





import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


__lowerCAmelCase :Optional[Any]     =    {
    'iou_prediction_head.layers.0': 'iou_prediction_head.proj_in',
    'iou_prediction_head.layers.1': 'iou_prediction_head.layers.0',
    'iou_prediction_head.layers.2': 'iou_prediction_head.proj_out',
    'mask_decoder.output_upscaling.0': 'mask_decoder.upscale_conv1',
    'mask_decoder.output_upscaling.1': 'mask_decoder.upscale_layer_norm',
    'mask_decoder.output_upscaling.3': 'mask_decoder.upscale_conv2',
    'mask_downscaling.0': 'mask_embed.conv1',
    'mask_downscaling.1': 'mask_embed.layer_norm1',
    'mask_downscaling.3': 'mask_embed.conv2',
    'mask_downscaling.4': 'mask_embed.layer_norm2',
    'mask_downscaling.6': 'mask_embed.conv3',
    'point_embeddings': 'point_embed',
    'pe_layer.positional_encoding_gaussian_matrix': 'shared_embedding.positional_embedding',
    'image_encoder': 'vision_encoder',
    'neck.0': 'neck.conv1',
    'neck.1': 'neck.layer_norm1',
    'neck.2': 'neck.conv2',
    'neck.3': 'neck.layer_norm2',
    'patch_embed.proj': 'patch_embed.projection',
    '.norm': '.layer_norm',
    'blocks': 'layers',
}







def    A				(							UpperCAmelCase				):
					_snake_case       :					Union[str, Any]				       =   {}
					state_dict.pop("pixel_mean"  ,  UpperCAmelCase				)
					state_dict.pop("pixel_std"  ,  UpperCAmelCase				)

					_snake_case       :					List[str]				       =   R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

					for key, value in state_dict.items():
										for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
															if key_to_modify in key:
																				_snake_case       :					Union[str, Any]				       =   key.replace(UpperCAmelCase  ,  UpperCAmelCase				)

										if re.match(UpperCAmelCase  ,  UpperCAmelCase				):
															_snake_case       :					int				       =   int(re.match(UpperCAmelCase  ,  UpperCAmelCase				).group(2				)				)
															if layer_nb == 0:
																				_snake_case       :					Tuple				       =   key.replace("layers.0"  ,  "proj_in"				)
															elif layer_nb == 1:
																				_snake_case       :					Tuple				       =   key.replace("layers.1"  ,  "layers.0"				)
															elif layer_nb == 2:
																				_snake_case       :					Optional[int]				       =   key.replace("layers.2"  ,  "proj_out"				)

										_snake_case       :					List[str]				       =   value

					_snake_case       :					Tuple				       =   model_state_dict[
					    "prompt_encoder.shared_embedding.positional_embedding"
					]

					return model_state_dict







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase="ybelkada/segment-anything"				):
					_snake_case       :					Union[str, Any]				       =   hf_hub_download(UpperCAmelCase  ,  F"""checkpoints/{model_name}.pth"""				)

					if "sam_vit_b" in model_name:
										_snake_case       :					Any				       =   SamConfig()
					elif "sam_vit_l" in model_name:
										_snake_case       :					Optional[int]				       =   SamVisionConfig(
										    hidden_size=1_024  ,  num_hidden_layers=24  ,  num_attention_heads=16  ,  global_attn_indexes=[5, 11, 17, 23]  ,  )

										_snake_case       :					List[Any]				       =   SamConfig(
										    vision_config=UpperCAmelCase  ,  )
					elif "sam_vit_h" in model_name:
										_snake_case       :					Tuple				       =   SamVisionConfig(
										    hidden_size=1_280  ,  num_hidden_layers=32  ,  num_attention_heads=16  ,  global_attn_indexes=[7, 15, 23, 31]  ,  )

										_snake_case       :					int				       =   SamConfig(
										    vision_config=UpperCAmelCase  ,  )

					_snake_case       :					str				       =   torch.load(UpperCAmelCase  ,  map_location="cpu"				)
					_snake_case       :					Union[str, Any]				       =   replace_keys(UpperCAmelCase				)

					_snake_case       :					Optional[int]				       =   SamImageProcessor()

					_snake_case       :					List[Any]				       =   SamProcessor(image_processor=UpperCAmelCase				)
					_snake_case       :					Optional[int]				       =   SamModel(UpperCAmelCase				)

					hf_model.load_state_dict(UpperCAmelCase				)
					_snake_case       :					Union[str, Any]				       =   hf_model.to("cuda"				)

					_snake_case       :					Union[str, Any]				       =   "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
					_snake_case       :					Union[str, Any]				       =   Image.open(requests.get(UpperCAmelCase  ,  stream=UpperCAmelCase				).raw				).convert("RGB"				)

					_snake_case       :					Optional[int]				       =   [[[400, 650]]]
					_snake_case       :					Optional[Any]				       =   [[1]]

					_snake_case       :					Tuple				       =   processor(images=np.array(UpperCAmelCase				)  ,  return_tensors="pt"				).to("cuda"				)

					with torch.no_grad():
										_snake_case       :					str				       =   hf_model(**UpperCAmelCase				)
					_snake_case       :					str				       =   output.iou_scores.squeeze()

					if model_name == "sam_vit_h_4b8939":
										assert scores[-1].item() == 0.5_7_9_8_9_0_2_5_1_1_5_9_6_6_8

										_snake_case       :					str				       =   processor(
										    images=np.array(UpperCAmelCase				)  ,  input_points=UpperCAmelCase  ,  input_labels=UpperCAmelCase  ,  return_tensors="pt"				).to("cuda"				)

										with torch.no_grad():
															_snake_case       :					List[str]				       =   hf_model(**UpperCAmelCase				)
										_snake_case       :					Optional[int]				       =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9_7_1_2_6_0_3_0_9_2_1_9_3_6_0_4

										_snake_case       :					Optional[int]				       =   ((75, 275, 1_725, 850),)

										_snake_case       :					List[str]				       =   processor(images=np.array(UpperCAmelCase				)  ,  input_boxes=UpperCAmelCase  ,  return_tensors="pt"				).to("cuda"				)

										with torch.no_grad():
															_snake_case       :					Any				       =   hf_model(**UpperCAmelCase				)
										_snake_case       :					Any				       =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.8_6_8_6_0_1_5_6_0_5_9_2_6_5_1_4

										# Test with 2 points and 1 image.
										_snake_case       :					str				       =   [[[400, 650], [800, 650]]]
										_snake_case       :					Optional[int]				       =   [[1, 1]]

										_snake_case       :					Any				       =   processor(
										    images=np.array(UpperCAmelCase				)  ,  input_points=UpperCAmelCase  ,  input_labels=UpperCAmelCase  ,  return_tensors="pt"				).to("cuda"				)

										with torch.no_grad():
															_snake_case       :					Dict				       =   hf_model(**UpperCAmelCase				)
										_snake_case       :					Optional[int]				       =   output.iou_scores.squeeze()

										assert scores[-1].item() == 0.9_9_3_6_0_4_7_7_9_2_4_3_4_6_9_2


if __name__ == "__main__":
							__lowerCAmelCase :Optional[int]     =    argparse.ArgumentParser()
							__lowerCAmelCase :int     =    ['sam_vit_b_01ec64', 'sam_vit_h_4b8939', 'sam_vit_l_0b3195']
							parser.add_argument(
							    '--model_name',
							    default='sam_vit_h_4b8939',
							    choices=choices,
							    type=str,
							    help='Path to hf config.json of model to convert',
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether to push the model and processor to the hub after converting',
							)
							parser.add_argument(
							    '--model_hub_id',
							    default='ybelkada/segment-anything',
							    choices=choices,
							    type=str,
							    help='Path to hf config.json of model to convert',
							)

							__lowerCAmelCase :Union[str, Any]     =    parser.parse_args()

							convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)






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

					_snake_case       :					List[Any]				       =   0
					_snake_case       :					Any				       =   0
					_snake_case       :					List[Any]				       =   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       :					Optional[Any]				       =   class_images[count]
															count += 1
															try:
																				_snake_case       :					Tuple				       =   requests.get(images["url"]				)
																				if img.status_code == 200:
																									_snake_case       :					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    A				(							):
					_snake_case       :					Any				       =   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__":
							__lowerCAmelCase :List[Any]     =    parse_args()
							retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)