
'''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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



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

    A       :   Dict					=						0
    A       :   Optional[int]					=						0
    A       :   Tuple					=						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:
            A       :   Optional[Any]					=						class_images[count]
            count += 1
            try:
                A       :   Dict					=						requests.get(images['''url''']						)
                if img.status_code == 200:
                    A       :   Tuple					=						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'''



    A       :   int					=						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			:				Optional[int]    		=			parse_args()
      retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)







from collections import OrderedDict
from typing import 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 			=		{
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """table-transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self      ,							A_=True      ,							A_=None      ,							A_=3      ,							A_=100      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=6      ,							A_=2048      ,							A_=8      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_="relu"      ,							A_=256      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=1.0      ,							A_=False      ,							A_="sine"      ,							A_="resnet50"      ,							A_=True      ,							A_=False      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=1      ,							A_=1      ,							A_=5      ,							A_=2      ,							A_=0.1      ,							**A_      ,							)					->Any:



       '''simple docstring'''





       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[Any]					=					CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
             elif isinstance(A_      ,							A_					):
                   __lowerCAmelCase					:						int					=					backbone_config.get('''model_type'''					)
                   __lowerCAmelCase					:						List[str]					=					CONFIG_MAPPING[backbone_model_type]
                   __lowerCAmelCase					:						Any					=					config_class.from_dict(A_					)
             # set timm attributes to None
             __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						List[str]					=					None, None, None

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



       '''simple docstring'''





       return self.encoder_attention_heads





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return self.d_model







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  version.parse("""1.11""" )
 @property
 def 	UpperCamelCase__  (							self					)					->Mapping[str, Mapping[int, str]]:



       '''simple docstring'''





       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)
 @property
 def 	UpperCamelCase__  (							self					)					->float:



       '''simple docstring'''





       return 1e-5





 @property
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       return 12


