


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= "dpt"


     def __init__(						self ,						__lowerCamelCase=7_6_8 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=1_2 ,						__lowerCamelCase=3_0_7_2 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-12 ,						__lowerCamelCase=3_8_4 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=3 ,						__lowerCamelCase=False ,						__lowerCamelCase=True ,						__lowerCamelCase=[2, 5, 8, 1_1] ,						__lowerCamelCase="project" ,						__lowerCamelCase=[4, 2, 1, 0.5] ,						__lowerCamelCase=[9_6, 1_9_2, 3_8_4, 7_6_8] ,						__lowerCamelCase=2_5_6 ,						__lowerCamelCase=-1 ,						__lowerCamelCase=False ,						__lowerCamelCase=True ,						__lowerCamelCase=0.4 ,						__lowerCamelCase=2_5_5 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=[1, 1_0_2_4, 2_4, 2_4] ,						__lowerCamelCase=[0, 1] ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[Any]:
            super().__init__(**__lowerCamelCase)

            _A      :       Any				    = hidden_size
            _A      :       Dict				    = is_hybrid

            if self.is_hybrid:
                   if backbone_config is None:
                          logger.info("Initializing the config with a `BiT` backbone.")
                          _A      :       int				    = {
                              "global_padding": "same",
                              "layer_type": "bottleneck",
                              "depths": [3, 4, 9],
                              "out_features": ["stage1", "stage2", "stage3"],
                              "embedding_dynamic_padding": True,
                          }
                          _A      :       Dict				    = BitConfig(**__lowerCamelCase)
                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase):
                          logger.info("Initializing the config with a `BiT` backbone.")
                          _A      :       Optional[Any]				    = BitConfig(**__lowerCamelCase)
                   elif isinstance(__lowerCamelCase ,						__lowerCamelCase):
                          _A      :       List[str]				    = backbone_config
                   else:
                          raise ValueError(
                              F"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.")

                   _A      :       Any				    = backbone_featmap_shape
                   _A      :       Optional[Any]				    = neck_ignore_stages

                   if readout_type != "project":
                          raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode.")
            else:
                   _A      :       List[Any]				    = None
                   _A      :       List[str]				    = None
                   _A      :       int				    = []

            _A      :       Any				    = num_hidden_layers
            _A      :       List[str]				    = num_attention_heads
            _A      :       Any				    = intermediate_size
            _A      :       int				    = hidden_act
            _A      :       int				    = hidden_dropout_prob
            _A      :       Tuple				    = attention_probs_dropout_prob
            _A      :       str				    = initializer_range
            _A      :       Optional[Any]				    = layer_norm_eps
            _A      :       Optional[Any]				    = image_size
            _A      :       Any				    = patch_size
            _A      :       List[Any]				    = num_channels
            _A      :       Union[str, Any]				    = qkv_bias
            _A      :       List[Any]				    = backbone_out_indices
            if readout_type not in ["ignore", "add", "project"]:
                   raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']")
            _A      :       int				    = readout_type
            _A      :       Union[str, Any]				    = reassemble_factors
            _A      :       str				    = neck_hidden_sizes
            _A      :       Optional[Any]				    = fusion_hidden_size
            _A      :       Union[str, Any]				    = head_in_index
            _A      :       List[str]				    = use_batch_norm_in_fusion_residual
            # auxiliary head attributes (semantic segmentation)
            _A      :       str				    = use_auxiliary_head
            _A      :       Union[str, Any]				    = auxiliary_loss_weight
            _A      :       Any				    = semantic_loss_ignore_index
            _A      :       Any				    = semantic_classifier_dropout


     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       str				    = copy.deepcopy(self.__dict__)

            if output["backbone_config"] is not None:
                   _A      :       Optional[Any]				    = self.backbone_config.to_dict()

            _A      :       Optional[int]				    = self.__class__.model_type
            return output



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	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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

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

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

							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:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    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'''






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


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




