
'''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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



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

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

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

    A       :   Dict					=						0
    A       :   Optional[int]					=						0
    A       :   Tuple					=						tqdm(desc='''downloading real regularization images'''		,				total=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:
            A       :   Optional[Any]					=						class_images[count]
            count += 1
            try:
                A       :   Dict					=						requests.get(images['''url''']						)
                if img.status_code == 200:
                    A       :   Tuple					=						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'''



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


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






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''bert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						vocab_size
         A       :   Optional[Any]					=						hidden_size
         A       :   List[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Dict					=						hidden_act
         A       :   Optional[Any]					=						intermediate_size
         A       :   List[Any]					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   List[str]					=						type_vocab_size
         A       :   Dict					=						initializer_range
         A       :   str					=						layer_norm_eps
         A       :   int					=						position_embedding_type
         A       :   Dict					=						use_cache
         A       :   str					=						classifier_dropout





class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         if self.task == "multiple-choice":
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A       :   Optional[int]					=						{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]	)




