






'''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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[str],       SCREAMING_SNAKE_CASE__:						int     )	->      List[str]:

   """simple docstring"""
   __a					  =     1.5
   __a					  =     int(factor * num_class_images     )
   __a					  =     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:
      __a					  =     client.query(text=SCREAMING_SNAKE_CASE__     )
      if len(SCREAMING_SNAKE_CASE__     ) >= factor * num_class_images or num_images > 1e4:
         break
      else:
         __a					  =     int(factor * num_images     )
         __a					  =     ClipClient(
             url='https://knn.laion.ai/knn-service',       indice_name='laion_400m',       num_images=SCREAMING_SNAKE_CASE__,       aesthetic_weight=0.1,       )

   __a					  =     0
   __a					  =     0
   __a					  =     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:
         __a					  =     class_images[count]
         count += 1
         try:
            __a					  =     requests.get(images['url']     )
            if img.status_code == 200:
               __a					  =     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						(					)	->      List[str]:

   """simple docstring"""
   __a					  =     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   :							Any         =   parse_args()
 retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)







'''simple docstring'''



import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
 pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase   :							int         =   logging.get_logger(__name__)

__UpperCamelCase   :							int         =   {
    """kakaobrain/align-base""": """https://huggingface.co/kakaobrain/align-base/resolve/main/config.json""",
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_text_model"




      def __init__(	self			, lowerCamelCase=3_0522			, lowerCamelCase=768			, lowerCamelCase=12			, lowerCamelCase=12			, lowerCamelCase=3072			, lowerCamelCase="gelu"			, lowerCamelCase=0.1			, lowerCamelCase=0.1			, lowerCamelCase=512			, lowerCamelCase=2			, lowerCamelCase=0.02			, lowerCamelCase=1e-12			, lowerCamelCase=0			, lowerCamelCase="absolute"			, lowerCamelCase=True			, **lowerCamelCase			, )				->List[Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     vocab_size
         __a					  =     hidden_size
         __a					  =     num_hidden_layers
         __a					  =     num_attention_heads
         __a					  =     hidden_act
         __a					  =     intermediate_size
         __a					  =     hidden_dropout_prob
         __a					  =     attention_probs_dropout_prob
         __a					  =     max_position_embeddings
         __a					  =     type_vocab_size
         __a					  =     initializer_range
         __a					  =     layer_norm_eps
         __a					  =     position_embedding_type
         __a					  =     use_cache
         __a					  =     pad_token_id






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the text config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['text_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align_vision_model"




      def __init__(	self			, lowerCamelCase = 3			, lowerCamelCase = 600			, lowerCamelCase = 2.0			, lowerCamelCase = 3.1			, lowerCamelCase = 8			, lowerCamelCase = [3, 3, 5, 3, 5, 5, 3]			, lowerCamelCase = [32, 16, 24, 40, 80, 112, 192]			, lowerCamelCase = [16, 24, 40, 80, 112, 192, 320]			, lowerCamelCase = []			, lowerCamelCase = [1, 2, 2, 2, 1, 2, 1]			, lowerCamelCase = [1, 2, 2, 3, 3, 4, 1]			, lowerCamelCase = [1, 6, 6, 6, 6, 6, 6]			, lowerCamelCase = 0.25			, lowerCamelCase = "swish"			, lowerCamelCase = 2560			, lowerCamelCase = "mean"			, lowerCamelCase = 0.02			, lowerCamelCase = 0.001			, lowerCamelCase = 0.99			, lowerCamelCase = 0.2			, **lowerCamelCase			, )				->Dict:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         __a					  =     num_channels
         __a					  =     image_size
         __a					  =     width_coefficient
         __a					  =     depth_coefficient
         __a					  =     depth_divisor
         __a					  =     kernel_sizes
         __a					  =     in_channels
         __a					  =     out_channels
         __a					  =     depthwise_padding
         __a					  =     strides
         __a					  =     num_block_repeats
         __a					  =     expand_ratios
         __a					  =     squeeze_expansion_ratio
         __a					  =     hidden_act
         __a					  =     hidden_dim
         __a					  =     pooling_type
         __a					  =     initializer_range
         __a					  =     batch_norm_eps
         __a					  =     batch_norm_momentum
         __a					  =     drop_connect_rate
         __a					  =     sum(lowerCamelCase   ) * 4






      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, **lowerCamelCase   )				->"PretrainedConfig":



         '''simple docstring'''
         cls._set_token_in_kwargs(lowerCamelCase   )

         __a  ,      __a					  =     cls.get_config_dict(lowerCamelCase			, **lowerCamelCase   )

         # get the vision config dict if we are loading from AlignConfig
         if config_dict.get('model_type'   ) == "align":
            __a					  =     config_dict['vision_config']

         if "model_type" in config_dict and hasattr(cls			, 'model_type'   ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

         return cls.from_dict(lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="align"
      __a	       =True




      def __init__(	self			, lowerCamelCase=None			, lowerCamelCase=None			, lowerCamelCase=640			, lowerCamelCase=1.0			, lowerCamelCase=0.02			, **lowerCamelCase			, )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(**lowerCamelCase   )

         if text_config is None:
            __a					  =     {}
            logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

         if vision_config is None:
            __a					  =     {}
            logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

         __a					  =     AlignTextConfig(**lowerCamelCase   )
         __a					  =     AlignVisionConfig(**lowerCamelCase   )

         __a					  =     projection_dim
         __a					  =     temperature_init_value
         __a					  =     initializer_range




      @classmethod
      def 			__UpperCamelCase				(	cls			, lowerCamelCase			, lowerCamelCase			, **lowerCamelCase   )				->int:



         '''simple docstring'''
         return cls(text_config=text_config.to_dict()			, vision_config=vision_config.to_dict()			, **lowerCamelCase   )






      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     copy.deepcopy(self.__dict__   )
         __a					  =     self.text_config.to_dict()
         __a					  =     self.vision_config.to_dict()
         __a					  =     self.__class__.model_type
         return output