



import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
a_								=       (720, 1_280)  # Height, Width
a_								=       (0.4, 0.6)  # if height or width lower than this scale, drop it.
a_								=       1 / 100
a_								=       """"""
a_								=       """"""
a_								=       """"""
a_								=       250





def 		a__      (		):
   __lowerCamelCase				,__lowerCamelCase    				=     get_dataset(_UpperCamelCase			,_UpperCamelCase				)
   for index in range(_UpperCamelCase				):
      __lowerCamelCase    				=     random.sample(range(len(_UpperCamelCase				)				)			,4				)
      __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     update_image_and_anno(
          _UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,_UpperCamelCase			,filter_scale=_UpperCamelCase			,)

      # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
      __lowerCamelCase    				=     random_chars(32				)
      __lowerCamelCase    				=     path.split(os.sep				)[-1].rsplit('''.'''			,1				)[0]
      __lowerCamelCase    				=     F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
      cva.imwrite(F"""{file_root}.jpg"""			,_UpperCamelCase			,[cva.IMWRITE_JPEG_QUALITY, 85]				)
      print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""				)
      __lowerCamelCase    				=     []
      for anno in new_annos:
         __lowerCamelCase    				=     anno[3] - anno[1]
         __lowerCamelCase    				=     anno[4] - anno[2]
         __lowerCamelCase    				=     anno[1] + width / 2
         __lowerCamelCase    				=     anno[2] + height / 2
         __lowerCamelCase    				=     F"""{anno[0]} {x_center} {y_center} {width} {height}"""
         annos_list.append(_UpperCamelCase				)
      with open(F"""{file_root}.txt"""			,'''w'''				) as outfile:
         outfile.write('''\n'''.join(line for line in annos_list				)				)





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     []
   __lowerCamelCase    				=     []
   for label_file in glob.glob(os.path.join(_UpperCamelCase			,'''*.txt'''				)				):
      __lowerCamelCase    				=     label_file.split(os.sep				)[-1].rsplit('''.'''			,1				)[0]
      with open(_UpperCamelCase				) as in_file:
         __lowerCamelCase    				=     in_file.readlines()
      __lowerCamelCase    				=     os.path.join(_UpperCamelCase			,F"""{label_name}.jpg"""				)

      __lowerCamelCase    				=     []
      for obj_list in obj_lists:
         __lowerCamelCase    				=     obj_list.rstrip('''\n'''				).split(''' '''				)
         __lowerCamelCase    				=     float(obj[1]				) - float(obj[3]				) / 2
         __lowerCamelCase    				=     float(obj[2]				) - float(obj[4]				) / 2
         __lowerCamelCase    				=     float(obj[1]				) + float(obj[3]				) / 2
         __lowerCamelCase    				=     float(obj[2]				) + float(obj[4]				) / 2

         boxes.append([int(obj[0]				), xmin, ymin, xmax, ymax]				)
      if not boxes:
         continue
      img_paths.append(_UpperCamelCase				)
      labels.append(_UpperCamelCase				)
   return img_paths, labels





def 		a__      (		_UpperCamelCase      :			list			,_UpperCamelCase      :			list			,_UpperCamelCase      :			list[int]			,_UpperCamelCase      :			tuple[int, int]			,_UpperCamelCase      :			tuple[float, float]			,_UpperCamelCase      :			float = 0.0			,):
   __lowerCamelCase    				=     np.zeros([output_size[0], output_size[1], 3]			,dtype=np.uinta				)
   __lowerCamelCase    				=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __lowerCamelCase    				=     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
   __lowerCamelCase    				=     int(scale_x * output_size[1]				)
   __lowerCamelCase    				=     int(scale_y * output_size[0]				)

   __lowerCamelCase    				=     []
   __lowerCamelCase    				=     []
   for i, index in enumerate(_UpperCamelCase				):
      __lowerCamelCase    				=     all_img_list[index]
      path_list.append(_UpperCamelCase				)
      __lowerCamelCase    				=     all_annos[index]
      __lowerCamelCase    				=     cva.imread(_UpperCamelCase				)
      if i == 0:  # top-left
         __lowerCamelCase    				=     cva.resize(_UpperCamelCase			,(divid_point_x, divid_point_y)				)
         __lowerCamelCase    				=     img
         for bbox in img_annos:
            __lowerCamelCase    				=     bbox[1] * scale_x
            __lowerCamelCase    				=     bbox[2] * scale_y
            __lowerCamelCase    				=     bbox[3] * scale_x
            __lowerCamelCase    				=     bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
      elif i == 1:  # top-right
         __lowerCamelCase    				=     cva.resize(_UpperCamelCase			,(output_size[1] - divid_point_x, divid_point_y)				)
         __lowerCamelCase    				=     img
         for bbox in img_annos:
            __lowerCamelCase    				=     scale_x + bbox[1] * (1 - scale_x)
            __lowerCamelCase    				=     bbox[2] * scale_y
            __lowerCamelCase    				=     scale_x + bbox[3] * (1 - scale_x)
            __lowerCamelCase    				=     bbox[4] * scale_y
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
      elif i == 2:  # bottom-left
         __lowerCamelCase    				=     cva.resize(_UpperCamelCase			,(divid_point_x, output_size[0] - divid_point_y)				)
         __lowerCamelCase    				=     img
         for bbox in img_annos:
            __lowerCamelCase    				=     bbox[1] * scale_x
            __lowerCamelCase    				=     scale_y + bbox[2] * (1 - scale_y)
            __lowerCamelCase    				=     bbox[3] * scale_x
            __lowerCamelCase    				=     scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)
      else:  # bottom-right
         __lowerCamelCase    				=     cva.resize(
             _UpperCamelCase			,(output_size[1] - divid_point_x, output_size[0] - divid_point_y)				)
         __lowerCamelCase    				=     img
         for bbox in img_annos:
            __lowerCamelCase    				=     scale_x + bbox[1] * (1 - scale_x)
            __lowerCamelCase    				=     scale_y + bbox[2] * (1 - scale_y)
            __lowerCamelCase    				=     scale_x + bbox[3] * (1 - scale_x)
            __lowerCamelCase    				=     scale_y + bbox[4] * (1 - scale_y)
            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]				)

    # Remove bounding box small than scale of filter
   if filter_scale > 0:
      __lowerCamelCase    				=     [
          anno
          for anno in new_anno
          if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
      ]

   return output_img, new_anno, path_list[0]





def 		a__      (		_UpperCamelCase      :			int				):
   assert number_char > 1, "The number of character should greater than 1"
   __lowerCamelCase    				=     ascii_lowercase + digits
   return "".join(random.choice(_UpperCamelCase				) for _ in range(_UpperCamelCase				)				)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")











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,
)


a_								=       {
    """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      :			Optional[Any]				):
   __lowerCamelCase    				=     {}
   state_dict.pop('''pixel_mean'''			,_UpperCamelCase				)
   state_dict.pop('''pixel_std'''			,_UpperCamelCase				)

   __lowerCamelCase    				=     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:
            __lowerCamelCase    				=     key.replace(_UpperCamelCase			,_UpperCamelCase				)

      if re.match(_UpperCamelCase			,_UpperCamelCase				):
         __lowerCamelCase    				=     int(re.match(_UpperCamelCase			,_UpperCamelCase				).group(2				)				)
         if layer_nb == 0:
            __lowerCamelCase    				=     key.replace('''layers.0'''			,'''proj_in'''				)
         elif layer_nb == 1:
            __lowerCamelCase    				=     key.replace('''layers.1'''			,'''layers.0'''				)
         elif layer_nb == 2:
            __lowerCamelCase    				=     key.replace('''layers.2'''			,'''proj_out'''				)

      __lowerCamelCase    				=     value

   __lowerCamelCase    				=     model_state_dict[
       '''prompt_encoder.shared_embedding.positional_embedding'''
   ]

   return model_state_dict





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Any			,_UpperCamelCase      :			Optional[int]="ybelkada/segment-anything"				):
   __lowerCamelCase    				=     hf_hub_download(_UpperCamelCase			,F"""checkpoints/{model_name}.pth"""				)

   if "sam_vit_b" in model_name:
      __lowerCamelCase    				=     SamConfig()
   elif "sam_vit_l" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=10_24			,num_hidden_layers=24			,num_attention_heads=16			,global_attn_indexes=[5, 11, 17, 23]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)
   elif "sam_vit_h" in model_name:
      __lowerCamelCase    				=     SamVisionConfig(
          hidden_size=12_80			,num_hidden_layers=32			,num_attention_heads=16			,global_attn_indexes=[7, 15, 23, 31]			,)

      __lowerCamelCase    				=     SamConfig(
          vision_config=_UpperCamelCase			,)

   __lowerCamelCase    				=     torch.load(_UpperCamelCase			,map_location='''cpu'''				)
   __lowerCamelCase    				=     replace_keys(_UpperCamelCase				)

   __lowerCamelCase    				=     SamImageProcessor()

   __lowerCamelCase    				=     SamProcessor(image_processor=_UpperCamelCase				)
   __lowerCamelCase    				=     SamModel(_UpperCamelCase				)

   hf_model.load_state_dict(_UpperCamelCase				)
   __lowerCamelCase    				=     hf_model.to('''cuda'''				)

   __lowerCamelCase    				=     '''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
   __lowerCamelCase    				=     Image.open(requests.get(_UpperCamelCase			,stream=_UpperCamelCase				).raw				).convert('''RGB'''				)

   __lowerCamelCase    				=     [[[4_00, 6_50]]]
   __lowerCamelCase    				=     [[1]]

   __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,return_tensors='''pt'''				).to('''cuda'''				)

   with torch.no_grad():
      __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
   __lowerCamelCase    				=     output.iou_scores.squeeze()

   if model_name == "sam_vit_h_4b8939":
      assert scores[-1].item() == 0.579_890_251_159_668

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_712_603_092_193_604

      __lowerCamelCase    				=     ((75, 2_75, 17_25, 8_50),)

      __lowerCamelCase    				=     processor(images=np.array(_UpperCamelCase				)			,input_boxes=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.8_686_015_605_926_514

      # Test with 2 points and 1 image.
      __lowerCamelCase    				=     [[[4_00, 6_50], [8_00, 6_50]]]
      __lowerCamelCase    				=     [[1, 1]]

      __lowerCamelCase    				=     processor(
          images=np.array(_UpperCamelCase				)			,input_points=_UpperCamelCase			,input_labels=_UpperCamelCase			,return_tensors='''pt'''				).to('''cuda'''				)

      with torch.no_grad():
         __lowerCamelCase    				=     hf_model(**_UpperCamelCase				)
      __lowerCamelCase    				=     output.iou_scores.squeeze()

      assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
  a_								=       argparse.ArgumentParser()
  a_								=       ["""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""",
  )

  a_								=       parser.parse_args()

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






