



import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class 				__snake_case						(     UpperCamelCase_						):
							# to overwrite at feature extractactor specific tests
							_a			      = None
							_a			      = None




							@property
							def    UpperCAmelCase__			(       self     :       int):
									return self.feat_extract_tester.prepare_feat_extract_dict()




							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     List[str]									= self.feature_extraction_class(**self.feat_extract_dict)
									self.assertTrue(hasattr(A_				,				'''feature_size'''))
									self.assertTrue(hasattr(A_				,				'''sampling_rate'''))
									self.assertTrue(hasattr(A_				,				'''padding_value'''))




							def    UpperCAmelCase__			(       self     :       Dict):
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     List[Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     int									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     str									= BatchFeature({input_name: speech_inputs})

									self.assertTrue(all(len(A_) == len(A_) for x, y in zip(A_				,				processed_features[input_name])))

									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Union[str, Any]									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''np''')

									lowerCAmelCase_			:     Optional[Any]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     Union[str, Any]									= batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))




							@require_torch
							def    UpperCAmelCase__			(       self     :       Any):
									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Optional[Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     List[Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     int									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''pt''')

									lowerCAmelCase_			:     List[str]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     Any									= batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))




							@require_tf
							def    UpperCAmelCase__			(       self     :       Optional[int]):
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common(equal_length=A_)
									lowerCAmelCase_			:     Union[str, Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     Optional[Any]									= BatchFeature({input_name: speech_inputs}				,				tensor_type='''tf''')

									lowerCAmelCase_			:     Optional[Any]									= processed_features[input_name]

									if len(batch_features_input.shape) < 3:
											lowerCAmelCase_			:     List[str]									= batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]=False):
									def _inputs_have_equal_length(A_     :       Optional[Any]):
											lowerCAmelCase_			:     Optional[Any]									= len(input[0])
											for input_slice in input[1:]:
													if len(A_) != length:
															return False
											return True

									def _inputs_are_equal(A_     :       Union[str, Any]				,				A_     :       str):
											if len(A_) != len(A_):
													return False

											for input_slice_a, input_slice_a in zip(A_				,				A_):
													if not np.allclose(np.asarray(A_)				,				np.asarray(A_)				,				atol=1e-3):
															return False
											return True

									lowerCAmelCase_			:     int									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Dict									= self.feat_extract_tester.prepare_inputs_for_common(numpify=A_)
									lowerCAmelCase_			:     List[Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[str]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Optional[int]									= self.feat_extract_tester.seq_length_diff
									lowerCAmelCase_			:     int									= self.feat_extract_tester.max_seq_length + pad_diff
									lowerCAmelCase_			:     List[str]									= self.feat_extract_tester.min_seq_length
									lowerCAmelCase_			:     int									= self.feat_extract_tester.batch_size
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.feature_size

									# test padding for List[int] + numpy
									lowerCAmelCase_			:     Dict									= feat_extract.pad(A_				,				padding=A_)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									lowerCAmelCase_			:     Optional[int]									= feat_extract.pad(A_				,				padding='''longest''')
									lowerCAmelCase_			:     List[str]									= input_a[input_name]

									lowerCAmelCase_			:     Optional[Any]									= feat_extract.pad(A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[-1]))
									lowerCAmelCase_			:     Any									= input_a[input_name]

									lowerCAmelCase_			:     Any									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									# max_length parameter has to be provided when setting `padding="max_length"`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''max_length''')[input_name]

									lowerCAmelCase_			:     Dict									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=A_				,				return_tensors='''np''')
									lowerCAmelCase_			:     str									= input_a[input_name]

									self.assertFalse(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_are_equal(A_				,				A_))
									self.assertTrue(len(input_a[0]) == pad_min_length)
									self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
									self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
									self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

									if feature_size > 1:
											self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

									# test padding for `pad_to_multiple_of` for List[int] + numpy
									lowerCAmelCase_			:     Optional[int]									= feat_extract.pad(A_				,				pad_to_multiple_of=1_0)
									lowerCAmelCase_			:     List[Any]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(A_				,				padding='''longest'''				,				pad_to_multiple_of=1_0)
									lowerCAmelCase_			:     Optional[int]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				pad_to_multiple_of=1_0				,				max_length=A_)
									lowerCAmelCase_			:     Optional[Any]									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				pad_to_multiple_of=1_0				,				max_length=A_				,				return_tensors='''np'''				,				)
									lowerCAmelCase_			:     Union[str, Any]									= input_a[input_name]

									self.assertTrue(all(len(A_) % 1_0 == 0 for x in input_a))
									self.assertTrue(_inputs_are_equal(A_				,				A_))

									lowerCAmelCase_			:     str									= pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
									self.assertTrue(all(len(A_) == expected_mult_pad_length for x in input_a))
									self.assertEqual(input_a.shape[:2]				,				(batch_size, expected_mult_pad_length))

									if feature_size > 1:
											self.assertTrue(input_a.shape[2] == feature_size)

									# Check padding value is correct
									lowerCAmelCase_			:     List[Any]									= (np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
									self.assertTrue(
									    abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
									    < 1e-3)
									self.assertTrue(
									    abs(
									        np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
									    < 1e-3)
									self.assertTrue(
									    abs(
									        np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
									        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
									    < 1e-3)
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1e-3)
									self.assertTrue(
									    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
									    < 1e-3)




							def    UpperCAmelCase__			(       self     :       List[Any]				,				A_     :       List[Any]=False):
									def _inputs_have_equal_length(A_     :       Optional[Any]):
											lowerCAmelCase_			:     Optional[Any]									= len(input[0])
											for input_slice in input[1:]:
													if len(A_) != length:
															return False
											return True

									def _inputs_are_equal(A_     :       List[str]				,				A_     :       Optional[Any]):
											if len(A_) != len(A_):
													return False

											for input_slice_a, input_slice_a in zip(A_				,				A_):
													if not np.allclose(np.asarray(A_)				,				np.asarray(A_)				,				atol=1e-3):
															return False
											return True

									lowerCAmelCase_			:     str									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     Tuple									= self.feat_extract_tester.prepare_inputs_for_common(numpify=A_)
									lowerCAmelCase_			:     Any									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[Any]									= BatchFeature({input_name: speech_inputs})

									# truncate to smallest
									lowerCAmelCase_			:     int									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				truncation=A_)
									lowerCAmelCase_			:     int									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0]))
									lowerCAmelCase_			:     str									= input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertFalse(_inputs_have_equal_length(A_))

									# truncate to smallest with np
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np'''				,				truncation=A_				,				)
									lowerCAmelCase_			:     Tuple									= input_a[input_name]

									lowerCAmelCase_			:     Any									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				return_tensors='''np''')
									lowerCAmelCase_			:     Optional[Any]									= input_a[input_name]

									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(A_))

									# truncate to middle
									lowerCAmelCase_			:     List[Any]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=A_				,				return_tensors='''np'''				,				)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									lowerCAmelCase_			:     int									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				truncation=A_)
									lowerCAmelCase_			:     int									= input_a[input_name]

									lowerCAmelCase_			:     Tuple									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[1])				,				return_tensors='''np''')
									lowerCAmelCase_			:     int									= input_a[input_name]

									self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertTrue(_inputs_are_equal(A_				,				A_))

									# since truncation forces padding to be smaller than longest input
									# function can't return `np.ndarray`, but has to return list
									self.assertFalse(_inputs_have_equal_length(A_))
									self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				truncation=A_)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''longest'''				,				truncation=A_)[input_name]

									# padding has to be max_length when setting `truncation=True`
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''longest'''				,				truncation=A_)[input_name]

									# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
									with self.assertRaises(A_):
											feat_extract.pad(A_				,				padding='''max_length'''				,				truncation=A_)[input_name]

									# test truncation for `pad_to_multiple_of` for List[int] + numpy
									lowerCAmelCase_			:     Dict									= 1_2
									lowerCAmelCase_			:     Dict									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=A_				,				truncation=A_				,				)
									lowerCAmelCase_			:     Any									= input_a[input_name]

									lowerCAmelCase_			:     List[str]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=len(speech_inputs[0])				,				pad_to_multiple_of=A_				,				)
									lowerCAmelCase_			:     Dict									= input_a[input_name]

									# retrieve expected_length as multiple of pad_to_multiple_of
									lowerCAmelCase_			:     Optional[int]									= len(speech_inputs[0])
									if expected_length % pad_to_multiple_of != 0:
											lowerCAmelCase_			:     Any									= ((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

									self.assertTrue(len(input_a[0]) == expected_length)
									self.assertTrue(_inputs_have_equal_length(A_))
									self.assertFalse(_inputs_have_equal_length(A_))




							def    UpperCAmelCase__			(       self     :       List[Any]):
									self._check_padding(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Tuple):
									self._check_padding(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Dict):
									self._check_truncation(numpify=A_)




							def    UpperCAmelCase__			(       self     :       Dict):
									self._check_truncation(numpify=A_)




							@require_torch
							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     List[str]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     int									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     Dict									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[str]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
									lowerCAmelCase_			:     str									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''pt''')[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)




							@require_tf
							def    UpperCAmelCase__			(       self     :       Tuple):
									lowerCAmelCase_			:     Union[str, Any]									= self.feature_extraction_class(**self.feat_extract_dict)
									lowerCAmelCase_			:     List[str]									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     Optional[int]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     List[Any]									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     Optional[Any]									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')[input_name]
									lowerCAmelCase_			:     List[str]									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''tf''')[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1e-2)




							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     Union[str, Any]									= self.feat_extract_dict
									lowerCAmelCase_			:     Optional[Any]									= True
									lowerCAmelCase_			:     str									= self.feature_extraction_class(**A_)
									lowerCAmelCase_			:     List[Any]									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     List[str]									= [len(A_) for x in speech_inputs]
									lowerCAmelCase_			:     Tuple									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     int									= BatchFeature({input_name: speech_inputs})

									lowerCAmelCase_			:     int									= feat_extract.pad(A_				,				padding='''longest'''				,				return_tensors='''np''')
									self.assertIn('''attention_mask'''				,				A_)
									self.assertListEqual(list(processed.attention_mask.shape)				,				list(processed[input_name].shape[:2]))
									self.assertListEqual(processed.attention_mask.sum(-1).tolist()				,				A_)







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     int									= self.feat_extract_dict
									lowerCAmelCase_			:     Optional[int]									= True
									lowerCAmelCase_			:     str									= self.feature_extraction_class(**A_)
									lowerCAmelCase_			:     str									= self.feat_extract_tester.prepare_inputs_for_common()
									lowerCAmelCase_			:     str									= [len(A_) for x in speech_inputs]
									lowerCAmelCase_			:     Union[str, Any]									= feat_extract.model_input_names[0]

									lowerCAmelCase_			:     Tuple									= BatchFeature({input_name: speech_inputs})
									lowerCAmelCase_			:     Any									= min(A_)

									lowerCAmelCase_			:     List[str]									= feat_extract.pad(
									    A_				,				padding='''max_length'''				,				max_length=A_				,				truncation=A_				,				return_tensors='''np''')
									self.assertIn('''attention_mask'''				,				A_)
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape)				,				[processed_pad[input_name].shape[0], max_length])
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1).tolist()				,				[max_length for x in speech_inputs])










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer


A__  :  str								=					logging.get_logger(__name__)

A__  :  Any								=					{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A__  :  str								=					{
    '''vocab_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'''
        ),
        '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt''',
        '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''google/realm-cc-news-pretrained-embedder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'''
        ),
        '''google/realm-cc-news-pretrained-encoder''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-scorer''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'''
        ),
        '''google/realm-cc-news-pretrained-openqa''': (
            '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-nq-reader''': (
            '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-openqa''': (
            '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'''
        ),
        '''google/realm-orqa-wq-reader''': (
            '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'''
        ),
    },
}

A__  :  Union[str, Any]								=					{
    '''google/realm-cc-news-pretrained-embedder''': 512,
    '''google/realm-cc-news-pretrained-encoder''': 512,
    '''google/realm-cc-news-pretrained-scorer''': 512,
    '''google/realm-cc-news-pretrained-openqa''': 512,
    '''google/realm-orqa-nq-openqa''': 512,
    '''google/realm-orqa-nq-reader''': 512,
    '''google/realm-orqa-wq-openqa''': 512,
    '''google/realm-orqa-wq-reader''': 512,
}

A__  :  Dict								=					{
    '''google/realm-cc-news-pretrained-embedder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-encoder''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-scorer''': {'''do_lower_case''': True},
    '''google/realm-cc-news-pretrained-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-nq-reader''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-openqa''': {'''do_lower_case''': True},
    '''google/realm-orqa-wq-reader''': {'''do_lower_case''': True},
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = VOCAB_FILES_NAMES
							_a			      = PRETRAINED_VOCAB_FILES_MAP
							_a			      = PRETRAINED_INIT_CONFIGURATION
							_a			      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_a			      = RealmTokenizer




							def __init__(       self     :       int				,				A_     :       Optional[int]=None				,				A_     :       Optional[Any]=None				,				A_     :       Optional[Any]=True				,				A_     :       Optional[int]="[UNK]"				,				A_     :       List[Any]="[SEP]"				,				A_     :       List[Any]="[PAD]"				,				A_     :       Optional[Any]="[CLS]"				,				A_     :       Dict="[MASK]"				,				A_     :       List[Any]=True				,				A_     :       List[str]=None				,				**A_     :       List[str]				,				):
									super().__init__(
									    A_				,				tokenizer_file=A_				,				do_lower_case=A_				,				unk_token=A_				,				sep_token=A_				,				pad_token=A_				,				cls_token=A_				,				mask_token=A_				,				tokenize_chinese_chars=A_				,				strip_accents=A_				,				**A_				,				)

									lowerCAmelCase_			:     Optional[int]									= json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get('''lowercase'''				,				A_) != do_lower_case
									    or normalizer_state.get('''strip_accents'''				,				A_) != strip_accents
									    or normalizer_state.get('''handle_chinese_chars'''				,				A_) != tokenize_chinese_chars
									):
											lowerCAmelCase_			:     int									= getattr(A_				,				normalizer_state.pop('''type'''))
											lowerCAmelCase_			:     str									= do_lower_case
											lowerCAmelCase_			:     Dict									= strip_accents
											lowerCAmelCase_			:     Optional[Any]									= tokenize_chinese_chars
											lowerCAmelCase_			:     Union[str, Any]									= normalizer_class(**A_)

									lowerCAmelCase_			:     Any									= do_lower_case




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Optional[Any]				,				**A_     :       Tuple):
									lowerCAmelCase_			:     List[str]									= PaddingStrategy.MAX_LENGTH

									lowerCAmelCase_			:     str									= text
									lowerCAmelCase_			:     int									= kwargs.pop('''text_pair'''				,				A_)
									lowerCAmelCase_			:     str									= kwargs.pop('''return_tensors'''				,				A_)

									lowerCAmelCase_			:     int									= {
									    '''input_ids''': [],
									    '''attention_mask''': [],
									    '''token_type_ids''': [],
									}

									for idx, candidate_text in enumerate(A_):
											if batch_text_pair is not None:
													lowerCAmelCase_			:     List[Any]									= batch_text_pair[idx]
											else:
													lowerCAmelCase_			:     List[Any]									= None

											lowerCAmelCase_			:     int									= super().__call__(A_				,				A_				,				return_tensors=A_				,				**A_)

											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''input_ids''')
											lowerCAmelCase_			:     List[str]									= encoded_candidates.get('''attention_mask''')
											lowerCAmelCase_			:     Optional[Any]									= encoded_candidates.get('''token_type_ids''')

											if encoded_input_ids is not None:
													output_data["input_ids"].append(A_)
											if encoded_attention_mask is not None:
													output_data["attention_mask"].append(A_)
											if encoded_token_type_ids is not None:
													output_data["token_type_ids"].append(A_)

									lowerCAmelCase_			:     List[str]									= {key: item for key, item in output_data.items() if len(A_) != 0}

									return BatchEncoding(A_				,				tensor_type=A_)




							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Tuple				,				A_     :       List[Any]=None):
									lowerCAmelCase_			:     Optional[Any]									= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
											output += token_ids_a + [self.sep_token_id]

									return output




							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       List[int]				,				A_     :       Optional[List[int]] = None):
									lowerCAmelCase_			:     Tuple									= [self.sep_token_id]
									lowerCAmelCase_			:     Union[str, Any]									= [self.cls_token_id]
									if token_ids_a is None:
											return len(cls + token_ids_a + sep) * [0]
									return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]







							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       str				,				A_     :       Optional[str] = None):
									lowerCAmelCase_			:     List[str]									= self._tokenizer.model.save(A_				,				name=A_)
									return tuple(A_)





