a)
												_UpperCamelCase										=  [top_mask['''score'''] for top_mask in unmasked_targets]
												self.assertEqual(nested_simplify(__a)    ,		nested_simplify(__a))

							# Raises with invalid
							with self.assertRaises(__a):
												_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=[])
							# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
							if "" not in tokenizer.get_vocab():
												with self.assertRaises(__a):
																	_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets=[''''''])
												with self.assertRaises(__a):
																	_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		targets='''''')



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a    ,		top_k=2)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}''')
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)

							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		top_k=2)
							self.assertEqual(
							    __a    ,		[
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							    ]    ,		)
							self.assertEqual(nested_simplify(__a)    ,		nested_simplify(__a))



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  tokenizer.get_vocab()
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)

							# top_k=2, ntargets=3
							_UpperCamelCase										=  sorted(vocab.keys())[:3]
							_UpperCamelCase										=  fill_masker(F'''This is a {tokenizer.mask_token}'''    ,		top_k=2    ,		targets=__a)

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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)
							_UpperCamelCase										=  tokenizer.get_vocab()
							# String duplicates + id duplicates
							_UpperCamelCase										=  sorted(vocab.keys())[:3]
							_UpperCamelCase										=  [targets[0], targets[1], targets[0], targets[2], targets[1]]
							_UpperCamelCase										=  fill_masker(F'''My name is {tokenizer.mask_token}'''    ,		targets=__a    ,		top_k=10)

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




		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  FillMaskPipeline(model=__a    ,		tokenizer=__a)

							_UpperCamelCase										=  fill_masker(
							    F'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''    ,		top_k=2)
							self.assertEqual(
							    __a    ,		[
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							        [
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							            {'''sequence''': ANY(__a), '''score''': ANY(__a), '''token''': ANY(__a), '''token_str''': ANY(__a)},
							        ],
							    ]    ,		)








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a														=		logging.get_logger(__name__)

_a														=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bert'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=2    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])






