


'''simple docstring'''




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


_lowerCAmelCase	:		str		  =						'pcuenq/k-upscaler'

def       __UpperCamelCase     (     _A     :					str       , _A     :					Any       , *, _A     :					Optional[int]       , _A     :					Any					)     ->   List[str]:




	"""simple docstring"""





	lowerCAmelCase       :     int   				=			{
	    # norm1
	    F"{diffusers_resnet_prefix}.norm1.linear.weight": checkpoint[F"{resnet_prefix}.main.0.mapper.weight"],
	    F"{diffusers_resnet_prefix}.norm1.linear.bias": checkpoint[F"{resnet_prefix}.main.0.mapper.bias"],
	    # conv1
	    F"{diffusers_resnet_prefix}.conv1.weight": checkpoint[F"{resnet_prefix}.main.2.weight"],
	    F"{diffusers_resnet_prefix}.conv1.bias": checkpoint[F"{resnet_prefix}.main.2.bias"],
	    # norm2
	    F"{diffusers_resnet_prefix}.norm2.linear.weight": checkpoint[F"{resnet_prefix}.main.4.mapper.weight"],
	    F"{diffusers_resnet_prefix}.norm2.linear.bias": checkpoint[F"{resnet_prefix}.main.4.mapper.bias"],
	    # conv2
	    F"{diffusers_resnet_prefix}.conv2.weight": checkpoint[F"{resnet_prefix}.main.6.weight"],
	    F"{diffusers_resnet_prefix}.conv2.bias": checkpoint[F"{resnet_prefix}.main.6.bias"],
	}

	if resnet.conv_shortcut is not None:
		rv.update(
		    {
		        F"{diffusers_resnet_prefix}.conv_shortcut.weight": checkpoint[F"{resnet_prefix}.skip.weight"],
		    }					)

	return rv

def       __UpperCamelCase     (     _A     :					Tuple       , *, _A     :					str       , _A     :					Union[str, Any]					)     ->   int:




	"""simple docstring"""





	lowerCAmelCase				, lowerCAmelCase				, lowerCAmelCase       :     int   				=			checkpoint[F"{attention_prefix}.qkv_proj.weight"].chunk(3       , dim=0					)
	lowerCAmelCase				, lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			checkpoint[F"{attention_prefix}.qkv_proj.bias"].chunk(3       , dim=0					)
	lowerCAmelCase       :     Union[str, Any]   				=			{
	    # norm
	    F"{diffusers_attention_prefix}.norm1.linear.weight": checkpoint[F"{attention_prefix}.norm_in.mapper.weight"],
	    F"{diffusers_attention_prefix}.norm1.linear.bias": checkpoint[F"{attention_prefix}.norm_in.mapper.bias"],
	    # to_q
	    F"{diffusers_attention_prefix}.attn1.to_q.weight": weight_q.squeeze(-1					).squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn1.to_q.bias": bias_q,
	    # to_k
	    F"{diffusers_attention_prefix}.attn1.to_k.weight": weight_k.squeeze(-1					).squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn1.to_k.bias": bias_k,
	    # to_v
	    F"{diffusers_attention_prefix}.attn1.to_v.weight": weight_v.squeeze(-1					).squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn1.to_v.bias": bias_v,
	    # to_out
	    F"{diffusers_attention_prefix}.attn1.to_out.0.weight": checkpoint[F"{attention_prefix}.out_proj.weight"]
	    .squeeze(-1					)
	    .squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn1.to_out.0.bias": checkpoint[F"{attention_prefix}.out_proj.bias"],
	}

	return rv

def       __UpperCamelCase     (     _A     :					str       , *, _A     :					Tuple       , _A     :					str       , _A     :					Optional[Any]					)     ->   Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase				, lowerCAmelCase       :     Optional[Any]   				=			checkpoint[F"{attention_prefix}.kv_proj.weight"].chunk(2       , dim=0					)
	lowerCAmelCase				, lowerCAmelCase       :     Optional[Any]   				=			checkpoint[F"{attention_prefix}.kv_proj.bias"].chunk(2       , dim=0					)

	lowerCAmelCase       :     Dict   				=			{
	    # norm2 (ada groupnorm)
	    F"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight": checkpoint[
	        F"{attention_prefix}.norm_dec.mapper.weight"
	    ],
	    F"{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias": checkpoint[
	        F"{attention_prefix}.norm_dec.mapper.bias"
	    ],
	    # layernorm on encoder_hidden_state
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight": checkpoint[
	        F"{attention_prefix}.norm_enc.weight"
	    ],
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias": checkpoint[
	        F"{attention_prefix}.norm_enc.bias"
	    ],
	    # to_q
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight": checkpoint[
	        F"{attention_prefix}.q_proj.weight"
	    ]
	    .squeeze(-1					)
	    .squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias": checkpoint[
	        F"{attention_prefix}.q_proj.bias"
	    ],
	    # to_k
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight": weight_k.squeeze(-1					).squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias": bias_k,
	    # to_v
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight": weight_v.squeeze(-1					).squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias": bias_v,
	    # to_out
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight": checkpoint[
	        F"{attention_prefix}.out_proj.weight"
	    ]
	    .squeeze(-1					)
	    .squeeze(-1					),
	    F"{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias": checkpoint[
	        F"{attention_prefix}.out_proj.bias"
	    ],
	}

	return rv

def       __UpperCamelCase     (     _A     :					str       , _A     :					Optional[int]       , _A     :					Optional[int]       , _A     :					List[Any]					)     ->   str:




	"""simple docstring"""





	lowerCAmelCase       :     Union[str, Any]   				=			'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
	lowerCAmelCase       :     Tuple   				=			F"{block_prefix}.{block_idx}"

	lowerCAmelCase       :     Dict   				=			{}

	if not hasattr(_A       , 'attentions'					):
		lowerCAmelCase       :     str   				=			1  # resnet only
	elif not block.attentions[0].add_self_attention:
		lowerCAmelCase       :     Any   				=			2  # resnet -> cross-attention
	else:
		lowerCAmelCase       :     List[Any]   				=			3  # resnet -> self-attention -> cross-attention)

	for resnet_idx, resnet in enumerate(block.resnets					):
		# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
		lowerCAmelCase       :     Tuple   				=			F"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
		lowerCAmelCase       :     Optional[Any]   				=			n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
		lowerCAmelCase       :     Tuple   				=			F"{block_prefix}.{idx}" if block_type == 'up' else F"{block_prefix}.{idx}"

		diffusers_checkpoint.update(
		    resnet_to_diffusers_checkpoint(
		        _A       , _A       , diffusers_resnet_prefix=_A       , resnet_prefix=_A					)					)

	if hasattr(_A       , 'attentions'					):
		for attention_idx, attention in enumerate(block.attentions					):
			lowerCAmelCase       :     Optional[Any]   				=			F"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
			lowerCAmelCase       :     Any   				=			n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
			lowerCAmelCase       :     Dict   				=			F"{block_prefix}.{idx}"
			lowerCAmelCase       :     Union[str, Any]   				=			F"{block_prefix}.{idx }"
			lowerCAmelCase       :     Tuple   				=			1 if not attention.add_self_attention else 2
			lowerCAmelCase       :     str   				=			(
			    n * attention_idx + cross_attention_index
			    if block_type == 'up'
			    else n * attention_idx + cross_attention_index + 1
			)
			lowerCAmelCase       :     List[Any]   				=			F"{block_prefix}.{idx }"

			diffusers_checkpoint.update(
			    cross_attn_to_diffusers_checkpoint(
			        _A       , diffusers_attention_prefix=_A       , diffusers_attention_index=2       , attention_prefix=_A       , )					)

			if attention.add_self_attention is True:
				diffusers_checkpoint.update(
				    self_attn_to_diffusers_checkpoint(
				        _A       , diffusers_attention_prefix=_A       , attention_prefix=_A       , )					)

	return diffusers_checkpoint

def       __UpperCamelCase     (     _A     :					Dict       , _A     :					Optional[int]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			{}

	# pre-processing
	diffusers_checkpoint.update(
	    {
	        'conv_in.weight': checkpoint['inner_model.proj_in.weight'],
	        'conv_in.bias': checkpoint['inner_model.proj_in.bias'],
	    }					)

	# timestep and class embedding
	diffusers_checkpoint.update(
	    {
	        'time_proj.weight': checkpoint['inner_model.timestep_embed.weight'].squeeze(-1					),
	        'time_embedding.linear_1.weight': checkpoint['inner_model.mapping.0.weight'],
	        'time_embedding.linear_1.bias': checkpoint['inner_model.mapping.0.bias'],
	        'time_embedding.linear_2.weight': checkpoint['inner_model.mapping.2.weight'],
	        'time_embedding.linear_2.bias': checkpoint['inner_model.mapping.2.bias'],
	        'time_embedding.cond_proj.weight': checkpoint['inner_model.mapping_cond.weight'],
	    }					)

	# down_blocks
	for down_block_idx, down_block in enumerate(model.down_blocks					):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A       , _A       , _A       , 'down'					)					)

	# up_blocks
	for up_block_idx, up_block in enumerate(model.up_blocks					):
		diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A       , _A       , _A       , 'up'					)					)

	# post-processing
	diffusers_checkpoint.update(
	    {
	        'conv_out.weight': checkpoint['inner_model.proj_out.weight'],
	        'conv_out.bias': checkpoint['inner_model.proj_out.bias'],
	    }					)

	return diffusers_checkpoint

def       __UpperCamelCase     (     _A     :					Any					)     ->   List[str]:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			original_config['input_channels'] + original_config['unet_cond_dim']
	lowerCAmelCase       :     Dict   				=			original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

	lowerCAmelCase       :     str   				=			original_config['channels']

	assert (
	    len(set(original_config['depths']					)					) == 1
	), "UNet2DConditionModel currently do not support blocks with different number of layers"
	lowerCAmelCase       :     List[str]   				=			original_config['depths'][0]

	lowerCAmelCase       :     List[str]   				=			original_config['mapping_cond_dim']
	lowerCAmelCase       :     Optional[Any]   				=			original_config['cross_cond_dim']

	lowerCAmelCase       :     List[str]   				=			[]
	lowerCAmelCase       :     Any   				=			[]
	for s, c in zip(original_config['self_attn_depths']       , original_config['cross_attn_depths']					):
		if s:
			lowerCAmelCase       :     List[Any]   				=			'self'
			lowerCAmelCase       :     Dict   				=			'cross' if c else None
		elif c:
			lowerCAmelCase       :     str   				=			'cross'
			lowerCAmelCase       :     str   				=			None
		else:
			lowerCAmelCase       :     Dict   				=			None
			lowerCAmelCase       :     Dict   				=			None
		attna_types.append(_A					)
		attna_types.append(_A					)

	lowerCAmelCase       :     Tuple   				=			UNetaDConditionModel(
	    in_channels=_A       , out_channels=_A       , down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')       , mid_block_type=_A       , up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')       , block_out_channels=_A       , layers_per_block=_A       , act_fn='gelu'       , norm_num_groups=_A       , cross_attention_dim=_A       , attention_head_dim=64       , time_cond_proj_dim=_A       , resnet_time_scale_shift='scale_shift'       , time_embedding_type='fourier'       , timestep_post_act='gelu'       , conv_in_kernel=1       , conv_out_kernel=1       , )

	return unet

def       __UpperCamelCase     (     _A     :					str					)     ->   List[str]:




	"""simple docstring"""





	lowerCAmelCase       :     List[str]   				=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'					)

	lowerCAmelCase       :     Optional[int]   				=			huggingface_hub.hf_hub_download(_A       , 'config_laion_text_cond_latent_upscaler_2.json'					)
	lowerCAmelCase       :     List[str]   				=			huggingface_hub.hf_hub_download(
	    _A       , 'laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'					)
	print(F"loading original model configuration from {orig_config_path}"					)
	print(F"loading original model checkpoint from {orig_weights_path}"					)

	print('converting to diffusers unet'					)
	lowerCAmelCase       :     Union[str, Any]   				=			K.config.load_config(open(_A					)					)['model']
	lowerCAmelCase       :     List[str]   				=			unet_model_from_original_config(_A					)

	lowerCAmelCase       :     Optional[Any]   				=			torch.load(_A       , map_location=_A					)['model_ema']
	lowerCAmelCase       :     Tuple   				=			unet_to_diffusers_checkpoint(_A       , _A					)

	model.load_state_dict(_A       , strict=_A					)
	model.save_pretrained(args.dump_path					)
	print(F"saving converted unet model in {args.dump_path}"					)


if __name__ == "__main__":
				_lowerCAmelCase	:		str		  =						argparse.ArgumentParser()

				parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
				_lowerCAmelCase	:		Optional[int]		  =						parser.parse_args()

				main(args)




'''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


_lowerCAmelCase	:		Tuple		  =						logging.get_logger(__name__)

_lowerCAmelCase	:		Dict		  =						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

_lowerCAmelCase	:		Optional[int]		  =						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

_lowerCAmelCase	:		Tuple		  =						{
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}

@lru_cache()
def       __UpperCamelCase     (     )     ->   Any:




	"""simple docstring"""





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

def       __UpperCamelCase     (     _A     :					Union[str, Any]					)     ->   Any:




	"""simple docstring"""





	lowerCAmelCase       :     Tuple   				=			set()
	lowerCAmelCase       :     List[Any]   				=			word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)					)
		lowerCAmelCase       :     Dict   				=			char
	return pairs






class 			lowerCAmelCase					(					a				):
	_lowerCamelCase :       int         =			VOCAB_FILES_NAMES
	_lowerCamelCase :       Any         =			PRETRAINED_VOCAB_FILES_MAP
	_lowerCamelCase :       int         =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_lowerCamelCase :       Optional[Any]         =			["""input_ids""", """attention_mask"""]



	def __init__(       self			,       snake_case__			,       snake_case__			,       snake_case__="replace"			,       snake_case__="<s>"			,       snake_case__="</s>"			,       snake_case__="</s>"			,       snake_case__="<s>"			,       snake_case__="<unk>"			,       snake_case__="<pad>"			,       snake_case__="<mask>"			,       snake_case__=False			,       **snake_case__			,       ):
		lowerCAmelCase       :     Optional[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else bos_token
		lowerCAmelCase       :     List[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else eos_token
		lowerCAmelCase       :     Any   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else sep_token
		lowerCAmelCase       :     str   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else cls_token
		lowerCAmelCase       :     Tuple   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else unk_token
		lowerCAmelCase       :     List[Any]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else pad_token

		# Mask token behave like a normal word, i.e. include the space before it
		lowerCAmelCase       :     List[str]   				=			AddedToken(snake_case__			,       lstrip=snake_case__			,       rstrip=snake_case__     ) if isinstance(snake_case__			,       snake_case__     ) else mask_token

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

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

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



	@property
	def 					lowercase  (       self     ):
		return len(self.encoder     )



	def 					lowercase  (       self     ):
		return dict(self.encoder			,       **self.added_tokens_encoder     )



	def 					lowercase  (       self			,       snake_case__     ):
		if token in self.cache:
			return self.cache[token]
		lowerCAmelCase       :     List[str]   				=			tuple(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )

		if not pairs:
			return token

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

				if word[i] == first and i < len(snake_case__     ) - 1 and word[i + 1] == second:
					new_word.append(first + second     )
					i += 2
				else:
					new_word.append(word[i]     )
					i += 1
			lowerCAmelCase       :     int   				=			tuple(snake_case__     )
			lowerCAmelCase       :     List[Any]   				=			new_word
			if len(snake_case__     ) == 1:
				break
			else:
				lowerCAmelCase       :     Union[str, Any]   				=			get_pairs(snake_case__     )
		lowerCAmelCase       :     Optional[Any]   				=			' '.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			word
		return word



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     List[Any]   				=			[]
		for token in re.findall(self.pat			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			''.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(snake_case__     ).split(' '     )     )
		return bpe_tokens



	def 					lowercase  (       self			,       snake_case__     ):
		return self.encoder.get(snake_case__			,       self.encoder.get(self.unk_token     )     )



	def 					lowercase  (       self			,       snake_case__     ):
		return self.decoder.get(snake_case__     )



	def 					lowercase  (       self			,       snake_case__     ):
		lowerCAmelCase       :     Union[str, Any]   				=			''.join(snake_case__     )
		lowerCAmelCase       :     Any   				=			bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'			,       errors=self.errors     )
		return text



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if not os.path.isdir(snake_case__     ):
			logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
			return
		lowerCAmelCase       :     Union[str, Any]   				=			os.path.join(
		    snake_case__			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
		lowerCAmelCase       :     int   				=			os.path.join(
		    snake_case__			,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

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

		lowerCAmelCase       :     List[Any]   				=			0
		with open(snake_case__			,       '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 snake_case__     : 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!'     )
					lowerCAmelCase       :     int   				=			token_index
				writer.write(' '.join(snake_case__     ) + '\n'     )
				index += 1

		return vocab_file, merge_file



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		if token_ids_a is None:
			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
		lowerCAmelCase       :     Optional[Any]   				=			[self.cls_token_id]
		lowerCAmelCase       :     List[str]   				=			[self.sep_token_id]
		return cls + token_ids_a + sep + sep + token_ids_a + sep



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None			,       snake_case__ = False     ):
		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=snake_case__			,       token_ids_a=snake_case__			,       already_has_special_tokens=snake_case__     )

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



	def 					lowercase  (       self			,       snake_case__			,       snake_case__ = None     ):
		lowerCAmelCase       :     Union[str, Any]   				=			[self.sep_token_id]
		lowerCAmelCase       :     Tuple   				=			[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 					lowercase  (       self			,       snake_case__			,       snake_case__=False			,       **snake_case__     ):
		lowerCAmelCase       :     List[str]   				=			kwargs.pop('add_prefix_space'			,       self.add_prefix_space     )
		if (is_split_into_words or add_prefix_space) and (len(snake_case__     ) > 0 and not text[0].isspace()):
			lowerCAmelCase       :     Optional[Any]   				=			' ' + text
		return (text, kwargs)
