






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    (      a :Optional[Any]	,							a :Tuple	,							a :Dict )							->			str:
 a		  =      1.5
 a		  =      int(factor * num_class_images )
 a		  =      ClipClient(
     url='''https://knn.laion.ai/knn-service'''	,							indice_name='''laion_400m'''	,							num_images=a	,							aesthetic_weight=0.1 )

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

 while True:
  a		  =      client.query(text=a )
  if len(a ) >= 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=a	,							aesthetic_weight=0.1	,							)

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

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


if __name__ == "__main__":
     UpperCAmelCase__				   =		parse_args()
     retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)










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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''llama'''
      __snake_case       =  ['''past_key_values''']







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						Union[str, Any]=32_000 ,					__UpperCAmelCase      :						str=4_096 ,					__UpperCAmelCase      :						int=11_008 ,					__UpperCAmelCase      :						Tuple=32 ,					__UpperCAmelCase      :						Optional[int]=32 ,					__UpperCAmelCase      :						List[str]=None ,					__UpperCAmelCase      :						Union[str, Any]="silu" ,					__UpperCAmelCase      :						Tuple=2_048 ,					__UpperCAmelCase      :						Optional[Any]=0.02 ,					__UpperCAmelCase      :						Any=1e-6 ,					__UpperCAmelCase      :						Union[str, Any]=True ,					__UpperCAmelCase      :						Optional[int]=0 ,					__UpperCAmelCase      :						Optional[int]=1 ,					__UpperCAmelCase      :						Optional[int]=2 ,					__UpperCAmelCase      :						Tuple=1 ,					__UpperCAmelCase      :						List[str]=False ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Tuple ,					)    ->str:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      max_position_embeddings
       a		  =      hidden_size
       a		  =      intermediate_size
       a		  =      num_hidden_layers
       a		  =      num_attention_heads

       # for backward compatibility
       if num_key_value_heads is None:
        a		  =      num_attention_heads

       a		  =      num_key_value_heads
       a		  =      hidden_act
       a		  =      initializer_range
       a		  =      rms_norm_eps
       a		  =      pretraining_tp
       a		  =      use_cache
       a		  =      rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=__UpperCAmelCase ,					bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					tie_word_embeddings=__UpperCAmelCase ,					**__UpperCAmelCase ,					)







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Tuple:




       """simple docstring"""


       if self.rope_scaling is None:
        return

       if not isinstance(self.rope_scaling ,					__UpperCAmelCase  ) or len(self.rope_scaling  ) != 2:
        raise ValueError(
            '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
            F"""got {self.rope_scaling}"""  )
       a		  =      self.rope_scaling.get('''type''' ,					__UpperCAmelCase  )
       a		  =      self.rope_scaling.get('''factor''' ,					__UpperCAmelCase  )
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"""  )
       if rope_scaling_factor is None or not isinstance(__UpperCAmelCase ,					__UpperCAmelCase  ) or rope_scaling_factor <= 1.0:
        raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"""  )


