'''simple docstring'''
import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case_			:     List[str]  	=  logging.get_logger(__name__)


snake_case_			:     int  	=  'https://openaipublic.azureedge.net/jukebox/models/'
snake_case_			:     List[Any]  	=  {
    'jukebox-1b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '1b_lyrics/prior_level_2.pth.tar',
    ],
    'jukebox-5b-lyrics': [
        '5b/vqvae.pth.tar',
        '5b/prior_level_0.pth.tar',
        '5b/prior_level_1.pth.tar',
        '5b_lyrics/prior_level_2.pth.tar',
    ],
}






def A__   (					UpperCAmelCase_							):





						if key.endswith('.model.1.bias'							) and len(key.split('.'							)							) > 1_0:
												_UpperCamelCase :		Optional[int]			    =       key.replace('.model.1.bias'  ,  '.conv1d_1.bias'							)
						elif key.endswith('.model.1.weight'							) and len(key.split('.'							)							) > 1_0:
												_UpperCamelCase :		List[Any]			    =       key.replace('.model.1.weight'  ,  '.conv1d_1.weight'							)
						elif key.endswith('.model.3.bias'							) and len(key.split('.'							)							) > 1_0:
												_UpperCamelCase :		List[Any]			    =       key.replace('.model.3.bias'  ,  '.conv1d_2.bias'							)
						elif key.endswith('.model.3.weight'							) and len(key.split('.'							)							) > 1_0:
												_UpperCamelCase :		List[str]			    =       key.replace('.model.3.weight'  ,  '.conv1d_2.weight'							)

						if "conditioner_blocks.0." in key:
												_UpperCamelCase :		Union[str, Any]			    =       key.replace('conditioner_blocks.0'  ,  'conditioner_blocks'							)

						if "prime_prior" in key:
												_UpperCamelCase :		Optional[int]			    =       key.replace('prime_prior'  ,  'encoder'							)

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												_UpperCamelCase :		Union[str, Any]			    =       key.replace('.emb.'  ,  '.'							)

						if key.endswith('k'							):  # replace vqvae.X.k with vqvae.X.codebook
												return key.replace('.k'  ,  '.codebook'							)
						if "y_emb." in key:
												return key.replace('y_emb.'  ,  'metadata_embedding.'							)

						if "x_emb.emb." in key:
												_UpperCamelCase :		Any			    =       key.replace('0.x_emb.emb'  ,  'embed_tokens'							)

						if "prime_state_ln" in key:
												return key.replace('prime_state_ln'  ,  'encoder.final_layer_norm'							)
						if ".ln" in key:
												return key.replace('.ln'  ,  '.layer_norm'							)
						if "_ln" in key:
												return key.replace('_ln'  ,  '_layer_norm'							)

						if "prime_state_proj" in key:
												return key.replace('prime_state_proj'  ,  'encoder.proj_in'							)
						if "prime_x_out" in key:
												return key.replace('prime_x_out'  ,  'encoder.lm_head'							)
						if "prior.x_out" in key:
												return key.replace('x_out'  ,  'fc_proj_out'							)
						if "x_emb" in key:
												return key.replace('x_emb'  ,  'embed_tokens'							)

						return key






def A__   (					UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase :		List[str]			    =       {}
						import re

						_UpperCamelCase :		Any			    =       re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'							)
						_UpperCamelCase :		int			    =       re.compile(
						    R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'							)
						_UpperCamelCase :		Optional[Any]			    =       re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'							)

						_UpperCamelCase :		List[Any]			    =       re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'							)
						_UpperCamelCase :		Tuple			    =       re.compile(
						    R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'							)
						_UpperCamelCase :		Tuple			    =       re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'							)

						_UpperCamelCase :		Optional[int]			    =       re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'							)
						_UpperCamelCase :		Tuple			    =       re.compile(
						    R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'							)
						_UpperCamelCase :		Tuple			    =       re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'							)

						for original_key, value in state_dict.items():
												# rename vqvae.encoder keys
												if re_encoder_block_conv_in.fullmatch(__snake_case							):
																		_UpperCamelCase :		Optional[Any]			    =       re_encoder_block_conv_in.match(__snake_case							)
																		_UpperCamelCase :		Dict			    =       regex_match.groups()
																		_UpperCamelCase :		str			    =       int(groups[2]							) * 2 + int(groups[3]							)
																		_UpperCamelCase :		str			    =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
																		_UpperCamelCase :		Any			    =       re_encoder_block_conv_in.sub(__snake_case  ,  __snake_case							)

												elif re_encoder_block_resnet.fullmatch(__snake_case							):
																		_UpperCamelCase :		Dict			    =       re_encoder_block_resnet.match(__snake_case							)
																		_UpperCamelCase :		str			    =       regex_match.groups()
																		_UpperCamelCase :		Any			    =       int(groups[2]							) * 2 + int(groups[3]							)
																		_UpperCamelCase :		str			    =       {'1': 1, '3': 2}[groups[-2]]
																		_UpperCamelCase :		List[Any]			    =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
																		_UpperCamelCase :		int			    =       f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																		_UpperCamelCase :		str			    =       prefix + resnet_block
																		_UpperCamelCase :		Optional[int]			    =       re_encoder_block_resnet.sub(__snake_case  ,  __snake_case							)

												elif re_encoder_block_proj_out.fullmatch(__snake_case							):
																		_UpperCamelCase :		List[str]			    =       re_encoder_block_proj_out.match(__snake_case							)
																		_UpperCamelCase :		Tuple			    =       regex_match.groups()
																		_UpperCamelCase :		Tuple			    =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
																		_UpperCamelCase :		Optional[Any]			    =       re_encoder_block_proj_out.sub(__snake_case  ,  __snake_case							)

												# rename vqvae.decoder keys
												elif re_decoder_block_conv_out.fullmatch(__snake_case							):
																		_UpperCamelCase :		List[Any]			    =       re_decoder_block_conv_out.match(__snake_case							)
																		_UpperCamelCase :		List[Any]			    =       regex_match.groups()
																		_UpperCamelCase :		Dict			    =       int(groups[2]							) * 2 + int(groups[3]							) - 2
																		_UpperCamelCase :		int			    =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
																		_UpperCamelCase :		List[Any]			    =       re_decoder_block_conv_out.sub(__snake_case  ,  __snake_case							)

												elif re_decoder_block_resnet.fullmatch(__snake_case							):
																		_UpperCamelCase :		Any			    =       re_decoder_block_resnet.match(__snake_case							)
																		_UpperCamelCase :		Optional[int]			    =       regex_match.groups()
																		_UpperCamelCase :		int			    =       int(groups[2]							) * 2 + int(groups[3]							) - 2
																		_UpperCamelCase :		Tuple			    =       {'1': 1, '3': 2}[groups[-2]]
																		_UpperCamelCase :		Dict			    =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
																		_UpperCamelCase :		Tuple			    =       f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																		_UpperCamelCase :		List[Any]			    =       prefix + resnet_block
																		_UpperCamelCase :		Optional[int]			    =       re_decoder_block_resnet.sub(__snake_case  ,  __snake_case							)

												elif re_decoder_block_proj_in.fullmatch(__snake_case							):
																		_UpperCamelCase :		Any			    =       re_decoder_block_proj_in.match(__snake_case							)
																		_UpperCamelCase :		Dict			    =       regex_match.groups()
																		_UpperCamelCase :		Union[str, Any]			    =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
																		_UpperCamelCase :		Dict			    =       re_decoder_block_proj_in.sub(__snake_case  ,  __snake_case							)

												# rename prior cond.model to upsampler.upsample_block and resnet
												elif re_prior_cond_conv_out.fullmatch(__snake_case							):
																		_UpperCamelCase :		List[str]			    =       re_prior_cond_conv_out.match(__snake_case							)
																		_UpperCamelCase :		Tuple			    =       regex_match.groups()
																		_UpperCamelCase :		List[str]			    =       int(groups[1]							) * 2 + int(groups[2]							) - 2
																		_UpperCamelCase :		Optional[int]			    =       f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
																		_UpperCamelCase :		Optional[int]			    =       re_prior_cond_conv_out.sub(__snake_case  ,  __snake_case							)

												elif re_prior_cond_resnet.fullmatch(__snake_case							):
																		_UpperCamelCase :		int			    =       re_prior_cond_resnet.match(__snake_case							)
																		_UpperCamelCase :		Any			    =       regex_match.groups()
																		_UpperCamelCase :		Union[str, Any]			    =       int(groups[1]							) * 2 + int(groups[2]							) - 2
																		_UpperCamelCase :		Any			    =       {'1': 1, '3': 2}[groups[-2]]
																		_UpperCamelCase :		Union[str, Any]			    =       f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
																		_UpperCamelCase :		Optional[Any]			    =       f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
																		_UpperCamelCase :		List[str]			    =       prefix + resnet_block
																		_UpperCamelCase :		List[str]			    =       re_prior_cond_resnet.sub(__snake_case  ,  __snake_case							)

												elif re_prior_cond_proj_in.fullmatch(__snake_case							):
																		_UpperCamelCase :		Any			    =       re_prior_cond_proj_in.match(__snake_case							)
																		_UpperCamelCase :		Tuple			    =       regex_match.groups()
																		_UpperCamelCase :		str			    =       f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
																		_UpperCamelCase :		Tuple			    =       re_prior_cond_proj_in.sub(__snake_case  ,  __snake_case							)

												# keep original key
												else:
																		_UpperCamelCase :		Tuple			    =       original_key

												_UpperCamelCase :		Optional[Any]			    =       replace_key(__snake_case							)

												if f'{key_prefix}.{key}' not in model_state_dict or key is None:
																		print(f'failed converting {original_key} to {key}, does not match'							)

												# handle missmatched shape
												elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
																		_UpperCamelCase :		List[Any]			    =       model_state_dict[f'{key_prefix}.{key}']
																		print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'							)
																		_UpperCamelCase :		Tuple			    =       original_key

												_UpperCamelCase :		Dict			    =       original_key
												_UpperCamelCase :		Dict			    =       value

						return new_dict






@torch.no_grad()
def A__   (					UpperCAmelCase_=None  ,  UpperCAmelCase_=None							):





						for file in MODEL_MAPPING[model_name]:
												if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'							):
																		_UpperCamelCase :		Optional[int]			    =       requests.get(f'{PREFIX}{file}'  ,  allow_redirects=__snake_case							)
																		os.makedirs(f'{pytorch_dump_folder_path}/'  ,  exist_ok=__snake_case							)
																		open(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'  ,  'wb'							).write(r.content							)

						_UpperCamelCase :		Optional[Any]			    =       MODEL_MAPPING[model_name.split('/'							)[-1]]

						_UpperCamelCase :		Dict			    =       JukeboxConfig.from_pretrained(__snake_case							)
						_UpperCamelCase :		Tuple			    =       JukeboxModel(__snake_case							)

						_UpperCamelCase :		List[str]			    =       []
						_UpperCamelCase :		Union[str, Any]			    =       {}
						for i, dict_name in enumerate(__snake_case							):
												_UpperCamelCase :		Dict			    =       torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'							)['model']

												_UpperCamelCase :		Union[str, Any]			    =       {}
												for k in old_dic.keys():
																		if k.endswith('.b'							):
																								_UpperCamelCase :		int			    =       old_dic[k]
																		elif k.endswith('.w'							):
																								_UpperCamelCase :		Union[str, Any]			    =       old_dic[k]
																		elif "level_2" not in dict_name and "cond.model." in k:
																								_UpperCamelCase :		Any			    =       old_dic[k]
																		else:
																								_UpperCamelCase :		Any			    =       old_dic[k]

												_UpperCamelCase :		Optional[int]			    =       'vqvae' if i == 0 else f'priors.{3 - i}'
												_UpperCamelCase :		Optional[Any]			    =       fix_jukebox_keys(__snake_case  ,  model.state_dict()  ,  __snake_case  ,  __snake_case							)
												weight_dict.append(__snake_case							)

						_UpperCamelCase :		List[Any]			    =       weight_dict.pop(0							)
						model.vqvae.load_state_dict(__snake_case							)
						for i in range(len(__snake_case							)							):
												model.priors[i].load_state_dict(weight_dict[2 - i]							)

						Path(__snake_case							).mkdir(exist_ok=__snake_case							)
						with open(f'{pytorch_dump_folder_path}/mapping.json'  ,  'w'							) as txtfile:
												json.dump(__snake_case  ,  __snake_case							)

						print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
						model.save_pretrained(__snake_case							)

						return weight_dict


if __name__ == "__main__":
					snake_case_			:     int  	=  argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--model_name',
					    default='jukebox-5b-lyrics',
					    type=str,
					    help='Name of the model you\'d like to convert.',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='jukebox-5b-lyrics-converted',
					    type=str,
					    help='Path to the output PyTorch model directory.',
					)
					snake_case_			:     Tuple  	=  parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







'''simple docstring'''
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


snake_case_			:     List[Any]  	=  logging.get_logger(__name__)


snake_case_			:     str  	=  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
snake_case_			:     Union[str, Any]  	=  {
    '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',
    },
}

snake_case_			:     Any  	=  {
    '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 A__   (					):
						_UpperCamelCase :		str			    =       (
						    list(range(ord('!'							)  ,  ord('~'							) + 1							)							) + list(range(ord('¡'							)  ,  ord('¬'							) + 1							)							) + list(range(ord('®'							)  ,  ord('ÿ'							) + 1							)							)
						)
						_UpperCamelCase :		Any			    =       bs[:]
						_UpperCamelCase :		Union[str, Any]			    =       0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(UpperCAmelCase_							)
																		cs.append(2**8 + n							)
																		n += 1
						_UpperCamelCase :		Any			    =       [chr(UpperCAmelCase_							) for n in cs]
						return dict(zip(UpperCAmelCase_  ,  UpperCAmelCase_							)							)






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase :		Tuple			    =       set()
						_UpperCamelCase :		Dict			    =       word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)							)
												_UpperCamelCase :		Any			    =       char
						return pairs






class 							lowercase__     (							lowercase       ):
	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__ :    Tuple			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Optional[Any]="replace"			,lowerCamelCase__ :    str="<s>"			,lowerCamelCase__ :    str="</s>"			,lowerCamelCase__ :    str="</s>"			,lowerCamelCase__ :    Any="<s>"			,lowerCamelCase__ :    Tuple="<unk>"			,lowerCamelCase__ :    List[str]="<pad>"			,lowerCamelCase__ :    Optional[Any]="<mask>"			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    List[str]			,):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else bos_token
							_UpperCamelCase :		Tuple			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else eos_token
							_UpperCamelCase :		Optional[int]			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else sep_token
							_UpperCamelCase :		Tuple			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else cls_token
							_UpperCamelCase :		Union[str, Any]			    =       AddedToken(lowerCamelCase__			,lstrip=lowerCamelCase__			,rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__			,lowerCamelCase__			) else unk_token
							_UpperCamelCase :		List[str]			    =       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
							_UpperCamelCase :		str			    =       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:
													_UpperCamelCase :		List[str]			    =       json.load(lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       {v: k for k, v in self.encoder.items()}
							_UpperCamelCase :		Optional[Any]			    =       errors  # how to handle errors in decoding
							_UpperCamelCase :		Tuple			    =       bytes_to_unicode()
							_UpperCamelCase :		List[str]			    =       {v: k for k, v in self.byte_encoder.items()}
							with open(lowerCamelCase__			,encoding='utf-8'			) as merges_handle:
													_UpperCamelCase :		Dict			    =       merges_handle.read().split('\n'			)[1:-1]
							_UpperCamelCase :		str			    =       [tuple(merge.split()			) for merge in bpe_merges]
							_UpperCamelCase :		Dict			    =       dict(zip(lowerCamelCase__			,range(len(lowerCamelCase__			)			)			)			)
							_UpperCamelCase :		Tuple			    =       {}
							_UpperCamelCase :		List[Any]			    =       add_prefix_space

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





	@property
	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							return len(self.encoder			)





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return dict(self.encoder			,**self.added_tokens_encoder			)





	def      UpperCamelCase_  (			self :    str			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							if token in self.cache:
													return self.cache[token]
							_UpperCamelCase :		Dict			    =       tuple(lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       get_pairs(lowerCamelCase__			)

							if not pairs:
													return token

							while True:
													_UpperCamelCase :		List[str]			    =       min(lowerCamelCase__			,key=lambda lowerCamelCase__			: self.bpe_ranks.get(lowerCamelCase__			,float('inf'			)			)			)
													if bigram not in self.bpe_ranks:
																			break
													_UpperCamelCase    ,      _UpperCamelCase :		int			    =       bigram
													_UpperCamelCase :		Optional[int]			    =       []
													_UpperCamelCase :		Dict			    =       0
													while i < len(lowerCamelCase__			):
																			try:
																									_UpperCamelCase :		int			    =       word.index(lowerCamelCase__			,lowerCamelCase__			)
																			except ValueError:
																									new_word.extend(word[i:]			)
																									break
																			else:
																									new_word.extend(word[i:j]			)
																									_UpperCamelCase :		Dict			    =       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
													_UpperCamelCase :		int			    =       tuple(lowerCamelCase__			)
													_UpperCamelCase :		Union[str, Any]			    =       new_word
													if len(lowerCamelCase__			) == 1:
																			break
													else:
																			_UpperCamelCase :		Any			    =       get_pairs(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       ' '.join(lowerCamelCase__			)
							_UpperCamelCase :		List[Any]			    =       word
							return word





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       []
							for token in re.findall(self.pat			,lowerCamelCase__			):
													_UpperCamelCase :		int			    =       ''.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      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    List[Any]			):







							'''simple docstring'''
							return self.encoder.get(lowerCamelCase__			,self.encoder.get(self.unk_token			)			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    int			):







							'''simple docstring'''
							return self.decoder.get(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       ''.join(lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       bytearray([self.byte_decoder[c] for c in text]			).decode('utf-8'			,errors=self.errors			)
							return text





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[str] = None			):







							'''simple docstring'''
							if not os.path.isdir(lowerCamelCase__			):
													logger.error(F'Vocabulary path ({save_directory}) should be a directory'			)
													return
							_UpperCamelCase :		List[Any]			    =       os.path.join(
							    lowerCamelCase__			,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)
							_UpperCamelCase :		Union[str, Any]			    =       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'			)

							_UpperCamelCase :		Optional[Any]			    =       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!'			)
																									_UpperCamelCase :		int			    =       token_index
																			writer.write(' '.join(lowerCamelCase__			) + '\n'			)
																			index += 1

							return vocab_file, merge_file





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							_UpperCamelCase :		Optional[Any]			    =       [self.cls_token_id]
							_UpperCamelCase :		str			    =       [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			,lowerCamelCase__ :    bool = False			):







							'''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      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[int]			,lowerCamelCase__ :    Optional[List[int]] = None			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       [self.sep_token_id]
							_UpperCamelCase :		Dict			    =       [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      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    Tuple=False			,**lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       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()):
													_UpperCamelCase :		List[str]			    =       ' ' + text
							return (text, kwargs)


