
import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

__a         =   [
    # (stable-diffusion, HF Diffusers)
    ('time_embed.0.weight', 'time_embedding.linear_1.weight'),
    ('time_embed.0.bias', 'time_embedding.linear_1.bias'),
    ('time_embed.2.weight', 'time_embedding.linear_2.weight'),
    ('time_embed.2.bias', 'time_embedding.linear_2.bias'),
    ('input_blocks.0.0.weight', 'conv_in.weight'),
    ('input_blocks.0.0.bias', 'conv_in.bias'),
    ('out.0.weight', 'conv_norm_out.weight'),
    ('out.0.bias', 'conv_norm_out.bias'),
    ('out.2.weight', 'conv_out.weight'),
    ('out.2.bias', 'conv_out.bias'),
]

__a         =   [
    # (stable-diffusion, HF Diffusers)
    ('in_layers.0', 'norm1'),
    ('in_layers.2', 'conv1'),
    ('out_layers.0', 'norm2'),
    ('out_layers.3', 'conv2'),
    ('emb_layers.1', 'time_emb_proj'),
    ('skip_connection', 'conv_shortcut'),
]

__a         =   []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
						# loop over downblocks/upblocks

						for j in range(2):
												# loop over resnets/attentions for downblocks
												__a         =   f"down_blocks.{i}.resnets.{j}."
												__a         =   f"input_blocks.{3*i + j + 1}.0."
												unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

												if i < 3:
																		# no attention layers in down_blocks.3
																		__a         =   f"down_blocks.{i}.attentions.{j}."
																		__a         =   f"input_blocks.{3*i + j + 1}.1."
																		unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

						for j in range(3):
												# loop over resnets/attentions for upblocks
												__a         =   f"up_blocks.{i}.resnets.{j}."
												__a         =   f"output_blocks.{3*i + j}.0."
												unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

												if i > 0:
																		# no attention layers in up_blocks.0
																		__a         =   f"up_blocks.{i}.attentions.{j}."
																		__a         =   f"output_blocks.{3*i + j}.1."
																		unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

						if i < 3:
												# no downsample in down_blocks.3
												__a         =   f"down_blocks.{i}.downsamplers.0.conv."
												__a         =   f"input_blocks.{3*(i+1)}.0.op."
												unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

												# no upsample in up_blocks.3
												__a         =   f"up_blocks.{i}.upsamplers.0."
												__a         =   f"output_blocks.{3*i + 2}.{1 if i == 0 else 2}."
												unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

__a         =   'mid_block.attentions.0.'
__a         =   'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
						__a         =   f"mid_block.resnets.{j}."
						__a         =   f"middle_block.{2*j}."
						unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# buyer beware: this is a *brittle* function,
				# and correct output requires that all of these pieces interact in
				# the exact order in which I have arranged them.
				lowercase_	    =   {k: k for k in unet_state_dict.keys()}
				for sd_name, hf_name in unet_conversion_map:
								lowercase_	    =   sd_name
				for k, v in mapping.items():
								if "resnets" in k:
												for sd_part, hf_part in unet_conversion_map_resnet:
																lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
												lowercase_	    =   v
				for k, v in mapping.items():
								for sd_part, hf_part in unet_conversion_map_layer:
												lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
								lowercase_	    =   v
				lowercase_	    =   {v: unet_state_dict[k] for k, v in mapping.items()}
				return new_state_dict


# ================#
# VAE Conversion #
# ================#

__a         =   [
    # (stable-diffusion, HF Diffusers)
    ('nin_shortcut', 'conv_shortcut'),
    ('norm_out', 'conv_norm_out'),
    ('mid.attn_1.', 'mid_block.attentions.0.'),
]

for i in range(4):
						# down_blocks have two resnets
						for j in range(2):
												__a         =   f"encoder.down_blocks.{i}.resnets.{j}."
												__a         =   f"encoder.down.{i}.block.{j}."
												vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

						if i < 3:
												__a         =   f"down_blocks.{i}.downsamplers.0."
												__a         =   f"down.{i}.downsample."
												vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

												__a         =   f"up_blocks.{i}.upsamplers.0."
												__a         =   f"up.{3-i}.upsample."
												vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

						# up_blocks have three resnets
						# also, up blocks in hf are numbered in reverse from sd
						for j in range(3):
												__a         =   f"decoder.up_blocks.{i}.resnets.{j}."
												__a         =   f"decoder.up.{3-i}.block.{j}."
												vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
						__a         =   f"mid_block.resnets.{i}."
						__a         =   f"mid.block_{i+1}."
						vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


__a         =   [
    # (stable-diffusion, HF Diffusers)
    ('norm.', 'group_norm.'),
    ('q.', 'query.'),
    ('k.', 'key.'),
    ('v.', 'value.'),
    ('proj_out.', 'proj_attn.'),
]




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				# convert HF linear weights to SD conv2d weights
				return w.reshape(*w.shape		,  1		,  1   )




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   {k: k for k in vae_state_dict.keys()}
				for k, v in mapping.items():
								for sd_part, hf_part in vae_conversion_map:
												lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
								lowercase_	    =   v
				for k, v in mapping.items():
								if "attentions" in k:
												for sd_part, hf_part in vae_conversion_map_attn:
																lowercase_	    =   v.replace(snake_case__		,  snake_case__   )
												lowercase_	    =   v
				lowercase_	    =   {v: vae_state_dict[k] for k, v in mapping.items()}
				lowercase_	    =   ['''q''', '''k''', '''v''', '''proj_out''']
				for k, v in new_state_dict.items():
								for weight_name in weights_to_convert:
												if F'''mid.attn_1.{weight_name}.weight''' in k:
																print(F'''Reshaping {k} for SD format'''   )
																lowercase_	    =   reshape_weight_for_sd(snake_case__   )
				return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


__a         =   [
    # (stable-diffusion, HF Diffusers)
    ('resblocks.', 'text_model.encoder.layers.'),
    ('ln_1', 'layer_norm1'),
    ('ln_2', 'layer_norm2'),
    ('.c_fc.', '.fc1.'),
    ('.c_proj.', '.fc2.'),
    ('.attn', '.self_attn'),
    ('ln_final.', 'transformer.text_model.final_layer_norm.'),
    ('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
    ('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
__a         =   {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__a         =   re.compile('|'.join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__a         =   {'q': 0, 'k': 1, 'v': 2}




def 							a		(				snake_case__:   Dict   ):


				'''simple docstring'''
				lowercase_	    =   {}
				lowercase_	    =   {}
				lowercase_	    =   {}
				for k, v in text_enc_dict.items():
								if (
								    k.endswith('''.self_attn.q_proj.weight'''   )
								    or k.endswith('''.self_attn.k_proj.weight'''   )
								    or k.endswith('''.self_attn.v_proj.weight'''   )
								):
												lowercase_	    =   k[: -len('''.q_proj.weight'''   )]
												lowercase_	    =   k[-len('''q_proj.weight'''   )]
												if k_pre not in capture_qkv_weight:
																lowercase_	    =   [None, None, None]
												lowercase_	    =   v
												continue

								if (
								    k.endswith('''.self_attn.q_proj.bias'''   )
								    or k.endswith('''.self_attn.k_proj.bias'''   )
								    or k.endswith('''.self_attn.v_proj.bias'''   )
								):
												lowercase_	    =   k[: -len('''.q_proj.bias'''   )]
												lowercase_	    =   k[-len('''q_proj.bias'''   )]
												if k_pre not in capture_qkv_bias:
																lowercase_	    =   [None, None, None]
												lowercase_	    =   v
												continue

								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   v

				for k_pre, tensors in capture_qkv_weight.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''   )
								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   torch.cat(snake_case__   )

				for k_pre, tensors in capture_qkv_bias.items():
								if None in tensors:
												raise Exception('''CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'''   )
								lowercase_	    =   textenc_pattern.sub(lambda snake_case__   : protected[re.escape(m.group(0   )   )]		,  snake_case__   )
								lowercase_	    =   torch.cat(snake_case__   )

				return new_state_dict




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				return text_enc_dict


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()

						parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
						parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
						parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
						parser.add_argument(
						    '--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
						)

						__a         =   parser.parse_args()

						assert args.model_path is not None, "Must provide a model path!"

						assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

						# Path for safetensors
						__a         =   osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
						__a         =   osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
						__a         =   osp.join(args.model_path, 'text_encoder', 'model.safetensors')

						# Load models from safetensors if it exists, if it doesn't pytorch
						if osp.exists(unet_path):
												__a         =   load_file(unet_path, device='cpu')
						else:
												__a         =   osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
												__a         =   torch.load(unet_path, map_location='cpu')

						if osp.exists(vae_path):
												__a         =   load_file(vae_path, device='cpu')
						else:
												__a         =   osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
												__a         =   torch.load(vae_path, map_location='cpu')

						if osp.exists(text_enc_path):
												__a         =   load_file(text_enc_path, device='cpu')
						else:
												__a         =   osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
												__a         =   torch.load(text_enc_path, map_location='cpu')

						# Convert the UNet model
						__a         =   convert_unet_state_dict(unet_state_dict)
						__a         =   {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}

						# Convert the VAE model
						__a         =   convert_vae_state_dict(vae_state_dict)
						__a         =   {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}

						# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
						__a         =   'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict

						if is_vaa_model:
												# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
												__a         =   {'transformer.' + k: v for k, v in text_enc_dict.items()}
												__a         =   convert_text_enc_state_dict_vaa(text_enc_dict)
												__a         =   {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
						else:
												__a         =   convert_text_enc_state_dict(text_enc_dict)
												__a         =   {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}

						# Put together new checkpoint
						__a         =   {**unet_state_dict, **vae_state_dict, **text_enc_dict}
						if args.half:
												__a         =   {k: v.half() for k, v in state_dict.items()}

						if args.use_safetensors:
												save_file(state_dict, args.checkpoint_path)
						else:
												__a         =   {'state_dict': state_dict}
												torch.save(state_dict, args.checkpoint_path)






import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a         =   logging.get_logger(__name__)

__a         =   '▁'

__a         =   {'vocab_file': 'vocab.txt', 'sentencepiece_model_ckpt': 'sentencepiece.bpe.model'}

__a         =   {
    'sentencepiece_model_file': 'sentencepiece.bpe.model',
    'vocab_file': 'vocab.txt',
}

__a         =   {
    'vocab_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt',
    },
    'sentencepiece_model_file': {
        'ernie-m-base': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
        'ernie-m-large': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model',
    },
}

__a         =   {
    'ernie-m-base': 5_1_4,
    'ernie-m-large': 5_1_4,
}

__a         =   {
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[str]       	=      ["input_ids"]

	a     :Dict       	=      VOCAB_FILES_NAMES
	a     :Union[str, Any]       	=      PRETRAINED_INIT_CONFIGURATION
	a     :Dict       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :List[str]       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Tuple       	=      RESOURCE_FILES_NAMES
	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]=None			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="utf8"			,				SCREAMING_SNAKE_CASE_    :     List[Any]="[UNK]"			,				SCREAMING_SNAKE_CASE_    :     Dict="[SEP]"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="[PAD]"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="[CLS]"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="[MASK]"			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[str, Any]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[int]			,				)			->	None:
					# Mask token behave like a normal word, i.e. include the space before it and
					# is included in the raw text, there should be a match in a non-normalized sentence.

					lowercase_	    =   {} if sp_model_kwargs is None else sp_model_kwargs
					super().__init__(
					    do_lower_case=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				vocab_file=SCREAMING_SNAKE_CASE_			,				encoding=SCREAMING_SNAKE_CASE_			,				sp_model_kwargs=self.sp_model_kwargs			,				**SCREAMING_SNAKE_CASE_			,				)
					lowercase_	    =   do_lower_case
					lowercase_	    =   sentencepiece_model_ckpt
					lowercase_	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(SCREAMING_SNAKE_CASE_			)

					# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
					if vocab_file is not None:
									lowercase_	    =   self.load_vocab(filepath=SCREAMING_SNAKE_CASE_			)
					else:
									lowercase_	    =   {self.sp_model.id_to_piece(SCREAMING_SNAKE_CASE_			): id for id in range(self.sp_model.get_piece_size()			)}
					lowercase_	    =   {v: k for k, v in self.vocab.items()}
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     str			)			->	List[Any]:
					if text is None:
									return None

					lowercase_	    =   self.tokenize(SCREAMING_SNAKE_CASE_			)
					lowercase_    ,      lowercase_	    =   '''''', []

					for i, ch in enumerate(SCREAMING_SNAKE_CASE_			):
									if ch in self.SP_CHAR_MAPPING:
													lowercase_	    =   self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE_			)
									else:
													lowercase_	    =   unicodedata.normalize('''NFKC'''			,				SCREAMING_SNAKE_CASE_			)
									if self.is_whitespace(SCREAMING_SNAKE_CASE_			):
													continue
									normalized_text += ch
									char_mapping.extend([i] * len(SCREAMING_SNAKE_CASE_			)			)

					lowercase_    ,      lowercase_    ,      lowercase_	    =   normalized_text, [], 0

					if self.do_lower_case:
									lowercase_	    =   text.lower()

					for token in split_tokens:
									if token[:1] == "▁":
													lowercase_	    =   token[1:]
									lowercase_	    =   text[offset:].index(SCREAMING_SNAKE_CASE_			) + offset
									lowercase_	    =   start + len(SCREAMING_SNAKE_CASE_			)

									token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)			)
									lowercase_	    =   end
					return token_mapping
	@property
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Union[str, Any]:
					return len(self.vocab			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[int]:
					return dict(self.vocab			,				**self.added_tokens_encoder			)
	def __getstate__(			self    :     Dict			)			->	int:
					lowercase_	    =   self.__dict__.copy()
					lowercase_	    =   None
					return state
	def __setstate__(			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	List[Any]:
					lowercase_	    =   d

					# for backward compatibility
					if not hasattr(self			,				'''sp_model_kwargs'''			):
									lowercase_	    =   {}

					lowercase_	    =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
					self.sp_model.Load(self.sentencepiece_model_ckpt			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	str:
					return "".join((self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) for c in text)			)
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=6_4			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.1			)			->	Any:

					if self.sp_model_kwargs.get('''enable_sampling'''			) is True:
									lowercase_	    =   True
					if self.sp_model_kwargs.get('''alpha'''			) is not None:
									lowercase_	    =   self.sp_model_kwargs.get('''alpha'''			)
					if self.sp_model_kwargs.get('''nbest_size'''			) is not None:
									lowercase_	    =   self.sp_model_kwargs.get('''nbest_size'''			)

					if not enable_sampling:
									lowercase_	    =   self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE_			)
					else:
									lowercase_	    =   self.sp_model.SampleEncodeAsPieces(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   []
					for pi, piece in enumerate(SCREAMING_SNAKE_CASE_			):
									if piece == SPIECE_UNDERLINE:
													if not pieces[pi + 1].startswith(SCREAMING_SNAKE_CASE_			) and pi != 0:
																	new_pieces.append(SCREAMING_SNAKE_CASE_			)
																	continue
													else:
																	continue
									lowercase_	    =   0
									for i, chunk in enumerate(SCREAMING_SNAKE_CASE_			):
													if chunk == SPIECE_UNDERLINE:
																	continue
													if self.is_ch_char(SCREAMING_SNAKE_CASE_			) or self.is_punct(SCREAMING_SNAKE_CASE_			):
																	if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																					new_pieces.append(piece[lst_i:i]			)
																	new_pieces.append(SCREAMING_SNAKE_CASE_			)
																	lowercase_	    =   i + 1
													elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
																	if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																					new_pieces.append(piece[lst_i:i]			)
																	lowercase_	    =   i
													elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
																	if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																					new_pieces.append(piece[lst_i:i]			)
																	lowercase_	    =   i
									if len(SCREAMING_SNAKE_CASE_			) > lst_i:
													new_pieces.append(piece[lst_i:]			)
					return new_pieces
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Optional[Any]:
					lowercase_	    =   ''''''.join(SCREAMING_SNAKE_CASE_			).replace(SCREAMING_SNAKE_CASE_			,				''' '''			).strip()
					return out_string
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			)			->	Any:
					lowercase_	    =   self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   ''''''.join(SCREAMING_SNAKE_CASE_			).replace(SCREAMING_SNAKE_CASE_			,				''' '''			).strip()
					return out_string
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	List[str]:
					return self.vocab.get(SCREAMING_SNAKE_CASE_			,				self.vocab.get(self.unk_token			)			)
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	List[Any]:
					return self.reverse_vocab.get(SCREAMING_SNAKE_CASE_			,				self.unk_token			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     str=None			)			->	List[str]:
					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]=None			)			->	int:
					if offset_mapping_a is None:
									return [(0, 0)] + offset_mapping_a + [(0, 0)]

					return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=None			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=False			)			->	Any:

					if already_has_special_tokens:
									if token_ids_a is not None:
													raise ValueError(
													    '''You should not supply a second sequence if the provided sequence of '''
													    '''ids is already formatted with special tokens for the model.'''			)
									return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

					if token_ids_a is not None:
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE_			)) + [1]
	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					# called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
					if token_ids_a is None:
									# [CLS] X [SEP]
									return (len(SCREAMING_SNAKE_CASE_			) + 2) * [0]

					# [CLS] A [SEP] [SEP] B [SEP]
					return [0] * (len(SCREAMING_SNAKE_CASE_			) + 1) + [1] * (len(SCREAMING_SNAKE_CASE_			) + 3)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			)			->	Optional[int]:
					if "\u4e00" <= char <= "\u9fff":
									return True
					return False
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Tuple:
					if ("a" <= char <= "z") or ("A" <= char <= "Z"):
									return True
					return False
	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     int			)			->	List[Any]:
					if char in ",;:.?!~，；：。？！《》【】":
									return True
					return False
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			)			->	Union[str, Any]:
					if char == " " or char == "\t" or char == "\n" or char == "\r":
									return True
					if len(SCREAMING_SNAKE_CASE_			) == 1:
									lowercase_	    =   unicodedata.category(SCREAMING_SNAKE_CASE_			)
									if cat == "Zs":
													return True
					return False
	def 		_lowercase    (			self    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Tuple:
					lowercase_	    =   {}
					with io.open(SCREAMING_SNAKE_CASE_			,				'''r'''			,				encoding='''utf-8'''			) as f:
									for index, line in enumerate(SCREAMING_SNAKE_CASE_			):
													lowercase_	    =   line.rstrip('''\n'''			)
													lowercase_	    =   int(SCREAMING_SNAKE_CASE_			)

					return token_to_idx




	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					lowercase_	    =   0
					if os.path.isdir(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   os.path.join(
									    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)
					else:
									lowercase_	    =   (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
					with open(SCREAMING_SNAKE_CASE_			,				'''w'''			,				encoding='''utf-8'''			) as writer:
									for token, token_index in sorted(self.vocab.items()			,				key=lambda SCREAMING_SNAKE_CASE_			: kv[1]			):
													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!'''			)
																	lowercase_	    =   token_index
													writer.write(token + '''\n'''			)
													index += 1

					lowercase_	    =   os.path.join(SCREAMING_SNAKE_CASE_			,				'''sentencepiece.bpe.model'''			)
					with open(SCREAMING_SNAKE_CASE_			,				'''wb'''			) as fi:
									lowercase_	    =   self.sp_model.serialized_model_proto()
									fi.write(SCREAMING_SNAKE_CASE_			)

					return (vocab_file,)




