

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


_UpperCAmelCase		:   int										=       logging.get_logger(__name__)

_UpperCAmelCase		:   int										=       {"""vocab_file""": """spiece.model"""}

_UpperCAmelCase		:   List[Any]										=       {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    }
}

_UpperCAmelCase		:   List[Any]										=       {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

# Segments (not really needed)
_UpperCAmelCase		:   List[str]										=       0
_UpperCAmelCase		:   int										=       1
_UpperCAmelCase		:   List[str]										=       2
_UpperCAmelCase		:   List[Any]										=       3
_UpperCAmelCase		:   Optional[int]										=       4







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Tuple       			=			VOCAB_FILES_NAMES
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			PRETRAINED_VOCAB_FILES_MAP
				__SCREAMING_SNAKE_CASE					: Tuple       			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			'''left'''




				def __init__(					self      ,   snake_case      ,   snake_case=False      ,   snake_case=True      ,   snake_case=False      ,   snake_case="<s>"      ,   snake_case="</s>"      ,   snake_case="<unk>"      ,   snake_case="<sep>"      ,   snake_case="<pad>"      ,   snake_case="<cls>"      ,   snake_case="<mask>"      ,   snake_case=["<eop>", "<eod>"]      ,   snake_case = None      ,   **snake_case      ,   ):
											# Mask token behave like a normal word, i.e. include the space before it
											snake_case_							=    AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case       ) if isinstance(snake_case      ,   snake_case       ) else mask_token

											snake_case_							=    {} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=snake_case      ,   remove_space=snake_case      ,   keep_accents=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   unk_token=snake_case      ,   sep_token=snake_case      ,   pad_token=snake_case      ,   cls_token=snake_case      ,   mask_token=snake_case      ,   additional_special_tokens=snake_case      ,   sp_model_kwargs=self.sp_model_kwargs      ,   **snake_case      ,   )

											snake_case_							=    3

											snake_case_							=    do_lower_case
											snake_case_							=    remove_space
											snake_case_							=    keep_accents
											snake_case_							=    vocab_file

											snake_case_							=    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(snake_case       )




				@property
				def     a							(					self       ):
											return len(self.sp_model       )




				def     a							(					self       ):
											snake_case_							=    {self.convert_ids_to_tokens(snake_case       ): i for i in range(self.vocab_size       )}
											vocab.update(self.added_tokens_encoder       )
											return vocab




				def __getstate__(					self       ):
											snake_case_							=    self.__dict__.copy()
											snake_case_							=    None
											return state




				def __setstate__(					self      ,   snake_case       ):
											snake_case_							=    d

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

											snake_case_							=    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
											self.sp_model.Load(self.vocab_file       )




				def     a							(					self      ,   snake_case       ):
											if self.remove_space:
																		snake_case_							=    ' '.join(inputs.strip().split()       )
											else:
																		snake_case_							=    inputs
											snake_case_							=    outputs.replace('``'      ,   '"'       ).replace('\'\''      ,   '"'       )

											if not self.keep_accents:
																		snake_case_							=    unicodedata.normalize('NFKD'      ,   snake_case       )
																		snake_case_							=    ''.join([c for c in outputs if not unicodedata.combining(snake_case       )]       )
											if self.do_lower_case:
																		snake_case_							=    outputs.lower()

											return outputs




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    self.preprocess_text(snake_case       )
											snake_case_							=    self.sp_model.encode(snake_case      ,   out_type=snake_case       )
											snake_case_							=    []
											for piece in pieces:
																		if len(snake_case       ) > 1 and piece[-1] == str(','       ) and piece[-2].isdigit():
																									snake_case_							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case      ,   ''       )       )
																									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																																if len(cur_pieces[0]       ) == 1:
																																							snake_case_							=    cur_pieces[1:]
																																else:
																																							snake_case_							=    cur_pieces[0][1:]
																									cur_pieces.append(piece[-1]       )
																									new_pieces.extend(snake_case       )
																		else:
																									new_pieces.append(snake_case       )

											return new_pieces




				def     a							(					self      ,   snake_case       ):
											return self.sp_model.PieceToId(snake_case       )




				def     a							(					self      ,   snake_case       ):
											return self.sp_model.IdToPiece(snake_case       )




				def     a							(					self      ,   snake_case       ):
											snake_case_							=    ''.join(snake_case       ).replace(snake_case      ,   ' '       ).strip()
											return out_string




				def     a							(					self      ,   snake_case      ,   snake_case = False      ,   snake_case = None      ,   snake_case = True      ,   **snake_case      ,   ):
											snake_case_							=    kwargs.pop('use_source_tokenizer'      ,   snake_case       )

											snake_case_							=    self.convert_ids_to_tokens(snake_case      ,   skip_special_tokens=snake_case       )

											# To avoid mixing byte-level and unicode for byte-level BPT
											# we need to build string separately for added tokens and byte-level tokens
											# cf. https://github.com/huggingface/transformers/issues/1133
											snake_case_							=    []
											snake_case_							=    []
											for token in filtered_tokens:
																		if skip_special_tokens and token in self.all_special_ids:
																									continue
																		if token in self.added_tokens_encoder:
																									if current_sub_text:
																																sub_texts.append(self.convert_tokens_to_string(snake_case       )       )
																																snake_case_							=    []
																									sub_texts.append(snake_case       )
																		else:
																									current_sub_text.append(snake_case       )
											if current_sub_text:
																		sub_texts.append(self.convert_tokens_to_string(snake_case       )       )

											# Mimic the behavior of the Rust tokenizer:
											# By default, there are no spaces between special tokens
											snake_case_							=    ''.join(snake_case       )

											snake_case_							=    (
											    clean_up_tokenization_spaces
											    if clean_up_tokenization_spaces is not None
											    else self.clean_up_tokenization_spaces
											)
											if clean_up_tokenization_spaces:
																		snake_case_							=    self.clean_up_tokenization(snake_case       )
																		return clean_text
											else:
																		return text




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [self.cls_token_id]
											if token_ids_a is None:
																		return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls




				def     a							(					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 not None:
																		return ([0] * len(snake_case       )) + [1] + ([0] * len(snake_case       )) + [1, 1]
											return ([0] * len(snake_case       )) + [1, 1]




				def     a							(					self      ,   snake_case      ,   snake_case = None       ):
											snake_case_							=    [self.sep_token_id]
											snake_case_							=    [2]

											if token_ids_a is None:
																		return len(token_ids_a + sep       ) * [0] + cls_segment_id
											return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id




				def     a							(					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
											snake_case_							=    os.path.join(
											    snake_case      ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

											if os.path.abspath(self.vocab_file       ) != os.path.abspath(snake_case       ) and os.path.isfile(self.vocab_file       ):
																		copyfile(self.vocab_file      ,   snake_case       )
											elif not os.path.isfile(self.vocab_file       ):
																		with open(snake_case      ,   'wb'       ) as fi:
																									snake_case_							=    self.sp_model.serialized_model_proto()
																									fi.write(snake_case       )

											return (out_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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					snake_case_							=    int(factor * num_images      )
																					snake_case_							=    ClipClient(
																					    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							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:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    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 					__lowerCamelCase	( ):







							'''simple docstring'''






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


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




