
import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


UpperCAmelCase__				:  List[Any]       					=				logging.getLogger(__name__)



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			random.seed(__SCREAMING_SNAKE_CASE )
			np.random.seed(__SCREAMING_SNAKE_CASE )
			torch.manual_seed(__SCREAMING_SNAKE_CASE )
			torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     str:
			model.eval()
			UpperCamelCase__    :		List[str]     =				next(model.parameters() ).device
			UpperCamelCase__    :		Union[str, Any]     =				1
			UpperCamelCase__    :		int     =				torch.zeros((eval_batch_size, context_len)				,    dtype=torch.long				,    device=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		int     =				DataLoader(__SCREAMING_SNAKE_CASE				,    shuffle=__SCREAMING_SNAKE_CASE				,    batch_size=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		List[str]     =				torch.zeros(1				,    device=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Any     =				0
			for batch in eval_dataloader:
						batch.to(__SCREAMING_SNAKE_CASE )
						# pad
						context.zero_()
						for i in range(__SCREAMING_SNAKE_CASE ):
									UpperCamelCase__    :		Optional[int]     =				batch[i]
						UpperCamelCase__    :		List[Any]     =				model(__SCREAMING_SNAKE_CASE				,    labels=__SCREAMING_SNAKE_CASE )
						eval_loss += outputs[0].sum().item()
						nb_eval_examples += batch.size(0 )
			UpperCamelCase__    :		int     =				eval_loss / nb_eval_examples
			UpperCamelCase__    :		Tuple     =				torch.exp(__SCREAMING_SNAKE_CASE )
			model.train()
			return perplexity



def       _lowercase					(      __SCREAMING_SNAKE_CASE="gpt2" )	->     Optional[int]:
			UpperCamelCase__    :		Optional[int]     =				GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE				,    output_hidden_states=__SCREAMING_SNAKE_CASE )
			torch.save(model.state_dict()				,    model_name + 'local.pt' )
			return model



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		List[str]     =				copy.deepcopy(__SCREAMING_SNAKE_CASE )
			model.to(__SCREAMING_SNAKE_CASE )

			UpperCamelCase__    :		Tuple     =				['bias', 'LayerNorm.weight']
			UpperCamelCase__    :		List[str]     =				[
			    {
			        'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay )],
			        'weight_decay': 0.0,
			    },
			    {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
			]
			UpperCamelCase__    :		Dict     =				AdamW(__SCREAMING_SNAKE_CASE				,    lr=5E-5				,    eps=1E-8 )
			UpperCamelCase__    :		List[str]     =				get_linear_schedule_with_warmup(__SCREAMING_SNAKE_CASE				,    0				,    __SCREAMING_SNAKE_CASE )
			torch.cuda.empty_cache()
			return model, lm_optimizer, lm_scheduler



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		str     =				np.array(__SCREAMING_SNAKE_CASE ).mean()
			UpperCamelCase__    :		int     =				np.array(__SCREAMING_SNAKE_CASE ).std()
			UpperCamelCase__    :		Any     =				(real_perps - avg) / std
			UpperCamelCase__    :		Optional[int]     =				list(zip(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ) )
			joblib.dump(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE="dev.jbl"				,    __SCREAMING_SNAKE_CASE=recopy_gpta				,    )	->     Union[str, Any]:
			UpperCamelCase__    :		Any     =				[]
			UpperCamelCase__    :		Optional[Any]     =				[]
			UpperCamelCase__    :		Optional[Any]     =				[]

			# Initialize the transformer model
			UpperCamelCase__    :		List[str]     =				copy.deepcopy(__SCREAMING_SNAKE_CASE )
			orig_model.to(device='cpu' )
			torch.cuda.empty_cache()

			# Compute perplexity of initial transformer model for comparison
			model.train()
			UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__    :		Dict     =				recopy_model(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

			for step in tqdm(range(__SCREAMING_SNAKE_CASE ) ):
						UpperCamelCase__    :		List[str]     =				torch.zeros((1, context_len)				,    dtype=torch.long				,    device=__SCREAMING_SNAKE_CASE )
						UpperCamelCase__    :		Dict     =				random.choice(__SCREAMING_SNAKE_CASE )
						UpperCamelCase__    :		Any     =				random.randint(0				,    len(story[0] ) - context_len - 1 )
						UpperCamelCase__    :		Union[str, Any]     =				story[0][start : start + context_len]
						lm_optimizer.zero_grad()
						UpperCamelCase__    :		int     =				model(__SCREAMING_SNAKE_CASE				,    labels=__SCREAMING_SNAKE_CASE )
						UpperCamelCase__    :		Optional[Any]     =				outputs[0]
						UpperCamelCase__    :		Optional[Any]     =				compute_perplexity(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )
						model.train()
						lm_loss.backward()
						# Do LM backprop
						torch.nn.utils.clip_grad_norm_(model.parameters()				,    3.0 )
						lm_optimizer.step()
						lm_scheduler.step()  # Update learning rate schedule

						# Compute perplexity after back-propagating on the selected context
						UpperCamelCase__    :		str     =				compute_perplexity(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

						# Periodically save the stored (X, IG(X)) pairs
						if step % 1000 == 0 and step > 1:
									intermittent_save(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

						# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
						UpperCamelCase__   ,			UpperCamelCase__   ,			UpperCamelCase__    :		List[Any]     =				recopy_model(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )

						past_perps.append(past_perp.item() )
						real_perps.append(orig_perp - real_perp.item() )
						contexts.append(np.array(context.cpu() ) )

			intermittent_save(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE )



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"				,    __SCREAMING_SNAKE_CASE=100				,    __SCREAMING_SNAKE_CASE=1026				,    __SCREAMING_SNAKE_CASE=True )	->     Union[str, Any]:
			UpperCamelCase__    :		Union[str, Any]     =				joblib.load(__SCREAMING_SNAKE_CASE )
			print('data loaded' )
			UpperCamelCase__    :		Dict     =				[]
			if trim:
						for i, example in enumerate(__SCREAMING_SNAKE_CASE ):
									if len(example[0] ) > min_len:
												UpperCamelCase__    :		Any     =				random.randint(0				,    len(example[0] ) - context_len - 1 )
												objective_set.append(example[0, start : start + context_len] )
									if len(__SCREAMING_SNAKE_CASE ) >= number:
												break
						UpperCamelCase__    :		List[Any]     =				[]
						for j in range(i + 1				,    len(__SCREAMING_SNAKE_CASE ) ):
									if len(data[j][0] ) > min_len:
												train_data.append(data[j] )
			else:
						UpperCamelCase__    :		Optional[Any]     =				data[0:number]
						UpperCamelCase__    :		Any     =				data[number:]

			joblib.dump(__SCREAMING_SNAKE_CASE				,    'objective_set.jbl' )
			print('objective set saved' )
			return train_data, objective_set



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=50				,    __SCREAMING_SNAKE_CASE="secondary_learner.pt" )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu' )
			# We will use the first 512 pairs from our dataset as a test set for
			# our secondary learner and the rest to train
			UpperCamelCase__    :		int     =				train_dataset[:512]
			UpperCamelCase__    :		Optional[Any]     =				train_dataset[512:]
			UpperCamelCase__    :		Dict     =				DataLoader(__SCREAMING_SNAKE_CASE				,    shuffle=__SCREAMING_SNAKE_CASE				,    batch_size=__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[Any]     =				DataLoader(__SCREAMING_SNAKE_CASE				,    shuffle=__SCREAMING_SNAKE_CASE				,    batch_size=__SCREAMING_SNAKE_CASE )

			# secondary learner model set up
			UpperCamelCase__    :		Tuple     =				nn.MSELoss()
			UpperCamelCase__    :		str     =				nn.MSELoss(reduction='sum' )
			secondary_learner.to(__SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		Optional[Any]     =				torch.optim.Adam(secondary_learner.parameters()				,    lr=0.00001 )
			secondary_learner.train()

			# TODO in original code this is written as number of actual batches seen
			# not number of items seen but other places it is number of items instead.
			# improve consistency! changed this to epochs for clarity
			UpperCamelCase__    :		List[str]     =				float('inf' )
			# Iterate through batches until we've used max_steps batches
			for epoch in range(int(__SCREAMING_SNAKE_CASE ) ):
						UpperCamelCase__    :		List[Any]     =				0.0
						secondary_learner.train()
						for step, batch in enumerate(__SCREAMING_SNAKE_CASE ):
									UpperCamelCase__    :		Optional[Any]     =				batch[0].to(__SCREAMING_SNAKE_CASE )
									UpperCamelCase__    :		Union[str, Any]     =				batch[1].to(__SCREAMING_SNAKE_CASE )
									UpperCamelCase__    :		List[Any]     =				secondary_learner(__SCREAMING_SNAKE_CASE )
									q_optimizer.zero_grad()
									UpperCamelCase__    :		List[Any]     =				loss(__SCREAMING_SNAKE_CASE				,    real_q.float() )
									q_loss.backward()
									q_optimizer.step()
									tr_q_loss += q_loss.item()

									# model trains fairly quickly so we won't wait for a full epoch
									# eval is triggered at eval_freq and end of epochs
									if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__SCREAMING_SNAKE_CASE )):
												UpperCamelCase__    :		List[Any]     =				tr_q_loss / (step + 1)

												secondary_learner.eval()
												UpperCamelCase__    :		Any     =				0.0
												UpperCamelCase__    :		int     =				0.0
												UpperCamelCase__    :		Union[str, Any]     =				[]
												UpperCamelCase__    :		Optional[int]     =				[]
												# Compute performance of the secondary learner after this batch
												for stepa, batcha in enumerate(__SCREAMING_SNAKE_CASE ):
															UpperCamelCase__    :		Optional[Any]     =				batcha[0].to(__SCREAMING_SNAKE_CASE )
															UpperCamelCase__    :		List[Any]     =				batcha[1].to(__SCREAMING_SNAKE_CASE )
															UpperCamelCase__    :		str     =				secondary_learner(__SCREAMING_SNAKE_CASE )
															q_lossa += test_loss(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE ).item()
															sum_qa += torch.sum(__SCREAMING_SNAKE_CASE ).item()
															for ei, i in enumerate(predicted_qa.cpu().detach().numpy() ):
																		predicted.append(i.item() )
															for ei, i in enumerate(real_qa.cpu().detach().numpy() ):
																		actual.append(i.item() )

												q_lossa /= len(__SCREAMING_SNAKE_CASE )
												print(
												    'Epoch: '				,    __SCREAMING_SNAKE_CASE				,    'step: '				,    __SCREAMING_SNAKE_CASE				,    'Avg. q:'				,    sum_qa / len(__SCREAMING_SNAKE_CASE )				,    'Train Loss: '				,    __SCREAMING_SNAKE_CASE				,    'Test Loss: '				,    __SCREAMING_SNAKE_CASE				,    )
												if q_lossa < best_test_loss:
															joblib.dump((predicted, actual)				,    'pred_vs_actual.jbl' )
															torch.save(secondary_learner.state_dict()				,    __SCREAMING_SNAKE_CASE )
															UpperCamelCase__    :		List[Any]     =				q_lossa

									secondary_learner.train()
			return secondary_learner



class 			UpperCamelCase_			(						nn.Module   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase) ->	Optional[int]:
										super(UpperCamelCase     ,						self).__init__()
										UpperCamelCase__    :		Optional[int]     =				model.transformer.wte
										UpperCamelCase__    :		Dict     =				copy.deepcopy(model.transformer.wte.weight)
										UpperCamelCase__    :		Optional[Any]     =				nn.Convad(self.embeddings.weight.size(1)     ,						2_56     ,						3     ,						padding=1)
										UpperCamelCase__    :		Tuple     =				nn.Sequential(nn.Linear(2_56     ,						32)     ,						nn.Dropout(p=0.1)     ,						nn.Linear(32     ,						32)     ,						nn.Linear(32     ,						1))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				torch.max(self.conv(self.embeddings(UpperCamelCase).squeeze(1).transpose(1     ,						2))     ,						2)[0]
										UpperCamelCase__    :		Union[str, Any]     =				self.fc(UpperCamelCase)
										return qs.squeeze(1)






							@classmethod
							def        lowerCAmelCase__ (				cls     ,						UpperCamelCase     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		int     =				cls(UpperCamelCase)  # this calls __init__
										UpperCamelCase__    :		Optional[int]     =				torch.load(UpperCamelCase)
										secondary_learner.load_state_dict(UpperCamelCase)
										UpperCamelCase__    :		List[Any]     =				model.transformer.wte
										UpperCamelCase__    :		int     =				copy.deepcopy(model.transformer.wte.weight)
										return secondary_learner





import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


UpperCAmelCase__				:  Optional[Any]       					=				logging.get_logger(__name__)

UpperCAmelCase__				:  List[str]       					=				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

UpperCAmelCase__				:  Optional[int]       					=				{
    '''vocab_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/vocab.json'''},
    '''merges_file''': {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/merges.txt'''},
}

UpperCAmelCase__				:  Any       					=				{
    '''openai-gpt''': 512,
}



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[int]:
			UpperCamelCase__    :		List[Any]     =				text.strip()
			if not text:
						return []
			UpperCamelCase__    :		List[Any]     =				text.split()
			return tokens



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase=True     ,						UpperCamelCase=None     ,						UpperCamelCase=True     ,						UpperCamelCase=None     ,						UpperCamelCase=True     ,						) ->	Tuple:
										if never_split is None:
													UpperCamelCase__    :		str     =				[]
										UpperCamelCase__    :		Optional[Any]     =				do_lower_case
										UpperCamelCase__    :		Optional[int]     =				set(UpperCamelCase)
										UpperCamelCase__    :		List[Any]     =				tokenize_chinese_chars
										UpperCamelCase__    :		Optional[int]     =				strip_accents
										UpperCamelCase__    :		Any     =				do_split_on_punc
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	List[str]:
										UpperCamelCase__    :		Tuple     =				self.never_split.union(set(UpperCamelCase)) if never_split else self.never_split
										UpperCamelCase__    :		Optional[int]     =				self._clean_text(UpperCamelCase)

										# This was added on November 1st, 2018 for the multilingual and Chinese
										# models. This is also applied to the English models now, but it doesn't
										# matter since the English models were not trained on any Chinese data
										# and generally don't have any Chinese data in them (there are Chinese
										# characters in the vocabulary because Wikipedia does have some Chinese
										# words in the English Wikipedia.).
										if self.tokenize_chinese_chars:
													UpperCamelCase__    :		List[str]     =				self._tokenize_chinese_chars(UpperCamelCase)
										# prevents treating the same character with different unicode codepoints as different characters
										UpperCamelCase__    :		Dict     =				unicodedata.normalize('NFC'     ,						UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				whitespace_tokenize(UpperCamelCase)
										UpperCamelCase__    :		List[Any]     =				[]
										for token in orig_tokens:
													if token not in never_split:
																if self.do_lower_case:
																			UpperCamelCase__    :		str     =				token.lower()
																			if self.strip_accents is not False:
																						UpperCamelCase__    :		str     =				self._run_strip_accents(UpperCamelCase)
																elif self.strip_accents:
																			UpperCamelCase__    :		Union[str, Any]     =				self._run_strip_accents(UpperCamelCase)
													split_tokens.extend(self._run_split_on_punc(UpperCamelCase     ,						UpperCamelCase))

										UpperCamelCase__    :		Dict     =				whitespace_tokenize(' '.join(UpperCamelCase))
										return output_tokens
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		Union[str, Any]     =				unicodedata.normalize('NFD'     ,						UpperCamelCase)
										UpperCamelCase__    :		Dict     =				[]
										for char in text:
													UpperCamelCase__    :		Union[str, Any]     =				unicodedata.category(UpperCamelCase)
													if cat == "Mn":
																continue
													output.append(UpperCamelCase)
										return "".join(UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	Dict:
										if not self.do_split_on_punc or (never_split is not None and text in never_split):
													return [text]
										UpperCamelCase__    :		Dict     =				list(UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				0
										UpperCamelCase__    :		Dict     =				True
										UpperCamelCase__    :		Optional[int]     =				[]
										while i < len(UpperCamelCase):
													UpperCamelCase__    :		List[str]     =				chars[i]
													if _is_punctuation(UpperCamelCase):
																output.append([char])
																UpperCamelCase__    :		Tuple     =				True
													else:
																if start_new_word:
																			output.append([])
																UpperCamelCase__    :		Union[str, Any]     =				False
																output[-1].append(UpperCamelCase)
													i += 1

										return ["".join(UpperCamelCase) for x in output]
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Tuple:
										UpperCamelCase__    :		List[str]     =				[]
										for char in text:
													UpperCamelCase__    :		List[str]     =				ord(UpperCamelCase)
													if self._is_chinese_char(UpperCamelCase):
																output.append(' ')
																output.append(UpperCamelCase)
																output.append(' ')
													else:
																output.append(UpperCamelCase)
										return "".join(UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Union[str, Any]:
										# This defines a "chinese character" as anything in the CJK Unicode block:
										#   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
										#
										# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
										# despite its name. The modern Korean Hangul alphabet is a different block,
										# as is Japanese Hiragana and Katakana. Those alphabets are used to write
										# space-separated words, so they are not treated specially and handled
										# like the all of the other languages.
										if (
										    (cp >= 0X4e00 and cp <= 0X9fff)
										    or (cp >= 0X3400 and cp <= 0X4dbf)  #
										    or (cp >= 0X2_0000 and cp <= 0X2_a6df)  #
										    or (cp >= 0X2_a700 and cp <= 0X2_b73f)  #
										    or (cp >= 0X2_b740 and cp <= 0X2_b81f)  #
										    or (cp >= 0X2_b820 and cp <= 0X2_ceaf)  #
										    or (cp >= 0Xf900 and cp <= 0Xfaff)
										    or (cp >= 0X2_f800 and cp <= 0X2_fa1f)  #
										):  #
													return True

										return False






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		List[Any]     =				[]
										for char in text:
													UpperCamelCase__    :		Dict     =				ord(UpperCamelCase)
													if cp == 0 or cp == 0Xfffd or _is_control(UpperCamelCase):
																continue
													if _is_whitespace(UpperCamelCase):
																output.append(' ')
													else:
																output.append(UpperCamelCase)
										return "".join(UpperCamelCase)



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Union[str, Any]:
			UpperCamelCase__    :		Optional[int]     =				set()
			UpperCamelCase__    :		str     =				word[0]
			for char in word[1:]:
						pairs.add((prev_char, char) )
						UpperCamelCase__    :		Any     =				char
			return pairs



def       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Optional[Any]:
			UpperCamelCase__    :		Tuple     =				text.replace('—'				,    '-' )
			UpperCamelCase__    :		Any     =				text.replace('–'				,    '-' )
			UpperCamelCase__    :		Any     =				text.replace('―'				,    '-' )
			UpperCamelCase__    :		Tuple     =				text.replace('…'				,    '...' )
			UpperCamelCase__    :		Dict     =				text.replace('´'				,    '\'' )
			UpperCamelCase__    :		int     =				re.sub(R'(-+|~+|!+|"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)'				,    R' \1 '				,    __SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		int     =				re.sub(R'\s*\n\s*'				,    ' \n '				,    __SCREAMING_SNAKE_CASE )
			UpperCamelCase__    :		str     =				re.sub(R'[^\S\n]+'				,    ' '				,    __SCREAMING_SNAKE_CASE )
			return text.strip()



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							VOCAB_FILES_NAMES
							UpperCamelCase_					   =							PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_					   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_					   =							["""input_ids""", """attention_mask"""]
							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase="<unk>"     ,						**UpperCamelCase) ->	Union[str, Any]:
										super().__init__(unk_token=UpperCamelCase     ,						**UpperCamelCase)

										try:
													import ftfy
													from spacy.lang.en import English

													UpperCamelCase__    :		Optional[int]     =				English()
													UpperCamelCase__    :		Dict     =				_nlp.tokenizer
													UpperCamelCase__    :		List[Any]     =				ftfy.fix_text
										except ImportError:
													logger.warning('ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy.')
													UpperCamelCase__    :		Dict     =				BasicTokenizer(do_lower_case=UpperCamelCase)
													UpperCamelCase__    :		List[Any]     =				None

										with open(UpperCamelCase     ,						encoding='utf-8') as vocab_handle:
													UpperCamelCase__    :		int     =				json.load(UpperCamelCase)
										UpperCamelCase__    :		Dict     =				{v: k for k, v in self.encoder.items()}
										with open(UpperCamelCase     ,						encoding='utf-8') as merges_handle:
													UpperCamelCase__    :		Optional[int]     =				merges_handle.read().split('\n')[1:-1]
										UpperCamelCase__    :		int     =				[tuple(merge.split()) for merge in merges]
										UpperCamelCase__    :		int     =				dict(zip(UpperCamelCase     ,						range(len(UpperCamelCase))))
										UpperCamelCase__    :		Optional[Any]     =				{}
							@property
							def        lowerCAmelCase__ (				self) ->	Dict:
										return True
							@property
							def        lowerCAmelCase__ (				self) ->	Union[str, Any]:
										return len(self.encoder)
							def        lowerCAmelCase__ (				self) ->	Tuple:
										return dict(self.encoder     ,						**self.added_tokens_encoder)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		Optional[Any]     =				tuple(token[:-1]) + (token[-1] + '</w>',)
										if token in self.cache:
													return self.cache[token]
										UpperCamelCase__    :		int     =				get_pairs(UpperCamelCase)

										if not pairs:
													return token + "</w>"

										while True:
													UpperCamelCase__    :		str     =				min(UpperCamelCase     ,						key=lambda UpperCamelCase: self.bpe_ranks.get(UpperCamelCase     ,						float('inf')))
													if bigram not in self.bpe_ranks:
																break
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				bigram
													UpperCamelCase__    :		str     =				[]
													UpperCamelCase__    :		List[Any]     =				0
													while i < len(UpperCamelCase):
																try:
																			UpperCamelCase__    :		Union[str, Any]     =				word.index(UpperCamelCase     ,						UpperCamelCase)
																except ValueError:
																			new_word.extend(word[i:])
																			break
																else:
																			new_word.extend(word[i:j])
																			UpperCamelCase__    :		Tuple     =				j

																if word[i] == first and i < len(UpperCamelCase) - 1 and word[i + 1] == second:
																			new_word.append(first + second)
																			i += 2
																else:
																			new_word.append(word[i])
																			i += 1
													UpperCamelCase__    :		int     =				tuple(UpperCamelCase)
													UpperCamelCase__    :		List[Any]     =				new_word
													if len(UpperCamelCase) == 1:
																break
													else:
																UpperCamelCase__    :		List[Any]     =				get_pairs(UpperCamelCase)
										UpperCamelCase__    :		Optional[int]     =				' '.join(UpperCamelCase)
										if word == "\n  </w>":
													UpperCamelCase__    :		Optional[int]     =				'\n</w>'
										UpperCamelCase__    :		Optional[Any]     =				word
										return word
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		int     =				[]
										if self.fix_text is None:
													# Using BERT's BasicTokenizer
													UpperCamelCase__    :		List[str]     =				self.nlp.tokenize(UpperCamelCase)
													for token in text:
																split_tokens.extend(list(self.bpe(UpperCamelCase).split(' ')))
										else:
													# Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
													UpperCamelCase__    :		Dict     =				self.nlp(text_standardize(self.fix_text(UpperCamelCase)))
													for token in text:
																split_tokens.extend(list(self.bpe(token.text.lower()).split(' ')))
										return split_tokens
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Tuple:
										return self.encoder.get(UpperCamelCase     ,						self.encoder.get(self.unk_token))
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[Any]:
										return self.decoder.get(UpperCamelCase     ,						self.unk_token)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	List[str]:
										UpperCamelCase__    :		Optional[Any]     =				''.join(UpperCamelCase).replace('</w>'     ,						' ').strip()
										return out_string






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase = None) ->	Tuple[str]:
										if not os.path.isdir(UpperCamelCase):
													logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
													return
										UpperCamelCase__    :		int     =				os.path.join(
										    UpperCamelCase     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
										UpperCamelCase__    :		List[str]     =				os.path.join(
										    UpperCamelCase     ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

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

										UpperCamelCase__    :		Any     =				0
										with open(UpperCamelCase     ,						'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 UpperCamelCase: 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!')
																			UpperCamelCase__    :		Dict     =				token_index
																writer.write(' '.join(UpperCamelCase) + '\n')
																index += 1

										return vocab_file, merge_file



