



"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__=0.9_99				,      lowerCamelCase__="cosine"				,      ) ->							int:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2			) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__			):
																					return math.exp(t * -12.0			)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""			)

							A         =					[]
							for i in range(lowerCamelCase__			):
														A         =					i / num_diffusion_timesteps
														A         =					(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase__			) / alpha_bar_fn(lowerCamelCase__			)				,      lowerCamelCase__			)			)
							return torch.tensor(lowerCamelCase__				,      dtype=torch.floataa			)





class      UpperCAmelCase__		(      UpperCamelCase   ,UpperCamelCase     ):
		lowerCAmelCase_       :	str 							=     [e.name for e in KarrasDiffusionSchedulers]
		lowerCAmelCase_       :	Optional[int] 							=     2



		@register_to_config
		def __init__( self  :    Any      ,							snake_case  :    int = 1_000      ,							snake_case  :    float = 0.00085      ,							snake_case  :    float = 0.012      ,							snake_case  :    str = "linear"      ,							snake_case  :    Optional[Union[np.ndarray, List[float]]] = None      ,							snake_case  :    str = "epsilon"      ,							snake_case  :    str = "linspace"      ,							snake_case  :    int = 0      ,							)			->						List[Any]:






									'''simple docstring'''

									if trained_betas is not None:
																A         =					torch.tensor(snake_case      ,							dtype=torch.floataa				)
									elif beta_schedule == "linear":
																A         =					torch.linspace(snake_case      ,							snake_case      ,							snake_case      ,							dtype=torch.floataa				)
									elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																A         =					(
																    torch.linspace(beta_start**0.5      ,							beta_end**0.5      ,							snake_case      ,							dtype=torch.floataa				) ** 2
																)
									elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																A         =					betas_for_alpha_bar(snake_case				)
									else:
																raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""				)

									A         =					1.0 - self.betas
									A         =					torch.cumprod(self.alphas      ,							dim=0				)

									#  set all values
									self.set_timesteps(snake_case      ,							snake_case      ,							snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Union[str, Any]=None				)			->						Union[str, Any]:






									'''simple docstring'''

									if schedule_timesteps is None:
																A         =					self.timesteps

									A         =					(schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter				) == 0:
																A         =					1 if len(snake_case				) > 1 else 0
									else:
																A         =					timestep.cpu().item() if torch.is_tensor(snake_case				) else timestep
																A         =					self._index_counter[timestep_int]

									return indices[pos].item()



		@property
		def 					A_		( self  :    int				)			->						str:






									'''simple docstring'''

									if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5



		def 					A_		( self  :    Any      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

									if self.state_in_first_order:
																A         =					self.sigmas[step_index]
									else:
																A         =					self.sigmas_interpol[step_index]

									A         =					sample / ((sigma**2 + 1) ** 0.5)
									return sample



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    Union[str, torch.device] = None      ,							snake_case  :    Optional[int] = None      ,							)			->						List[str]:






									'''simple docstring'''

									A         =					num_inference_steps

									A         =					num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
																A         =					np.linspace(0      ,							num_train_timesteps - 1      ,							snake_case      ,							dtype=snake_case				)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
																A         =					num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A         =					(np.arange(0      ,							snake_case				) * step_ratio).round()[::-1].copy().astype(snake_case				)
																timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
																A         =					num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																A         =					(np.arange(snake_case      ,							0      ,							-step_ratio				)).round().copy().astype(snake_case				)
																timesteps -= 1
									else:
																raise ValueError(
																    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

									A         =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
									A         =					torch.from_numpy(np.log(snake_case				)				).to(snake_case				)

									A         =					np.interp(snake_case      ,							np.arange(0      ,							len(snake_case				)				)      ,							snake_case				)
									A         =					np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
									A         =					torch.from_numpy(snake_case				).to(device=snake_case				)

									# interpolate sigmas
									A         =					sigmas.log().lerp(sigmas.roll(1				).log()      ,							0.5				).exp()

									A         =					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2				), sigmas[-1:]]				)
									A         =					torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2				), sigmas_interpol[-1:]]				)

									if str(snake_case				).startswith('mps'				):
																# mps does not support float64
																A         =					torch.from_numpy(snake_case				).to(snake_case      ,							dtype=torch.floataa				)
									else:
																A         =					torch.from_numpy(snake_case				).to(snake_case				)

									# interpolate timesteps
									A         =					self.sigma_to_t(snake_case				).to(snake_case      ,							dtype=timesteps.dtype				)
									A         =					torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])      ,							dim=-1				).flatten()

									A         =					torch.cat([timesteps[:1], interleaved_timesteps]				)

									A         =					None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									A         =					defaultdict(snake_case				)



		def 					A_		( self  :    Union[str, Any]      ,							snake_case  :    Optional[Any]				)			->						Dict:






									'''simple docstring'''

									A         =					sigma.log()

									# get distribution
									A         =					log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									A         =					dists.ge(0				).cumsum(dim=0				).argmax(dim=0				).clamp(max=self.log_sigmas.shape[0] - 2				)
									A         =					low_idx + 1

									A         =					self.log_sigmas[low_idx]
									A         =					self.log_sigmas[high_idx]

									# interpolate sigmas
									A         =					(low - log_sigma) / (low - high)
									A         =					w.clamp(0      ,							1				)

									# transform interpolation to time range
									A         =					(1 - w) * low_idx + w * high_idx
									A         =					t.view(sigma.shape				)
									return t



		@property
		def 					A_		( self  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.sample is None



		def 					A_		( self  :    Any      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    Union[float, torch.FloatTensor]      ,							snake_case  :    Union[torch.FloatTensor, np.ndarray]      ,							snake_case  :    bool = True      ,							)			->						Union[SchedulerOutput, Tuple]:






									'''simple docstring'''

									A         =					self.index_for_timestep(snake_case				)

									# advance index counter by 1
									A         =					timestep.cpu().item() if torch.is_tensor(snake_case				) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
																A         =					self.sigmas[step_index]
																A         =					self.sigmas_interpol[step_index + 1]
																A         =					self.sigmas[step_index + 1]
									else:
																# 2nd order / KDPM2's method
																A         =					self.sigmas[step_index - 1]
																A         =					self.sigmas_interpol[step_index]
																A         =					self.sigmas[step_index]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									A         =					0
									A         =					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
																A         =					sigma_hat if self.state_in_first_order else sigma_interpol
																A         =					sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
																A         =					sigma_hat if self.state_in_first_order else sigma_interpol
																A         =					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																    sample / (sigma_input**2 + 1)
																)
									elif self.config.prediction_type == "sample":
																raise NotImplementedError('prediction_type not implemented yet: sample'				)
									else:
																raise ValueError(
																    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

									if self.state_in_first_order:
																# 2. Convert to an ODE derivative for 1st order
																A         =					(sample - pred_original_sample) / sigma_hat
																# 3. delta timestep
																A         =					sigma_interpol - sigma_hat

																# store for 2nd order step
																A         =					sample
									else:
																# DPM-Solver-2
																# 2. Convert to an ODE derivative for 2nd order
																A         =					(sample - pred_original_sample) / sigma_interpol

																# 3. delta timestep
																A         =					sigma_next - sigma_hat

																A         =					self.sample
																A         =					None

									A         =					sample + derivative * dt

									if not return_dict:
																return (prev_sample,)

									return SchedulerOutput(prev_sample=snake_case				)



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							snake_case  :    torch.FloatTensor      ,							)			->						torch.FloatTensor:






									'''simple docstring'''

									A         =					self.sigmas.to(device=original_samples.device      ,							dtype=original_samples.dtype				)
									if original_samples.device.type == "mps" and torch.is_floating_point(snake_case				):
																# mps does not support float64
																A         =					self.timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
																A         =					timesteps.to(original_samples.device      ,							dtype=torch.floataa				)
									else:
																A         =					self.timesteps.to(original_samples.device				)
																A         =					timesteps.to(original_samples.device				)

									A         =					[self.index_for_timestep(snake_case      ,							snake_case				) for t in timesteps]

									A         =					sigmas[step_indices].flatten()
									while len(sigma.shape				) < len(original_samples.shape				):
																A         =					sigma.unsqueeze(-1				)

									A         =					original_samples + noise * sigma
									return noisy_samples



		def __len__( self  :    Tuple				)			->						Optional[int]:






									'''simple docstring'''

									return self.config.num_train_timesteps








"""simple docstring"""







import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


A    =			logging.get_logger(__name__)

A    =			{'vocab_file': 'vocab.txt', 'emoji_file': 'emoji.json'}

A    =			{
    'vocab_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt',
    },
    'emoji_file': {
        'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json',
    },
}

A    =			{
    'abeja/gpt-neox-japanese-2.7b': 2_0_4_8,
}

def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__			) ->							str:
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					json.loads(f.read()			)

							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							A         =					collections.OrderedDict()
							with open(lowerCamelCase__				,      'r'				,      encoding='utf-8'			) as f:
														A         =					f.readlines()
							A         =					[[t.rstrip('\n'			)] if (t == ',' or ',' not in t) else t.rstrip('\n'			).split(','			) for t in token]
							for idx, b in enumerate(lowerCamelCase__			):
														A         =					b
														A         =					idx
														for wd in b:
																					A         =					idx

							return vocab, raw_vocab, ids_to_tokens, emoji






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Optional[int] 							=     VOCAB_FILES_NAMES
		lowerCAmelCase_       :	Any 							=     PRETRAINED_VOCAB_FILES_MAP
		lowerCAmelCase_       :	Optional[Any] 							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCAmelCase_       :	str 							=     ["""input_ids""", """attention_mask"""]



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Tuple      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="<|endoftext|>"      ,							snake_case  :    List[str]="<|endoftext|>"      ,							snake_case  :    Any="<|startoftext|>"      ,							snake_case  :    Any="<|endoftext|>"      ,							snake_case  :    Tuple=False      ,							**snake_case  :    List[str]      ,							)			->						Optional[Any]:






									'''simple docstring'''

									super().__init__(
									    unk_token=snake_case      ,							pad_token=snake_case      ,							bos_token=snake_case      ,							eos_token=snake_case      ,							do_clean_text=snake_case      ,							**snake_case      ,							)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
																    ' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									if not os.path.isfile(snake_case				):
																raise ValueError(
																    f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
																    ' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`'				)
									A         =					do_clean_text
									A      ,  A      ,  A      ,  A         =					load_vocab_and_emoji(snake_case      ,							snake_case				)
									A         =					SubWordJapaneseTokenizer(
									    vocab=self.vocab      ,							ids_to_tokens=self.ids_to_tokens      ,							emoji=self.emoji				)



		@property
		def 					A_		( self  :    Any				)			->						List[str]:






									'''simple docstring'''

									return len(self.raw_vocab				)



		def 					A_		( self  :    str				)			->						str:






									'''simple docstring'''

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



		def 					A_		( self  :    List[str]      ,							snake_case  :    Tuple				)			->						Optional[Any]:






									'''simple docstring'''

									return self.subword_tokenizer.tokenize(snake_case      ,							clean=self.do_clean_text				)



		def 					A_		( self  :    int      ,							snake_case  :    Optional[int]				)			->						Dict:






									'''simple docstring'''

									return self.vocab.get(snake_case      ,							self.vocab.get(self.unk_token				)				)



		def 					A_		( self  :    int      ,							snake_case  :    List[Any]				)			->						Tuple:






									'''simple docstring'''

									return self.subword_tokenizer.convert_id_to_token(snake_case				)



		def 					A_		( self  :    str      ,							snake_case  :    int				)			->						Optional[Any]:






									'''simple docstring'''

									A         =					''.join(snake_case				).strip()
									return out_string



		def 					A_		( self  :    Optional[int]      ,							snake_case  :    "Conversation"				)			->						List[int]:






									'''simple docstring'''

									A         =					[]
									for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(snake_case      ,							add_special_tokens=snake_case				) + [self.eos_token_id]				)

									if len(snake_case				) > self.model_max_length:
																A         =					input_ids[-self.model_max_length :]
									return input_ids



		def 					A_		( self  :    int      ,							snake_case  :    str      ,							snake_case  :    Optional[str] = None				)			->						Tuple[str]:






									'''simple docstring'''

									A         =					0
									if os.path.isdir(snake_case				):
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']				)
																A         =					os.path.join(
																    snake_case      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file']				)
									else:
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
																)
																A         =					(
																    (filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
																)
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																for token_index, token in self.ids_to_tokens.items():
																							if index != token_index:
																														logger.warning(
																														    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
																														    ' Please check that the vocabulary is not corrupted!'				)
																														A         =					token_index
																							writer.write(','.join(snake_case				) + '\n'				)
																							index += 1
									with open(snake_case      ,							'w'      ,							encoding='utf-8'				) as writer:
																json.dump(self.emoji      ,							snake_case				)
									return vocab_file, emoji_file






class      UpperCAmelCase__		(      UpperCamelCase     ):



		def __init__( self  :    str      ,							snake_case  :    Dict      ,							snake_case  :    Optional[Any]      ,							snake_case  :    List[Any]				)			->						int:






									'''simple docstring'''

									A         =					vocab  # same as swe
									A         =					ids_to_tokens  # same as bpe
									A         =					emoji
									A         =					np.max([len(snake_case				) for w in self.vocab.keys()]				)
									A         =					re.compile(r'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)'				)
									A         =					re.compile(r'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*'				)
									A         =					re.compile(r'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}'				)
									A         =					re.compile(
									    r'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*'				)
									A         =					re.compile(
									    r'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*'				)
									A         =					'─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
									A         =					'▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
									A         =					str.maketrans({k: '<BLOCK>' for k in keisen + blocks}				)



		def __len__( self  :    List[str]				)			->						List[str]:






									'''simple docstring'''

									return len(self.ids_to_tokens				)



		def 					A_		( self  :    Tuple      ,							snake_case  :    Any				)			->						Optional[int]:






									'''simple docstring'''

									A         =					self.content_repattera.sub('<URL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<EMAIL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<TEL>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<DATE>'      ,							snake_case				)
									A         =					self.content_repattera.sub('<PRICE>'      ,							snake_case				)
									A         =					content.translate(self.content_transa				)
									while "<BLOCK><BLOCK>" in content:
																A         =					content.replace('<BLOCK><BLOCK>'      ,							'<BLOCK>'				)
									return content



		def 					A_		( self  :    Any      ,							snake_case  :    int      ,							snake_case  :    Any=False				)			->						Any:






									'''simple docstring'''

									A         =					text.replace(' '      ,							'<SP>'				)
									A         =					text.replace('　'      ,							'<SP>'				)
									A         =					text.replace('\r\n'      ,							'<BR>'				)
									A         =					text.replace('\n'      ,							'<BR>'				)
									A         =					text.replace('\r'      ,							'<BR>'				)
									A         =					text.replace('\t'      ,							'<TAB>'				)
									A         =					text.replace('—'      ,							'ー'				)
									A         =					text.replace('−'      ,							'ー'				)
									for k, v in self.emoji["emoji"].items():
																if k in text:
																							A         =					text.replace(snake_case      ,							snake_case				)
									if clean:
																A         =					self.clean_text(snake_case				)

									def check_simbol(snake_case  :    Union[str, Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 2:
																							A         =					(int(e[0]				) << 8) + int(e[1]				)
																							if (
																							    (c >= 0xc2a1 and c <= 0xc2bf)
																							    or (c >= 0xc780 and c <= 0xc783)
																							    or (c >= 0xcab9 and c <= 0xcbbf)
																							    or (c >= 0xcc80 and c <= 0xcda2)
																							):
																														return True
																return False

									def checkuae(snake_case  :    List[Any]				):
																A         =					x.encode()
																if len(snake_case				) == 1 and len(snake_case				) == 3:
																							A         =					(int(e[0]				) << 16) + (int(e[1]				) << 8) + int(e[2]				)
																							if c >= 0xe2_8080 and c <= 0xe2_b07f:
																														return True
																return False

									A         =					0
									A         =					[]
									while pos < len(snake_case				):
																A         =					min(len(snake_case				)      ,							pos + self.maxlen + 1				) if text[pos] == '<' else pos + 3
																A         =					[]  # (token_id, token, pos)
																for e in range(snake_case      ,							snake_case      ,							-1				):
																							A         =					text[pos:e]
																							if wd in self.vocab:
																														if wd[0] == "<" and len(snake_case				) > 2:
																																					A         =					[(self.vocab[wd], wd, e)]
																																					break
																														else:
																																					candidates.append((self.vocab[wd], wd, e)				)
																if len(snake_case				) > 0:
																							# the smallest token_id is adopted
																							A      ,  A      ,  A         =					sorted(snake_case      ,							key=lambda snake_case				: x[0]				)[0]
																							result.append(snake_case				)
																							A         =					e
																else:
																							A         =					pos + 1
																							A         =					text[pos:end]
																							if check_simbol(snake_case				):
																														result.append('<KIGOU>'				)
																							elif checkuae(snake_case				):
																														result.append('<U2000U2BFF>'				)
																							else:
																														for i in wd.encode('utf-8'				):
																																					result.append('<|byte%d|>' % i				)
																							A         =					end
									return result



		def 					A_		( self  :    List[str]      ,							snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]="\n"				)			->						List[Any]:






									'''simple docstring'''

									A         =					[]
									A         =					[]
									A         =					self.ids_to_tokens[index][0]
									if word[:6] == "<|byte" and word[-2:] == "|>":
																byte_tokens.append(int(word[6:-2]				)				)
									else:
																if len(snake_case				) > 0:
																							words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
																							A         =					[]
																if word[:7] == "<|emoji" and word[-2:] == "|>":
																							words.append(self.emoji['emoji_inv'][word]				)
																elif word == "<SP>":
																							words.append(' '				)
																elif word == "<BR>":
																							words.append(snake_case				)
																elif word == "<TAB>":
																							words.append('\t'				)
																elif word == "<BLOCK>":
																							words.append('▀'				)
																elif word == "<KIGOU>":
																							words.append('ǀ'				)
																elif word == "<U2000U2BFF>":
																							words.append('‖'				)
																else:
																							words.append(snake_case				)
									if len(snake_case				) > 0:
																words.append(bytearray(snake_case				).decode('utf-8'      ,							errors='replace'				)				)
									A         =					''.join(snake_case				)
									return text



