




'''simple docstring'''







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_14,
    'ernie-m-large': 5_14,
}

A     ={
    'ernie-m-base': {'do_lower_case': False},
    'ernie-m-large': {'do_lower_case': False},
}


class 			_a  (	__lowerCamelCase ):
       __a				:     List[str]   				=					["input_ids"]

       __a				:     List[str]   				=					VOCAB_FILES_NAMES
       __a				:     Tuple   				=					PRETRAINED_INIT_CONFIGURATION
       __a				:     Any   				=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __a				:     Optional[Any]   				=					PRETRAINED_VOCAB_FILES_MAP
       __a				:     Optional[Any]   				=					RESOURCE_FILES_NAMES
       def __init__(	self						:	Any   ,				lowercase						:	int   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	List[Any]=False   ,				lowercase						:	Union[str, Any]="utf8"   ,				lowercase						:	Dict="[UNK]"   ,				lowercase						:	Any="[SEP]"   ,				lowercase						:	int="[PAD]"   ,				lowercase						:	Union[str, Any]="[CLS]"   ,				lowercase						:	int="[MASK]"   ,				lowercase						:	Optional[Any] = None   ,				**lowercase						:	int   ,				):



           '''simple docstring'''

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

           # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
           if vocab_file is not None:
               UpperCAmelCase			       =		self.load_vocab(filepath=lowercase				)
           else:
               UpperCAmelCase			       =		{self.sp_model.id_to_piece(lowercase				): id for id in range(self.sp_model.get_piece_size()				)}
           UpperCAmelCase			       =		{v: k for k, v in self.vocab.items()}
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	int				):



           '''simple docstring'''

           if text is None:
               return None

           UpperCAmelCase			       =		self.tokenize(lowercase				)
           UpperCAmelCase       ,     UpperCAmelCase			       =		'''''', []

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

           UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		normalized_text, [], 0

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

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

               token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)				)
               UpperCAmelCase			       =		end
           return token_mapping
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return len(self.vocab				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           return dict(self.vocab   ,				**self.added_tokens_encoder				)
       def __getstate__(	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.__dict__.copy()
           UpperCAmelCase			       =		None
           return state
       def __setstate__(	self						:	List[str]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		d

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

           UpperCAmelCase			       =		spm.SentencePieceProcessor(**self.sp_model_kwargs				)
           self.sp_model.Load(self.sentencepiece_model_ckpt				)
       def     A      (	self						:	Any   ,				lowercase						:	List[Any]				):



           '''simple docstring'''

           return "".join((self.SP_CHAR_MAPPING.get(lowercase   ,				lowercase				) for c in text)				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Tuple   ,				lowercase						:	str=False   ,				lowercase						:	Dict=64   ,				lowercase						:	Dict=0.1				):



           '''simple docstring'''

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

           if not enable_sampling:
               UpperCAmelCase			       =		self.sp_model.EncodeAsPieces(lowercase				)
           else:
               UpperCAmelCase			       =		self.sp_model.SampleEncodeAsPieces(lowercase   ,				lowercase   ,				lowercase				)
           UpperCAmelCase			       =		[]
           for pi, piece in enumerate(lowercase				):
               if piece == SPIECE_UNDERLINE:
                   if not pieces[pi + 1].startswith(lowercase				) and pi != 0:
                       new_pieces.append(lowercase				)
                       continue
                   else:
                       continue
               UpperCAmelCase			       =		0
               for i, chunk in enumerate(lowercase				):
                   if chunk == SPIECE_UNDERLINE:
                       continue
                   if self.is_ch_char(lowercase				) or self.is_punct(lowercase				):
                       if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                           new_pieces.append(piece[lst_i:i]				)
                       new_pieces.append(lowercase				)
                       UpperCAmelCase			       =		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]				)
                       UpperCAmelCase			       =		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]				)
                       UpperCAmelCase			       =		i
               if len(lowercase				) > lst_i:
                   new_pieces.append(piece[lst_i:]				)
           return new_pieces
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		''''''.join(lowercase				).replace(lowercase   ,				''' '''				).strip()
           return out_string
       def     A      (	self						:	Dict   ,				lowercase						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.convert_ids_to_tokens(lowercase				)
           UpperCAmelCase			       =		''''''.join(lowercase				).replace(lowercase   ,				''' '''				).strip()
           return out_string
       def     A      (	self						:	str   ,				lowercase						:	Dict				):



           '''simple docstring'''

           return self.vocab.get(lowercase   ,				self.vocab.get(self.unk_token				)				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	int				):



           '''simple docstring'''

           return self.reverse_vocab.get(lowercase   ,				self.unk_token				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	Any   ,				lowercase						:	Any=None				):



           '''simple docstring'''

           if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           UpperCAmelCase			       =		[self.cls_token_id]
           UpperCAmelCase			       =		[self.sep_token_id]
           return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	List[str]   ,				lowercase						:	str=None				):



           '''simple docstring'''

           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     A      (	self						:	int   ,				lowercase						:	List[str]   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	List[str]=False				):



           '''simple docstring'''

           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(lowercase				)) + [1, 1] + ([0] * len(lowercase				)) + [1]
           return [1] + ([0] * len(lowercase				)) + [1]
       def     A      (	self						:	int   ,				lowercase						:	Any   ,				lowercase						:	str = None				):



           '''simple docstring'''

           if token_ids_a is None:
               # [CLS] X [SEP]
               return (len(lowercase				) + 2) * [0]

           # [CLS] A [SEP] [SEP] B [SEP]
           return [0] * (len(lowercase				) + 1) + [1] * (len(lowercase				) + 3)
       def     A      (	self						:	List[Any]   ,				lowercase						:	Dict				):



           '''simple docstring'''

           if "\u4e00" <= char <= "\u9fff":
               return True
           return False
       def     A      (	self						:	List[Any]   ,				lowercase						:	Optional[int]				):



           '''simple docstring'''

           if ("a" <= char <= "z") or ("A" <= char <= "Z"):
               return True
           return False
       def     A      (	self						:	str   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           if char in ",;:.?!~，；：。？！《》【】":
               return True
           return False
       def     A      (	self						:	Optional[Any]   ,				lowercase						:	str				):



           '''simple docstring'''

           if char == " " or char == "\t" or char == "\n" or char == "\r":
               return True
           if len(lowercase				) == 1:
               UpperCAmelCase			       =		unicodedata.category(lowercase				)
               if cat == "Zs":
                   return True
           return False
       def     A      (	self						:	List[str]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		{}
           with io.open(lowercase   ,				'''r'''   ,				encoding='''utf-8'''				) as f:
               for index, line in enumerate(lowercase				):
                   UpperCAmelCase			       =		line.rstrip('''\n'''				)
                   UpperCAmelCase			       =		int(lowercase				)

           return token_to_idx







       def     A      (	self						:	Optional[Any]   ,				lowercase						:	List[str]   ,				lowercase						:	List[Any] = None				):



           '''simple docstring'''

           UpperCAmelCase			       =		0
           if os.path.isdir(lowercase				):
               UpperCAmelCase			       =		os.path.join(
                   lowercase   ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
           else:
               UpperCAmelCase			       =		(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
           with open(lowercase   ,				'''w'''   ,				encoding='''utf-8'''				) as writer:
               for token, token_index in sorted(self.vocab.items()   ,				key=lambda lowercase				: 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!'''				)
                       UpperCAmelCase			       =		token_index
                   writer.write(token + '''\n'''				)
                   index += 1

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

           return (vocab_file,)






import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

	os.makedirs(F'''{class_data_dir}/images''' ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if len(list(Path(F'''{class_data_dir}/images'''							).iterdir()							)							) >= num_class_images:
		return

	while True:
		lowercase       =    client.query(text=__SCREAMING_SNAKE_CASE							)
		if len(__SCREAMING_SNAKE_CASE							) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			lowercase       =    int(factor * num_images							)
			lowercase       =    ClipClient(
			    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1 ,							)

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

	with open(F'''{class_data_dir}/caption.txt''' ,							'w'							) as fa, open(F'''{class_data_dir}/urls.txt''' ,							'w'							) as fa, open(
	    F'''{class_data_dir}/images.txt''' ,							'w'							) as fa:
		while total < num_class_images:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    Image.open(BytesIO(img.content							)							)
					with open(F'''{class_data_dir}/images/{total}.jpg''' ,							'wb'							) as f:
						f.write(img.content							)
					fa.write(images['caption'] + '\n'							)
					fa.write(images['url'] + '\n'							)
					fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'							)
					total += 1
					pbar.update(1							)
				else:
					continue
			except Exception:
				continue
	return






def 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser('' ,							add_help=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_prompt' ,							help='text prompt to retrieve images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_data_dir' ,							help='path to save images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--num_class_images' ,							help='number of images to download' ,							default=200 ,							type=__SCREAMING_SNAKE_CASE							)
	return parser.parse_args()


if __name__ == "__main__":
	UpperCAmelCase	      = parse_args()
	retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)

