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

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

			while True:
						lowerCAmelCase__      :      Dict         =		client.query(text=SCREAMING_SNAKE_CASE_     )
						if len(SCREAMING_SNAKE_CASE_     ) >= factor * num_class_images or num_images > 1e4:
									break
						else:
									lowerCAmelCase__      :      Dict         =		int(factor * num_images     )
									lowerCAmelCase__      :      Any         =		ClipClient(
									    url='https://knn.laion.ai/knn-service'       ,							indice_name='laion_400m'       ,							num_images=SCREAMING_SNAKE_CASE_       ,							aesthetic_weight=0.1       ,							)

			lowerCAmelCase__      :      Any         =		0
			lowerCAmelCase__      :      Optional[Any]         =		0
			lowerCAmelCase__      :      int         =		tqdm(desc='downloading real regularization images'       ,							total=SCREAMING_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:
									lowerCAmelCase__      :      List[Any]         =		class_images[count]
									count += 1
									try:
												lowerCAmelCase__      :      Optional[Any]         =		requests.get(images['url']     )
												if img.status_code == 200:
															lowerCAmelCase__      :      List[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  lowerCAmelCase__			(  )				->		str:
			lowerCAmelCase__      :      Optional[int]         =		argparse.ArgumentParser(''       ,							add_help=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--class_prompt'       ,							help='text prompt to retrieve images'       ,							required=SCREAMING_SNAKE_CASE_       ,							type=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--class_data_dir'       ,							help='path to save images'       ,							required=SCREAMING_SNAKE_CASE_       ,							type=SCREAMING_SNAKE_CASE_     )
			parser.add_argument('--num_class_images'       ,							help='number of images to download'       ,							default=200       ,							type=SCREAMING_SNAKE_CASE_     )
			return parser.parse_args()


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

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							super().__init__(*a    ,     **a   )
							requires_backends(self    ,     'vision'   )
							self.check_model_type(
							    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							    if self.framework == 'tf'
							    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






				def __call__(						self					:      Any    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							return super().__call__(a    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

							if self.framework == "pt":
										lowerCAmelCase__      :      List[Any]         =		model_outputs.logits.softmax(-1   )[0]
										lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		probs.topk(a   )
							elif self.framework == "tf":
										lowerCAmelCase__      :      Any         =		stable_softmax(model_outputs.logits    ,     axis=-1   )[0]
										lowerCAmelCase__      :      Any         =		tf.math.top_k(a    ,     k=a   )
										lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		topk.values.numpy(), topk.indices.numpy()
							else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''   )

							lowerCAmelCase__      :      List[Any]         =		scores.tolist()
							lowerCAmelCase__      :      List[str]         =		ids.tolist()
							return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a    ,     a   )]