import pickle

import numpy as np
from matplotlib import pyplot as plt






class      __lowercase					:

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


								'''simple docstring'''

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

					def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		str:


								'''simple docstring'''

								lowercase							=		{
								    '''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(__lowerCamelCase							,							'''wb'''   ) as f:
											pickle.dump(__lowerCamelCase							,							__lowerCamelCase   )

								print(f'Model saved： {save_path}'   )

					@classmethod
					def        __a		(	cls				:      Optional[int]							,							__lowerCamelCase				:      Tuple   )							->		List[str]:


								'''simple docstring'''

								with open(__lowerCamelCase							,							'''rb'''   ) as f:
											lowercase							=		pickle.load(__lowerCamelCase   )  # noqa: S301

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

					def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]   )							->		Dict:


								'''simple docstring'''

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

					def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Optional[int]:


								'''simple docstring'''

								return round(__lowerCamelCase							,							3   )

					def        __a		(	self				:      Any							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Dict:


								'''simple docstring'''

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

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

					def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Union[str, Any]="average_pool"   )							->		str:


								'''simple docstring'''

								lowercase							=		len(featuremaps[0]   )
								lowercase							=		int(size_map / size_pooling   )
								lowercase							=		[]
								for i_map in range(len(__lowerCamelCase   )   ):
											lowercase							=		featuremaps[i_map]
											lowercase							=		[]
											for i_focus in range(0							,							__lowerCamelCase							,							__lowerCamelCase   ):
														for j_focus in range(0							,							__lowerCamelCase							,							__lowerCamelCase   ):
																	lowercase							=		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(__lowerCamelCase   )   )
																	elif pooling_type == "max_pooling":
																				# max pooling
																				map_pooled.append(np.max(__lowerCamelCase   )   )
											lowercase							=		np.asmatrix(__lowerCamelCase   ).reshape(__lowerCamelCase							,							__lowerCamelCase   )
											featuremap_pooled.append(__lowerCamelCase   )
								return featuremap_pooled

					def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Union[str, Any]:


								'''simple docstring'''

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

					def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


								'''simple docstring'''

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

					def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      List[Any]   )							->		Dict:


								'''simple docstring'''

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

					def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[Any]=bool   )							->		str:


								'''simple docstring'''

								print('''----------------------Start Training-------------------------'''   )
								print((''' - - Shape: Train_Data  ''', np.shape(__lowerCamelCase   ))   )
								print((''' - - Shape: Teach_Data  ''', np.shape(__lowerCamelCase   ))   )
								lowercase							=		0
								lowercase							=		[]
								lowercase							=		1_00_00
								while rp < n_repeat and mse >= error_accuracy:
											lowercase							=		0
											print(f'-------------Learning Time {rp}--------------'   )
											for p in range(len(__lowerCamelCase   )   ):
														# print('------------Learning Image: %d--------------'%p)
														lowercase							=		np.asmatrix(datas_train[p]   )
														lowercase							=		np.asarray(datas_teach[p]   )
														lowercase		,lowercase							=		self.convolute(
														    __lowerCamelCase							,							self.conva							,							self.w_conva							,							self.thre_conva							,							conv_step=self.step_conva							,							)
														lowercase							=		self.pooling(__lowerCamelCase							,							self.size_poolinga   )
														lowercase							=		np.shape(__lowerCamelCase   )
														lowercase							=		self._expand(__lowerCamelCase   )
														lowercase							=		data_bp_input

														lowercase							=		np.dot(__lowerCamelCase							,							self.vji.T   ) - self.thre_bpa
														lowercase							=		self.sig(__lowerCamelCase   )
														lowercase							=		np.dot(__lowerCamelCase							,							self.wkj.T   ) - self.thre_bpa
														lowercase							=		self.sig(__lowerCamelCase   )

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

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

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

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

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

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

					def        __a		(	self				:      str							,							__lowerCamelCase				:      Any   )							->		Any:


								'''simple docstring'''

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

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



					def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Union[str, Any]:


								'''simple docstring'''

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

								return data_conveda, data_pooleda


if __name__ == "__main__":
						pass






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_      =      logging.get_logger(__name__)


A_      =      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
A_      =      {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

A_      =      {
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}






@lru_cache()
def        __UpperCAmelCase    (       )->       Optional[int]:



			"""simple docstring"""







			lowercase							=		(
			    list(range(ord('''!'''       ),		ord('''~'''       ) + 1       )       ) + list(range(ord('''¡'''       ),		ord('''¬'''       ) + 1       )       ) + list(range(ord('''®'''       ),		ord('''ÿ'''       ) + 1       )       )
			)
			lowercase							=		bs[:]
			lowercase							=		0
			for b in range(2**8       ):
						if b not in bs:
									bs.append(UpperCAmelCase       )
									cs.append(2**8 + n       )
									n += 1
			lowercase							=		[chr(UpperCAmelCase       ) for n in cs]
			return dict(zip(UpperCAmelCase,		UpperCAmelCase       )       )






def        __UpperCAmelCase    (       UpperCAmelCase       )->       str:



			"""simple docstring"""







			lowercase							=		set()
			lowercase							=		word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)       )
						lowercase							=		char
			return pairs






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowercase					 =     ['input_ids', 'attention_mask']

						def __init__(	self				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]="replace"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      int="</s>"							,							__lowerCamelCase				:      Optional[Any]="<s>"							,							__lowerCamelCase				:      Optional[int]="<unk>"							,							__lowerCamelCase				:      List[str]="<pad>"							,							__lowerCamelCase				:      Union[str, Any]="<mask>"							,							__lowerCamelCase				:      str=False							,							**__lowerCamelCase				:      List[str]							,							)							->		Dict:


									'''simple docstring'''

									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else bos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else eos_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else sep_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else cls_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else unk_token
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else mask_token

									super().__init__(
									    errors=__lowerCamelCase							,							bos_token=__lowerCamelCase							,							eos_token=__lowerCamelCase							,							unk_token=__lowerCamelCase							,							sep_token=__lowerCamelCase							,							cls_token=__lowerCamelCase							,							pad_token=__lowerCamelCase							,							mask_token=__lowerCamelCase							,							add_prefix_space=__lowerCamelCase							,							**__lowerCamelCase							,							)

									with open(__lowerCamelCase							,							encoding='''utf-8'''   ) as vocab_handle:
												lowercase							=		json.load(__lowerCamelCase   )
									lowercase							=		{v: k for k, v in self.encoder.items()}
									lowercase							=		errors  # how to handle errors in decoding
									lowercase							=		bytes_to_unicode()
									lowercase							=		{v: k for k, v in self.byte_encoder.items()}
									with open(__lowerCamelCase							,							encoding='''utf-8'''   ) as merges_handle:
												lowercase							=		merges_handle.read().split('''\n'''   )[1:-1]
									lowercase							=		[tuple(merge.split()   ) for merge in bpe_merges]
									lowercase							=		dict(zip(__lowerCamelCase							,							range(len(__lowerCamelCase   )   )   )   )
									lowercase							=		{}
									lowercase							=		add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									lowercase							=		re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''   )

						@property
						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

									return len(self.encoder   )

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


									'''simple docstring'''

									return dict(self.encoder							,							**self.added_tokens_encoder   )

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


									'''simple docstring'''

									if token in self.cache:
												return self.cache[token]
									lowercase							=		tuple(__lowerCamelCase   )
									lowercase							=		get_pairs(__lowerCamelCase   )

									if not pairs:
												return token

									while True:
												lowercase							=		min(__lowerCamelCase							,							key=lambda __lowerCamelCase   : self.bpe_ranks.get(__lowerCamelCase							,							float('''inf'''   )   )   )
												if bigram not in self.bpe_ranks:
															break
												lowercase		,lowercase							=		bigram
												lowercase							=		[]
												lowercase							=		0
												while i < len(__lowerCamelCase   ):
															try:
																		lowercase							=		word.index(__lowerCamelCase							,							__lowerCamelCase   )
															except ValueError:
																		new_word.extend(word[i:]   )
																		break
															else:
																		new_word.extend(word[i:j]   )
																		lowercase							=		j

															if word[i] == first and i < len(__lowerCamelCase   ) - 1 and word[i + 1] == second:
																		new_word.append(first + second   )
																		i += 2
															else:
																		new_word.append(word[i]   )
																		i += 1
												lowercase							=		tuple(__lowerCamelCase   )
												lowercase							=		new_word
												if len(__lowerCamelCase   ) == 1:
															break
												else:
															lowercase							=		get_pairs(__lowerCamelCase   )
									lowercase							=		''' '''.join(__lowerCamelCase   )
									lowercase							=		word
									return word

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									for token in re.findall(self.pat							,							__lowerCamelCase   ):
												lowercase							=		''''''.join(
												    self.byte_encoder[b] for b in token.encode('''utf-8'''   )   )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCamelCase   ).split(''' '''   )   )
									return bpe_tokens

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

									return self.encoder.get(__lowerCamelCase							,							self.encoder.get(self.unk_token   )   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Union[str, Any]   )							->		Any:


									'''simple docstring'''

									return self.decoder.get(__lowerCamelCase   )

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


									'''simple docstring'''

									lowercase							=		''''''.join(__lowerCamelCase   )
									lowercase							=		bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8'''							,							errors=self.errors   )
									return text

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


									'''simple docstring'''

									if not os.path.isdir(__lowerCamelCase   ):
												logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
												return
									lowercase							=		os.path.join(
									    __lowerCamelCase							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
									lowercase							=		os.path.join(
									    __lowerCamelCase							,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

									with open(__lowerCamelCase							,							'''w'''							,							encoding='''utf-8'''   ) as f:
												f.write(json.dumps(self.encoder							,							indent=2							,							sort_keys=__lowerCamelCase							,							ensure_ascii=__lowerCamelCase   ) + '''\n'''   )

									lowercase							=		0
									with open(__lowerCamelCase							,							'''w'''							,							encoding='''utf-8'''   ) as writer:
												writer.write('''#version: 0.2\n'''   )
												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,							key=lambda __lowerCamelCase   : kv[1]   ):
															if index != token_index:
																		logger.warning(
																		    f'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																		    ''' Please check that the tokenizer is not corrupted!'''   )
																		lowercase							=		token_index
															writer.write(''' '''.join(__lowerCamelCase   ) + '''\n'''   )
															index += 1

									return vocab_file, merge_file

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase							=		[self.cls_token_id]
									lowercase							=		[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None							,							__lowerCamelCase				:      bool = False   )							->		List[int]:


									'''simple docstring'''

									if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=__lowerCamelCase							,							token_ids_a=__lowerCamelCase							,							already_has_special_tokens=__lowerCamelCase   )

									if token_ids_a is None:
												return [1] + ([0] * len(__lowerCamelCase   )) + [1]
									return [1] + ([0] * len(__lowerCamelCase   )) + [1, 1] + ([0] * len(__lowerCamelCase   )) + [1]

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									lowercase							=		[self.sep_token_id]
									lowercase							=		[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 + sep + token_ids_a + sep   ) * [0]



						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]=False							,							**__lowerCamelCase				:      List[str]   )							->		int:


									'''simple docstring'''

									lowercase							=		kwargs.pop('''add_prefix_space'''							,							self.add_prefix_space   )
									if (is_split_into_words or add_prefix_space) and (len(__lowerCamelCase   ) > 0 and not text[0].isspace()):
												lowercase							=		''' ''' + text
									return (text, kwargs)





