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	(    _lowercase		,  _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    List[str]							=							1.5
						SCREAMING_SNAKE_CASE							:    List[Any]							=							int(factor * num_class_images						)
						SCREAMING_SNAKE_CASE							:    Dict							=							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:
												SCREAMING_SNAKE_CASE							:    Dict							=							client.query(text=_lowercase						)
												if len(_lowercase						) >= factor * num_class_images or num_images > 1e4:
																		break
												else:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							int(factor * num_images						)
																		SCREAMING_SNAKE_CASE							:    Any							=							ClipClient(
																		    url='''https://knn.laion.ai/knn-service'''		,  indice_name='''laion_400m'''		,  num_images=_lowercase		,  aesthetic_weight=0.1		,  )

						SCREAMING_SNAKE_CASE							:    int							=							0
						SCREAMING_SNAKE_CASE							:    str							=							0
						SCREAMING_SNAKE_CASE							:    Union[str, 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:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							class_images[count]
																		count += 1
																		try:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							requests.get(images['''url''']						)
																								if img.status_code == 200:
																														SCREAMING_SNAKE_CASE							:    Dict							=							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	(    ):
						SCREAMING_SNAKE_CASE							:    List[str]							=							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__":
					__UpperCamelCase		:       int									=     parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCamelCase		:       List[Any]									=     {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Optional[Any]									=     ['GLPNFeatureExtractor']
					__UpperCamelCase		:       List[str]									=     ['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase		:       Optional[Any]									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





