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 			A__      (	snake_case_ :		Optional[int]       ,				snake_case_ :		Optional[int]       ,				snake_case_ :		List[Any]      ):
	SCREAMING_SNAKE_CASE__:   str=      1.5
	SCREAMING_SNAKE_CASE__:   Dict=      int(factor * num_class_images      )
	SCREAMING_SNAKE_CASE__:   Optional[int]=      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:
		SCREAMING_SNAKE_CASE__:   int=      client.query(text=snake_case_      )
		if len(snake_case_      ) >= factor * num_class_images or num_images > 1E4:
			break
		else:
			SCREAMING_SNAKE_CASE__:   Dict=      int(factor * num_images      )
			SCREAMING_SNAKE_CASE__:   Any=      ClipClient(
			    url='''https://knn.laion.ai/knn-service'''       ,				indice_name='''laion_400m'''       ,				num_images=snake_case_       ,				aesthetic_weight=0.1       ,				)

	SCREAMING_SNAKE_CASE__:   int=      0
	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      0
	SCREAMING_SNAKE_CASE__:   Dict=      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:
			SCREAMING_SNAKE_CASE__:   Dict=      class_images[count]
			count += 1
			try:
				SCREAMING_SNAKE_CASE__:   Union[str, Any]=      requests.get(images['''url''']      )
				if img.status_code == 200:
					SCREAMING_SNAKE_CASE__:   List[str]=      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 			A__      (	):
	SCREAMING_SNAKE_CASE__:   Union[str, Any]=      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_     :   Any 				=				parse_args()
						retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


from collections import OrderedDict
from typing import Mapping

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


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)

lowercase_     :   List[Any] 				=				{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

