



'''simple docstring'''





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""detr"""
  a_				   =["""past_key_values"""]
  a_				   ={
      """hidden_size""": """d_model""",
      """num_attention_heads""": """encoder_attention_heads""",
  }




  def __init__(					self :      List[Any]     ,      _a :      List[str]=True     ,      _a :      Optional[Any]=None     ,      _a :      Tuple=3     ,      _a :      str=100     ,      _a :      Any=6     ,      _a :      List[str]=2048     ,      _a :      List[str]=8     ,      _a :      Tuple=6     ,      _a :      Union[str, Any]=2048     ,      _a :      Optional[Any]=8     ,      _a :      List[Any]=0.0     ,      _a :      List[str]=0.0     ,      _a :      Any=True     ,      _a :      Optional[int]="relu"     ,      _a :      Dict=256     ,      _a :      Union[str, Any]=0.1     ,      _a :      int=0.0     ,      _a :      Dict=0.0     ,      _a :      Tuple=0.02     ,      _a :      List[str]=1.0     ,      _a :      List[str]=False     ,      _a :      Dict="sine"     ,      _a :      Any="resnet50"     ,      _a :      Union[str, Any]=True     ,      _a :      List[Any]=False     ,      _a :      Any=1     ,      _a :      List[str]=5     ,      _a :      Optional[Any]=2     ,      _a :      str=1     ,      _a :      str=1     ,      _a :      Optional[int]=5     ,      _a :      Any=2     ,      _a :      Tuple=0.1     ,      **_a :      Union[str, Any]     ,      )   ->   Dict:
    if backbone_config is not None and use_timm_backbone:
      raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'  )

    if not use_timm_backbone:
      if backbone_config is None:
        logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'  )
        __lowerCamelCase			:			Optional[int]       			=					CONFIG_MAPPING['resnet'](out_features=['stage4']  )
      elif isinstance(_a     ,      _a  ):
        __lowerCamelCase			:			List[Any]       			=					backbone_config.get('model_type'  )
        __lowerCamelCase			:			Any       			=					CONFIG_MAPPING[backbone_model_type]
        __lowerCamelCase			:			Tuple       			=					config_class.from_dict(_a  )
      # set timm attributes to None
      __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					None, None, None

    __lowerCamelCase			:			Tuple       			=					use_timm_backbone
    __lowerCamelCase			:			Dict       			=					backbone_config
    __lowerCamelCase			:			List[Any]       			=					num_channels
    __lowerCamelCase			:			Optional[Any]       			=					num_queries
    __lowerCamelCase			:			List[str]       			=					d_model
    __lowerCamelCase			:			int       			=					encoder_ffn_dim
    __lowerCamelCase			:			int       			=					encoder_layers
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_attention_heads
    __lowerCamelCase			:			List[Any]       			=					decoder_ffn_dim
    __lowerCamelCase			:			Optional[int]       			=					decoder_layers
    __lowerCamelCase			:			Dict       			=					decoder_attention_heads
    __lowerCamelCase			:			Union[str, Any]       			=					dropout
    __lowerCamelCase			:			int       			=					attention_dropout
    __lowerCamelCase			:			List[Any]       			=					activation_dropout
    __lowerCamelCase			:			Union[str, Any]       			=					activation_function
    __lowerCamelCase			:			Optional[Any]       			=					init_std
    __lowerCamelCase			:			int       			=					init_xavier_std
    __lowerCamelCase			:			Union[str, Any]       			=					encoder_layerdrop
    __lowerCamelCase			:			Optional[int]       			=					decoder_layerdrop
    __lowerCamelCase			:			Optional[int]       			=					encoder_layers
    __lowerCamelCase			:			str       			=					auxiliary_loss
    __lowerCamelCase			:			str       			=					position_embedding_type
    __lowerCamelCase			:			int       			=					backbone
    __lowerCamelCase			:			Tuple       			=					use_pretrained_backbone
    __lowerCamelCase			:			str       			=					dilation
    # Hungarian matcher
    __lowerCamelCase			:			List[str]       			=					class_cost
    __lowerCamelCase			:			Optional[Any]       			=					bbox_cost
    __lowerCamelCase			:			str       			=					giou_cost
    # Loss coefficients
    __lowerCamelCase			:			Optional[int]       			=					mask_loss_coefficient
    __lowerCamelCase			:			List[Any]       			=					dice_loss_coefficient
    __lowerCamelCase			:			Optional[Any]       			=					bbox_loss_coefficient
    __lowerCamelCase			:			int       			=					giou_loss_coefficient
    __lowerCamelCase			:			str       			=					eos_coefficient
    super().__init__(is_encoder_decoder=_a     ,      **_a  )




  @property
  def 		_lowercase	(					self :      List[Any]  )   ->   int:
    return self.encoder_attention_heads




  @property
  def 		_lowercase	(					self :      List[str]  )   ->   int:
    return self.d_model




  @classmethod
  def 		_lowercase	(					cls :      str     ,      _a :      PretrainedConfig     ,      **_a :      List[str]  )   ->   Dict:
    return cls(backbone_config=_a     ,      **_a  )




  def 		_lowercase	(					self :      Any  )   ->   Dict[str, any]:
    __lowerCamelCase			:			str       			=					copy.deepcopy(self.__dict__  )
    if output["backbone_config"] is not None:
      __lowerCamelCase			:			Dict       			=					self.backbone_config.to_dict()
    __lowerCamelCase			:			List[Any]       			=					self.__class__.model_type
    return output






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =version.parse("""1.11"""	)




  @property
  def 		_lowercase	(					self :      List[Any]  )   ->   Mapping[str, Mapping[int, str]]:
    return OrderedDict(
        [
            ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ('pixel_mask', {0: 'batch'}),
        ]  )




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   float:
    return 1e-5




  @property
  def 		_lowercase	(					self :      int  )   ->   int:
    return 12











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

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

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

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  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:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			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_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


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






