



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


A__  :  str								=					logging.get_logger(__name__)

# General docstring
A__  :  List[str]								=					'''ResNetConfig'''

# Base docstring
A__  :  Tuple								=					'''microsoft/resnet-50'''
A__  :  List[str]								=					[1, 2048, 7, 7]

# Image classification docstring
A__  :  str								=					'''microsoft/resnet-50'''
A__  :  List[str]								=					'''tiger cat'''

A__  :  Dict								=					[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       int				,				A_     :       int				,				A_     :       int = 3				,				A_     :       int = 1				,				A_     :       str = "relu"):
									super().__init__()
									lowerCAmelCase_			:     Optional[Any]									= nn.Convad(
									    A_				,				A_				,				kernel_size=A_				,				stride=A_				,				padding=kernel_size // 2				,				bias=A_)
									lowerCAmelCase_			:     Any									= nn.BatchNormad(A_)
									lowerCAmelCase_			:     Optional[int]									= ACTaFN[activation] if activation is not None else nn.Identity()







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor):
									lowerCAmelCase_			:     Tuple									= self.convolution(A_)
									lowerCAmelCase_			:     str									= self.normalization(A_)
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       Optional[Any]				,				A_     :       ResNetConfig):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= ResNetConvLayer(
									    config.num_channels				,				config.embedding_size				,				kernel_size=7				,				stride=2				,				activation=config.hidden_act)
									lowerCAmelCase_			:     Any									= nn.MaxPoolad(kernel_size=3				,				stride=2				,				padding=1)
									lowerCAmelCase_			:     Dict									= config.num_channels







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     Tuple									= pixel_values.shape[1]
									if num_channels != self.num_channels:
											raise ValueError(
											    '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.''')
									lowerCAmelCase_			:     int									= self.embedder(A_)
									lowerCAmelCase_			:     List[str]									= self.pooler(A_)
									return embedding





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2):
									super().__init__()
									lowerCAmelCase_			:     Optional[Any]									= nn.Convad(A_				,				A_				,				kernel_size=1				,				stride=A_				,				bias=A_)
									lowerCAmelCase_			:     int									= nn.BatchNormad(A_)







							def    UpperCAmelCase__			(       self     :       Any				,				A_     :       Tensor):
									lowerCAmelCase_			:     str									= self.convolution(A_)
									lowerCAmelCase_			:     Optional[int]									= self.normalization(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       int				,				A_     :       int				,				A_     :       int = 1				,				A_     :       str = "relu"):
									super().__init__()
									lowerCAmelCase_			:     Tuple									= in_channels != out_channels or stride != 1
									lowerCAmelCase_			:     str									= (
									    ResNetShortCut(A_				,				A_				,				stride=A_) if should_apply_shortcut else nn.Identity()
									)
									lowerCAmelCase_			:     str									= nn.Sequential(
									    ResNetConvLayer(A_				,				A_				,				stride=A_)				,				ResNetConvLayer(A_				,				A_				,				activation=A_)				,				)
									lowerCAmelCase_			:     List[Any]									= ACTaFN[activation]







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     Optional[int]									= hidden_state
									lowerCAmelCase_			:     Any									= self.layer(A_)
									lowerCAmelCase_			:     Optional[int]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Tuple									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       Optional[int]				,				A_     :       int				,				A_     :       int				,				A_     :       int = 1				,				A_     :       str = "relu"				,				A_     :       int = 4):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= in_channels != out_channels or stride != 1
									lowerCAmelCase_			:     List[str]									= out_channels // reduction
									lowerCAmelCase_			:     Union[str, Any]									= (
									    ResNetShortCut(A_				,				A_				,				stride=A_) if should_apply_shortcut else nn.Identity()
									)
									lowerCAmelCase_			:     List[Any]									= nn.Sequential(
									    ResNetConvLayer(A_				,				A_				,				kernel_size=1)				,				ResNetConvLayer(A_				,				A_				,				stride=A_)				,				ResNetConvLayer(A_				,				A_				,				kernel_size=1				,				activation=A_)				,				)
									lowerCAmelCase_			:     int									= ACTaFN[activation]







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Dict):
									lowerCAmelCase_			:     List[str]									= hidden_state
									lowerCAmelCase_			:     Optional[Any]									= self.layer(A_)
									lowerCAmelCase_			:     Optional[Any]									= self.shortcut(A_)
									hidden_state += residual
									lowerCAmelCase_			:     Dict									= self.activation(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       int				,				A_     :       ResNetConfig				,				A_     :       int				,				A_     :       int				,				A_     :       int = 2				,				A_     :       int = 2				,				):
									super().__init__()

									lowerCAmelCase_			:     List[str]									= ResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else ResNetBasicLayer

									lowerCAmelCase_			:     int									= nn.Sequential(
									    # downsampling is done in the first layer with stride of 2
									    layer(A_				,				A_				,				stride=A_				,				activation=config.hidden_act)				,				*[layer(A_				,				A_				,				activation=config.hidden_act) for _ in range(depth - 1)]				,				)







							def    UpperCAmelCase__			(       self     :       Tuple				,				A_     :       Tensor):
									lowerCAmelCase_			:     Union[str, Any]									= input
									for layer in self.layers:
											lowerCAmelCase_			:     List[str]									= layer(A_)
									return hidden_state





class 				__snake_case						(     nn.Module						):




							def __init__(       self     :       List[str]				,				A_     :       ResNetConfig):
									super().__init__()
									lowerCAmelCase_			:     Union[str, Any]									= nn.ModuleList([])
									# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    ResNetStage(
									        A_				,				config.embedding_size				,				config.hidden_sizes[0]				,				stride=2 if config.downsample_in_first_stage else 1				,				depth=config.depths[0]				,				))
									lowerCAmelCase_			:     Tuple									= zip(config.hidden_sizes				,				config.hidden_sizes[1:])
									for (in_channels, out_channels), depth in zip(A_				,				config.depths[1:]):
											self.stages.append(ResNetStage(A_				,				A_				,				A_				,				depth=A_))







							def    UpperCAmelCase__			(       self     :       Union[str, Any]				,				A_     :       Tensor				,				A_     :       bool = False				,				A_     :       bool = True):
									lowerCAmelCase_			:     Union[str, Any]									= () if output_hidden_states else None

									for stage_module in self.stages:
											if output_hidden_states:
													lowerCAmelCase_			:     int									= hidden_states + (hidden_state,)

											lowerCAmelCase_			:     int									= stage_module(A_)

									if output_hidden_states:
											lowerCAmelCase_			:     List[Any]									= hidden_states + (hidden_state,)

									if not return_dict:
											return tuple(v for v in [hidden_state, hidden_states] if v is not None)

									return BaseModelOutputWithNoAttention(
									    last_hidden_state=A_				,				hidden_states=A_				,				)





class 				__snake_case						(     UpperCamelCase_						):
							_a			      = ResNetConfig
							_a			      = '''resnet'''
							_a			      = '''pixel_values'''
							_a			      = True




							def    UpperCAmelCase__			(       self     :       Optional[Any]				,				A_     :       Any):
									if isinstance(A_				,				nn.Convad):
											nn.init.kaiming_normal_(module.weight				,				mode='''fan_out'''				,				nonlinearity='''relu''')
									elif isinstance(A_				,				(nn.BatchNormad, nn.GroupNorm)):
											nn.init.constant_(module.weight				,				1)
											nn.init.constant_(module.bias				,				0)







							def    UpperCAmelCase__			(       self     :       str				,				A_     :       int				,				A_     :       Union[str, Any]=False):
									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     Union[str, Any]									= value





A__  :  Any								=					R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

A__  :  Union[str, Any]								=					R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''



@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Any				,				A_     :       Any):
									super().__init__(A_)
									lowerCAmelCase_			:     str									= config
									lowerCAmelCase_			:     Union[str, Any]									= ResNetEmbeddings(A_)
									lowerCAmelCase_			:     Dict									= ResNetEncoder(A_)
									lowerCAmelCase_			:     Any									= nn.AdaptiveAvgPoolad((1, 1))
									# Initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC				,				output_type=A_				,				config_class=_CONFIG_FOR_DOC				,				modality='''vision'''				,				expected_output=_EXPECTED_OUTPUT_SHAPE				,				)
							def    UpperCAmelCase__			(       self     :       Optional[int]				,				A_     :       Tensor				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None):
									lowerCAmelCase_			:     Optional[int]									= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									lowerCAmelCase_			:     Optional[int]									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Tuple									= self.embedder(A_)

									lowerCAmelCase_			:     Tuple									= self.encoder(
									    A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     List[Any]									= encoder_outputs[0]

									lowerCAmelCase_			:     str									= self.pooler(A_)

									if not return_dict:
											return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=A_				,				pooler_output=A_				,				hidden_states=encoder_outputs.hidden_states				,				)





@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       List[str]):
									super().__init__(A_)
									lowerCAmelCase_			:     Optional[Any]									= config.num_labels
									lowerCAmelCase_			:     Union[str, Any]									= ResNetModel(A_)
									# classification head
									lowerCAmelCase_			:     List[str]									= nn.Sequential(
									    nn.Flatten()				,				nn.Linear(config.hidden_sizes[-1]				,				config.num_labels) if config.num_labels > 0 else nn.Identity()				,				)
									# initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@add_code_sample_docstrings(
							    checkpoint=_IMAGE_CLASS_CHECKPOINT				,				output_type=A_				,				config_class=_CONFIG_FOR_DOC				,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,				)
							def    UpperCAmelCase__			(       self     :       int				,				A_     :       Optional[torch.FloatTensor] = None				,				A_     :       Optional[torch.LongTensor] = None				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None				,				):
									lowerCAmelCase_			:     Dict									= return_dict if return_dict is not None else self.config.use_return_dict

									lowerCAmelCase_			:     Dict									= self.resnet(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Optional[Any]									= outputs.pooler_output if return_dict else outputs[1]

									lowerCAmelCase_			:     List[str]									= self.classifier(A_)

									lowerCAmelCase_			:     List[str]									= None

									if labels is not None:
											if self.config.problem_type is None:
													if self.num_labels == 1:
															lowerCAmelCase_			:     List[Any]									= '''regression'''
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
															lowerCAmelCase_			:     Optional[Any]									= '''single_label_classification'''
													else:
															lowerCAmelCase_			:     str									= '''multi_label_classification'''
											if self.config.problem_type == "regression":
													lowerCAmelCase_			:     List[str]									= MSELoss()
													if self.num_labels == 1:
															lowerCAmelCase_			:     List[Any]									= loss_fct(logits.squeeze()				,				labels.squeeze())
													else:
															lowerCAmelCase_			:     Dict									= loss_fct(A_				,				A_)
											elif self.config.problem_type == "single_label_classification":
													lowerCAmelCase_			:     int									= CrossEntropyLoss()
													lowerCAmelCase_			:     Optional[int]									= loss_fct(logits.view(-1				,				self.num_labels)				,				labels.view(-1))
											elif self.config.problem_type == "multi_label_classification":
													lowerCAmelCase_			:     List[str]									= BCEWithLogitsLoss()
													lowerCAmelCase_			:     Tuple									= loss_fct(A_				,				A_)

									if not return_dict:
											lowerCAmelCase_			:     int									= (logits,) + outputs[2:]
											return (loss,) + output if loss is not None else output

									return ImageClassifierOutputWithNoAttention(loss=A_				,				logits=A_				,				hidden_states=outputs.hidden_states)





@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''						,UpperCamelCase_						,)
class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):




							def __init__(       self     :       Dict				,				A_     :       int):
									super().__init__(A_)
									super()._init_backbone(A_)

									lowerCAmelCase_			:     List[Any]									= [config.embedding_size] + config.hidden_sizes
									lowerCAmelCase_			:     Dict									= ResNetEmbeddings(A_)
									lowerCAmelCase_			:     Any									= ResNetEncoder(A_)

									# initialize weights and apply final processing
									self.post_init()







							@add_start_docstrings_to_model_forward(A_)
							@replace_return_docstrings(output_type=A_				,				config_class=_CONFIG_FOR_DOC)
							def    UpperCAmelCase__			(       self     :       List[str]				,				A_     :       Tensor				,				A_     :       Optional[bool] = None				,				A_     :       Optional[bool] = None):
									lowerCAmelCase_			:     Optional[Any]									= return_dict if return_dict is not None else self.config.use_return_dict
									lowerCAmelCase_			:     Optional[int]									= (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)

									lowerCAmelCase_			:     Any									= self.embedder(A_)

									lowerCAmelCase_			:     str									= self.encoder(A_				,				output_hidden_states=A_				,				return_dict=A_)

									lowerCAmelCase_			:     Union[str, Any]									= outputs.hidden_states

									lowerCAmelCase_			:     List[Any]									= ()
									for idx, stage in enumerate(self.stage_names):
											if stage in self.out_features:
													feature_maps += (hidden_states[idx],)

									if not return_dict:
											lowerCAmelCase_			:     Any									= (feature_maps,)
											if output_hidden_states:
													output += (outputs.hidden_states,)
											return output

									return BackboneOutput(
									    feature_maps=A_				,				hidden_states=outputs.hidden_states if output_hidden_states else None				,				attentions=A_				,				)










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_)





