





"""simple docstring"""




import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType

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


a		:				Dict  			=    logging.get_logger(__name__)

a		:				int  			=    {
    "google/owlvit-base-patch32": "https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json",
    "google/owlvit-base-patch16": "https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json",
    "google/owlvit-large-patch14": "https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json",
}


class  __UpperCamelCase       (		UpperCAmelCase_		):
							lowerCamelCase     :      Union[str, Any]         ="""owlvit_text_model"""






							def __init__(     self   ,      lowerCAmelCase__=4_9408   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=2048   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=8   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__="quick_gelu"   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1.0   ,      lowerCAmelCase__=0   ,      lowerCAmelCase__=4_9406   ,      lowerCAmelCase__=4_9407   ,      **lowerCAmelCase__   ,      )			->				Tuple:
								super().__init__(pad_token_id=_lowercase   ,      bos_token_id=_lowercase   ,      eos_token_id=_lowercase   ,      **_lowercase   )

								a   :	Optional[Any]			=       vocab_size
								a   :	List[str]			=       hidden_size
								a   :	Any			=       intermediate_size
								a   :	List[Any]			=       num_hidden_layers
								a   :	Optional[Any]			=       num_attention_heads
								a   :	str			=       max_position_embeddings
								a   :	str			=       hidden_act
								a   :	Dict			=       layer_norm_eps
								a   :	Optional[Any]			=       attention_dropout
								a   :	Optional[Any]			=       initializer_range
								a   :	List[Any]			=       initializer_factor






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Dict:
								cls._set_token_in_kwargs(_lowercase   )

								a, a   :	int			=       cls.get_config_dict(_lowercase   ,      **_lowercase   )

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get("model_type"   ) == "owlvit":
									a   :	Optional[int]			=       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(_lowercase   ,      **_lowercase   )


class  __UpperCamelCase       (		UpperCAmelCase_		):
							lowerCamelCase     :      Union[str, Any]         ="""owlvit_vision_model"""






							def __init__(     self   ,      lowerCAmelCase__=768   ,      lowerCAmelCase__=3072   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=12   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=768   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__="quick_gelu"   ,      lowerCAmelCase__=1E-5   ,      lowerCAmelCase__=0.0   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=1.0   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								super().__init__(**_lowercase   )

								a   :	Any			=       hidden_size
								a   :	Dict			=       intermediate_size
								a   :	Union[str, Any]			=       num_hidden_layers
								a   :	Optional[int]			=       num_attention_heads
								a   :	Dict			=       num_channels
								a   :	Dict			=       image_size
								a   :	List[Any]			=       patch_size
								a   :	Optional[Any]			=       hidden_act
								a   :	str			=       layer_norm_eps
								a   :	str			=       attention_dropout
								a   :	List[Any]			=       initializer_range
								a   :	List[Any]			=       initializer_factor






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Dict:
								cls._set_token_in_kwargs(_lowercase   )

								a, a   :	str			=       cls.get_config_dict(_lowercase   ,      **_lowercase   )

								# get the vision config dict if we are loading from OwlViTConfig
								if config_dict.get("model_type"   ) == "owlvit":
									a   :	Tuple			=       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(_lowercase   ,      **_lowercase   )


class  __UpperCamelCase       (		UpperCAmelCase_		):
							lowerCamelCase     :      str         ="""owlvit"""
							lowerCamelCase     :      Dict         =True






							def __init__(     self   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=None   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=2.6_592   ,      lowerCAmelCase__=True   ,      **lowerCAmelCase__   ,      )			->				List[str]:
								super().__init__(**_lowercase   )

								if text_config is None:
									a   :	List[str]			=       {}
									logger.info("text_config is None. Initializing the OwlViTTextConfig with default values."   )

								if vision_config is None:
									a   :	int			=       {}
									logger.info("vision_config is None. initializing the OwlViTVisionConfig with default values."   )

								a   :	List[Any]			=       OwlViTTextConfig(**_lowercase   )
								a   :	Union[str, Any]			=       OwlViTVisionConfig(**_lowercase   )

								a   :	Optional[Any]			=       projection_dim
								a   :	Union[str, Any]			=       logit_scale_init_value
								a   :	Any			=       return_dict
								a   :	Optional[int]			=       1.0






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Dict:
								cls._set_token_in_kwargs(_lowercase   )

								a, a   :	str			=       cls.get_config_dict(_lowercase   ,      **_lowercase   )

								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(_lowercase   ,      **_lowercase   )






							@classmethod
							def 					__a     (     cls   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      **lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	List[str]			=       {}
								a   :	Union[str, Any]			=       text_config
								a   :	int			=       vision_config

								return cls.from_dict(_lowercase   ,      **_lowercase   )






							def 					__a     (     self   )			->				Tuple:
								a   :	Optional[Any]			=       copy.deepcopy(self.__dict__   )
								a   :	Dict			=       self.text_config.to_dict()
								a   :	Any			=       self.vision_config.to_dict()
								a   :	Dict			=       self.__class__.model_type
								return output


class  __UpperCamelCase       (		UpperCAmelCase_		):






							@property
							def 					__a     (     self   )			->				Any:
								return OrderedDict(
								    [
								        ("input_ids", {0: "batch", 1: "sequence"}),
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								        ("attention_mask", {0: "batch", 1: "sequence"}),
								    ]   )






							@property
							def 					__a     (     self   )			->				Tuple:
								return OrderedDict(
								    [
								        ("logits_per_image", {0: "batch"}),
								        ("logits_per_text", {0: "batch"}),
								        ("text_embeds", {0: "batch"}),
								        ("image_embeds", {0: "batch"}),
								    ]   )






							@property
							def 					__a     (     self   )			->				Tuple:
								return 1E-4






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = -1   ,      lowerCAmelCase__ = -1   ,      lowerCAmelCase__ = None   ,      )			->				Tuple:
								a   :	Any			=       super().generate_dummy_inputs(
								    processor.tokenizer   ,      batch_size=_lowercase   ,      seq_length=_lowercase   ,      framework=_lowercase   )
								a   :	List[Any]			=       super().generate_dummy_inputs(
								    processor.image_processor   ,      batch_size=_lowercase   ,      framework=_lowercase   )
								return {**text_input_dict, **image_input_dict}






							@property
							def 					__a     (     self   )			->				Dict:
								return 14









"""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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					List[str]					, _lowercase		:					Optional[Any]      )				->str:






	'''simple docstring'''






	a   :	Union[str, Any]			=       1.5
	a   :	List[str]			=       int(factor * num_class_images      )
	a   :	Optional[Any]			=       ClipClient(
	    url="https://knn.laion.ai/knn-service"					, indice_name="laion_400m"					, num_images=_lowercase					, aesthetic_weight=0.1      )

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

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

	a   :	Optional[int]			=       0
	a   :	str			=       0
	a   :	Any			=       tqdm(desc="downloading real regularization images"					, total=_lowercase      )

	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   :	str			=       requests.get(images["url"]      )
				if img.status_code == 200:
					a   :	int			=       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    _SCREAMING_SNAKE_CASE	(			)				->Dict:






	'''simple docstring'''






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


if __name__ == "__main__":
		a		:				List[Any]  			=    parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


