import pickle

import numpy as np
from matplotlib import pyplot as plt


class   lowercase      :




							def __init__(  self				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   int=0.2		,							_UpperCamelCase				:   List[str]=0.2 )   ->			Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    bp_numa
												SCREAMING_SNAKE_CASE           =    bp_numa
												SCREAMING_SNAKE_CASE           =    bp_numa
												SCREAMING_SNAKE_CASE           =    conva_get[:2]
												SCREAMING_SNAKE_CASE           =    conva_get[2]
												SCREAMING_SNAKE_CASE           =    size_pa
												SCREAMING_SNAKE_CASE           =    rate_w
												SCREAMING_SNAKE_CASE           =    rate_t
												SCREAMING_SNAKE_CASE           =    [
												    np.mat(-1 * np.random.rand(self.conva[0]		,							self.conva[0] ) + 0.5 )
												    for i in range(self.conva[1] )
												]
												SCREAMING_SNAKE_CASE           =    np.mat(-1 * np.random.rand(self.num_bpa		,							self.num_bpa ) + 0.5 )
												SCREAMING_SNAKE_CASE           =    np.mat(-1 * np.random.rand(self.num_bpa		,							self.num_bpa ) + 0.5 )
												SCREAMING_SNAKE_CASE           =    -2 * np.random.rand(self.conva[1] ) + 1
												SCREAMING_SNAKE_CASE           =    -2 * np.random.rand(self.num_bpa ) + 1
												SCREAMING_SNAKE_CASE           =    -2 * np.random.rand(self.num_bpa ) + 1




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   str )   ->			Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    {
												    "num_bp1": self.num_bpa,
												    "num_bp2": self.num_bpa,
												    "num_bp3": self.num_bpa,
												    "conv1": self.conva,
												    "step_conv1": self.step_conva,
												    "size_pooling1": self.size_poolinga,
												    "rate_weight": self.rate_weight,
												    "rate_thre": self.rate_thre,
												    "w_conv1": self.w_conva,
												    "wkj": self.wkj,
												    "vji": self.vji,
												    "thre_conv1": self.thre_conva,
												    "thre_bp2": self.thre_bpa,
												    "thre_bp3": self.thre_bpa,
												}
												with open(_UpperCamelCase		,							"wb" ) as f:
																	pickle.dump(_UpperCamelCase		,							_UpperCamelCase )

												print(F"Model saved： {save_path}" )




							@classmethod
							def 		__snake_case(  cls				:   Optional[Any]		,							_UpperCamelCase				:   Optional[Any] )   ->			int:



												'''simple docstring'''

												with open(_UpperCamelCase		,							"rb" ) as f:
																	SCREAMING_SNAKE_CASE           =    pickle.load(_UpperCamelCase )  # noqa: S301

												SCREAMING_SNAKE_CASE           =    model_dic.get("conv1" )
												conv_get.append(model_dic.get("step_conv1" ) )
												SCREAMING_SNAKE_CASE           =    model_dic.get("size_pooling1" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("num_bp1" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("num_bp2" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("num_bp3" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("rate_weight" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("rate_thre" )
												# create model instance
												SCREAMING_SNAKE_CASE           =    CNN(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
												# modify model parameter
												SCREAMING_SNAKE_CASE           =    model_dic.get("w_conv1" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("wkj" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("vji" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("thre_conv1" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("thre_bp2" )
												SCREAMING_SNAKE_CASE           =    model_dic.get("thre_bp3" )
												return conv_ins




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Dict )   ->			str:



												'''simple docstring'''

												return 1 / (1 + np.exp(-1 * x ))




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Union[str, Any] )   ->			Optional[int]:



												'''simple docstring'''

												return round(_UpperCamelCase		,							3 )




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Tuple )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    convs[0]
												SCREAMING_SNAKE_CASE           =    convs[1]
												SCREAMING_SNAKE_CASE           =    np.shape(_UpperCamelCase )[0]
												# get the data slice of original image data, data_focus
												SCREAMING_SNAKE_CASE           =    []
												for i_focus in range(0		,							size_data - size_conv + 1		,							_UpperCamelCase ):
																	for j_focus in range(0		,							size_data - size_conv + 1		,							_UpperCamelCase ):
																						SCREAMING_SNAKE_CASE           =    data[
																						    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																						]
																						data_focus.append(_UpperCamelCase )
        # calculate the feature map of every single kernel, and saved as list of matrix
												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    int((size_data - size_conv) / conv_step + 1 )
												for i_map in range(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    []
																	for i_focus in range(len(_UpperCamelCase ) ):
																						SCREAMING_SNAKE_CASE           =    (
																						    np.sum(np.multiply(data_focus[i_focus]		,							w_convs[i_map] ) )
																						    - thre_convs[i_map]
																						)
																						featuremap.append(self.sig(_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    np.asmatrix(_UpperCamelCase ).reshape(
																	    _UpperCamelCase		,							_UpperCamelCase )
																	data_featuremap.append(_UpperCamelCase )

												# expanding the data slice to One dimenssion
												SCREAMING_SNAKE_CASE           =    []
												for each_focus in data_focus:
																	focusa_list.extend(self.Expand_Mat(_UpperCamelCase ) )
												SCREAMING_SNAKE_CASE           =    np.asarray(_UpperCamelCase )
												return focus_list, data_featuremap




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Any="average_pool" )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    len(featuremaps[0] )
												SCREAMING_SNAKE_CASE           =    int(size_map / size_pooling )
												SCREAMING_SNAKE_CASE           =    []
												for i_map in range(len(_UpperCamelCase ) ):
																	SCREAMING_SNAKE_CASE           =    featuremaps[i_map]
																	SCREAMING_SNAKE_CASE           =    []
																	for i_focus in range(0		,							_UpperCamelCase		,							_UpperCamelCase ):
																						for j_focus in range(0		,							_UpperCamelCase		,							_UpperCamelCase ):
																											SCREAMING_SNAKE_CASE           =    feature_map[
																											    i_focus : i_focus + size_pooling,
																											    j_focus : j_focus + size_pooling,
																											]
																											if pooling_type == "average_pool":
																																# average pooling
																																map_pooled.append(np.average(_UpperCamelCase ) )
																											elif pooling_type == "max_pooling":
																																# max pooling
																																map_pooled.append(np.max(_UpperCamelCase ) )
																	SCREAMING_SNAKE_CASE           =    np.asmatrix(_UpperCamelCase ).reshape(_UpperCamelCase		,							_UpperCamelCase )
																	featuremap_pooled.append(_UpperCamelCase )
												return featuremap_pooled




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   int )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												for i in range(len(_UpperCamelCase ) ):
																	SCREAMING_SNAKE_CASE           =    np.shape(data[i] )
																	SCREAMING_SNAKE_CASE           =    data[i].reshape(1		,							shapes[0] * shapes[1] )
																	SCREAMING_SNAKE_CASE           =    data_listed.getA().tolist()[0]
																	data_expanded.extend(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    np.asarray(_UpperCamelCase )
												return data_expanded




							def 		__snake_case(  self				:   str		,							_UpperCamelCase				:   Optional[Any] )   ->			Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    np.asarray(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    np.shape(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    data_mat.reshape(1		,							shapes[0] * shapes[1] )
												return data_expanded




							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    0
												for i_map in range(_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    np.ones((size_map, size_map) )
																	for i in range(0		,							_UpperCamelCase		,							_UpperCamelCase ):
																						for j in range(0		,							_UpperCamelCase		,							_UpperCamelCase ):
																											SCREAMING_SNAKE_CASE           =    pd_pool[
																											    i_pool
																											]
																											SCREAMING_SNAKE_CASE           =    i_pool + 1
																	SCREAMING_SNAKE_CASE           =    np.multiply(
																	    _UpperCamelCase		,							np.multiply(out_map[i_map]		,							(1 - out_map[i_map]) ) )
																	pd_all.append(_UpperCamelCase )
												return pd_all




							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Any		,							_UpperCamelCase				:   int		,							_UpperCamelCase				:   Dict		,							_UpperCamelCase				:   List[str]		,							_UpperCamelCase				:   Any=bool )   ->			List[str]:



												'''simple docstring'''

												print("----------------------Start Training-------------------------" )
												print((" - - Shape: Train_Data  ", np.shape(_UpperCamelCase )) )
												print((" - - Shape: Teach_Data  ", np.shape(_UpperCamelCase )) )
												SCREAMING_SNAKE_CASE           =    0
												SCREAMING_SNAKE_CASE           =    []
												SCREAMING_SNAKE_CASE           =    10_000
												while rp < n_repeat and mse >= error_accuracy:
																	SCREAMING_SNAKE_CASE           =    0
																	print(F"-------------Learning Time {rp}--------------" )
																	for p in range(len(_UpperCamelCase ) ):
																						# print('------------Learning Image: %d--------------'%p)
																						SCREAMING_SNAKE_CASE           =    np.asmatrix(datas_train[p] )
																						SCREAMING_SNAKE_CASE           =    np.asarray(datas_teach[p] )
																						SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.convolute(
																						    _UpperCamelCase		,							self.conva		,							self.w_conva		,							self.thre_conva		,							conv_step=self.step_conva		,							)
																						SCREAMING_SNAKE_CASE           =    self.pooling(_UpperCamelCase		,							self.size_poolinga )
																						SCREAMING_SNAKE_CASE           =    np.shape(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    self._expand(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    data_bp_input

																						SCREAMING_SNAKE_CASE           =    np.dot(_UpperCamelCase		,							self.vji.T ) - self.thre_bpa
																						SCREAMING_SNAKE_CASE           =    self.sig(_UpperCamelCase )
																						SCREAMING_SNAKE_CASE           =    np.dot(_UpperCamelCase		,							self.wkj.T ) - self.thre_bpa
																						SCREAMING_SNAKE_CASE           =    self.sig(_UpperCamelCase )

																						# --------------Model Leaning ------------------------
																						# calculate error and gradient---------------
																						SCREAMING_SNAKE_CASE           =    np.multiply(
																						    (data_teach - bp_outa)		,							np.multiply(_UpperCamelCase		,							(1 - bp_outa) ) )
																						SCREAMING_SNAKE_CASE           =    np.multiply(
																						    np.dot(_UpperCamelCase		,							self.wkj )		,							np.multiply(_UpperCamelCase		,							(1 - bp_outa) ) )
																						SCREAMING_SNAKE_CASE           =    np.dot(_UpperCamelCase		,							self.vji )

																						SCREAMING_SNAKE_CASE           =    pd_i_all / (self.size_poolinga * self.size_poolinga)
																						SCREAMING_SNAKE_CASE           =    pd_conva_pooled.T.getA().tolist()
																						SCREAMING_SNAKE_CASE           =    self._calculate_gradient_from_pool(
																						    _UpperCamelCase		,							_UpperCamelCase		,							shape_featuremapa[0]		,							shape_featuremapa[1]		,							self.size_poolinga		,							)
																						# weight and threshold learning process---------
																						# convolution layer
																						for k_conv in range(self.conva[1] ):
																											SCREAMING_SNAKE_CASE           =    self._expand_mat(pd_conva_all[k_conv] )
																											SCREAMING_SNAKE_CASE           =    self.rate_weight * np.dot(_UpperCamelCase		,							_UpperCamelCase )

																											SCREAMING_SNAKE_CASE           =    self.w_conva[k_conv] + delta_w.reshape(
																											    (self.conva[0], self.conva[0]) )

																											SCREAMING_SNAKE_CASE           =    (
																											    self.thre_conva[k_conv]
																											    - np.sum(pd_conva_all[k_conv] ) * self.rate_thre
																											)
																						# all connected layer
																						SCREAMING_SNAKE_CASE           =    self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																						SCREAMING_SNAKE_CASE           =    self.vji + pd_j_all.T * bp_outa * self.rate_weight
																						SCREAMING_SNAKE_CASE           =    self.thre_bpa - pd_k_all * self.rate_thre
																						SCREAMING_SNAKE_CASE           =    self.thre_bpa - pd_j_all * self.rate_thre
																						# calculate the sum error of all single image
																						SCREAMING_SNAKE_CASE           =    np.sum(abs(data_teach - bp_outa ) )
																						error_count += errors
																						# print('   ----Teach      ',data_teach)
																						# print('   ----BP_output  ',bp_out3)
																	SCREAMING_SNAKE_CASE           =    rp + 1
																	SCREAMING_SNAKE_CASE           =    error_count / patterns
																	all_mse.append(_UpperCamelCase )

												def draw_error():
																	SCREAMING_SNAKE_CASE           =    [error_accuracy for i in range(int(n_repeat * 1.2 ) )]
																	plt.plot(_UpperCamelCase		,							"+-" )
																	plt.plot(_UpperCamelCase		,							"r--" )
																	plt.xlabel("Learning Times" )
																	plt.ylabel("All_mse" )
																	plt.grid(_UpperCamelCase		,							alpha=0.5 )
																	plt.show()

												print("------------------Training Complished---------------------" )
												print((" - - Training epoch: ", rp, F"     - - Mse: {mse:.6f}") )
												if draw_e:
																	draw_error()
												return mse




							def 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple )   ->			int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    []
												print("-------------------Start Testing-------------------------" )
												print((" - - Shape: Test_Data  ", np.shape(_UpperCamelCase )) )
												for p in range(len(_UpperCamelCase ) ):
																	SCREAMING_SNAKE_CASE           =    np.asmatrix(datas_test[p] )
																	SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.convolute(
																	    _UpperCamelCase		,							self.conva		,							self.w_conva		,							self.thre_conva		,							conv_step=self.step_conva		,							)
																	SCREAMING_SNAKE_CASE           =    self.pooling(_UpperCamelCase		,							self.size_poolinga )
																	SCREAMING_SNAKE_CASE           =    self._expand(_UpperCamelCase )

																	SCREAMING_SNAKE_CASE           =    data_bp_input
																	SCREAMING_SNAKE_CASE           =    bp_outa * self.vji.T - self.thre_bpa
																	SCREAMING_SNAKE_CASE           =    self.sig(_UpperCamelCase )
																	SCREAMING_SNAKE_CASE           =    bp_outa * self.wkj.T - self.thre_bpa
																	SCREAMING_SNAKE_CASE           =    self.sig(_UpperCamelCase )
																	produce_out.extend(bp_outa.getA().tolist() )
												SCREAMING_SNAKE_CASE           =    [list(map(self.do_round		,							_UpperCamelCase ) ) for each in produce_out]
												return np.asarray(_UpperCamelCase )





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Any )   ->			List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    np.asmatrix(_UpperCamelCase )
												SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    self.convolute(
												    _UpperCamelCase		,							self.conva		,							self.w_conva		,							self.thre_conva		,							conv_step=self.step_conva		,							)
												SCREAMING_SNAKE_CASE           =    self.pooling(_UpperCamelCase		,							self.size_poolinga )

												return data_conveda, data_pooleda


if __name__ == "__main__":
							pass





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				List[Any]     			=						[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

_lowerCamelCase   :				Optional[Any]     			=						{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
_lowerCamelCase   :				Any     			=						{f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase   :				Optional[Any]     			=						{f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							VOCAB_FILES_NAMES
							lowercase__  :    Optional[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Union[str, Any]									=							FunnelTokenizer
							lowercase__  :    List[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    int									=							2




							def __init__(  self				:   str		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   str="<unk>"		,							_UpperCamelCase				:   Optional[Any]="<sep>"		,							_UpperCamelCase				:   Optional[int]="<pad>"		,							_UpperCamelCase				:   int="<cls>"		,							_UpperCamelCase				:   Dict="<mask>"		,							_UpperCamelCase				:   Union[str, Any]="<s>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Dict="##"		,							**_UpperCamelCase				:   Dict		,							)   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							do_lower_case=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							clean_text=_UpperCamelCase		,							tokenize_chinese_chars=_UpperCamelCase		,							strip_accents=_UpperCamelCase		,							wordpieces_prefix=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
												if (
												    normalizer_state.get("lowercase"		,							_UpperCamelCase ) != do_lower_case
												    or normalizer_state.get("strip_accents"		,							_UpperCamelCase ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"		,							_UpperCamelCase ) != tokenize_chinese_chars
												):
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							normalizer_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    do_lower_case
																	SCREAMING_SNAKE_CASE           =    strip_accents
																	SCREAMING_SNAKE_CASE           =    tokenize_chinese_chars
																	SCREAMING_SNAKE_CASE           =    normalizer_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_lower_case




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Dict=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [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 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												if token_ids_a is None:
																	return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
												return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




