





"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_fnet import FNetTokenizer
else:
		a		:				int  			=    None

a		:				Any  			=    logging.get_logger(__name__)
a		:				Optional[Any]  			=    {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:				Tuple  			=    {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

a		:				Any  			=    {
    '''google/fnet-base''': 512,
    '''google/fnet-large''': 512,
}

a		:				Tuple  			=    '''▁'''


class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      Optional[Any]         =VOCAB_FILES_NAMES
							lowerCamelCase     :      Dict         =PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase     :      str         =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase     :      Optional[int]         =["""input_ids""", """token_type_ids"""]
							lowerCamelCase     :      List[str]         =FNetTokenizer






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=False   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__="<unk>"   ,      lowerCAmelCase__="[SEP]"   ,      lowerCAmelCase__="<pad>"   ,      lowerCAmelCase__="[CLS]"   ,      lowerCAmelCase__="[MASK]"   ,      **lowerCAmelCase__   ,      )			->				Any:
								# 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.
								a   :	str			=       (
								    AddedToken(lowerCAmelCase__   ,      lstrip=lowerCAmelCase__   ,      rstrip=lowerCAmelCase__   ,      normalized=lowerCAmelCase__   )
								    if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   )
								    else mask_token
								)

								super().__init__(
								    lowerCAmelCase__   ,      tokenizer_file=lowerCAmelCase__   ,      do_lower_case=lowerCAmelCase__   ,      remove_space=lowerCAmelCase__   ,      keep_accents=lowerCAmelCase__   ,      unk_token=lowerCAmelCase__   ,      sep_token=lowerCAmelCase__   ,      pad_token=lowerCAmelCase__   ,      cls_token=lowerCAmelCase__   ,      mask_token=lowerCAmelCase__   ,      **lowerCAmelCase__   ,      )

								a   :	Any			=       do_lower_case
								a   :	str			=       remove_space
								a   :	Any			=       keep_accents
								a   :	Optional[Any]			=       vocab_file
								a   :	int			=       False if not self.vocab_file else True






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Optional[Any]			=       [self.sep_token_id]
								a   :	Union[str, Any]			=       [self.cls_token_id]
								if token_ids_a is None:
									return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[int]:
								a   :	Any			=       [self.sep_token_id]
								a   :	Optional[Any]			=       [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   ) * [0] + len(token_ids_a + sep   ) * [1]






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[str]:
								if not os.path.isdir(lowerCAmelCase__   ):
									logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
									return
								a   :	Union[str, Any]			=       os.path.join(
								    lowerCAmelCase__   ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase__   ):
									copyfile(self.vocab_file   ,      lowerCAmelCase__   )

								return (out_vocab_file,)









"""simple docstring"""




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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       1.5
	a   :	List[str]			=       int(factor * num_class_images      )
	a   :	Optional[Any]			=       ClipClient(
	    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1      )

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

	while True:
		a   :	List[Any]			=       client.query(text=_lowercase      )
		if len(_lowercase      ) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			a   :	Optional[int]			=       int(factor * num_images      )
			a   :	str			=       ClipClient(
			    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1					, )

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	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:
			a   :	Optional[Any]			=       class_images[count]
			count += 1
			try:
				a   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       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    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






	a   :	Optional[int]			=       argparse.ArgumentParser(""					, add_help=_lowercase      )
	parser.add_argument("--class_prompt"					, help="text prompt to retrieve images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--class_data_dir"					, help="path to save images"					, required=_lowercase					, type=_lowercase      )
	parser.add_argument("--num_class_images"					, help="number of images to download"					, default=200					, type=_lowercase      )
	return parser.parse_args()


if __name__ == "__main__":
		a		:				List[Any]  			=    parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


