
'''simple docstring'''


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,
)


snake_case_ :  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  __snake_case     (    _UpperCAmelCase					:       Any):
   UpperCamelCase       =  {}
   state_dict.pop('''pixel_mean''',      _UpperCAmelCase)
   state_dict.pop('''pixel_std''',      _UpperCAmelCase)

   UpperCamelCase       =  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:
            UpperCamelCase       =  key.replace(_UpperCAmelCase,      _UpperCAmelCase)

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

      UpperCamelCase       =  value

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

   return model_state_dict



def  __snake_case     (    _UpperCAmelCase					:       Optional[Any],      _UpperCAmelCase					:       Dict,      _UpperCAmelCase					:       Tuple,      _UpperCAmelCase					:       Tuple="ybelkada/segment-anything"):
   UpperCamelCase       =  hf_hub_download(_UpperCAmelCase,      f'checkpoints/{model_name}.pth')

   if "sam_vit_b" in model_name:
      UpperCamelCase       =  SamConfig()
   elif "sam_vit_l" in model_name:
      UpperCamelCase       =  SamVisionConfig(
          hidden_size=1024,      num_hidden_layers=24,      num_attention_heads=16,      global_attn_indexes=[5, 11, 17, 23],      )

      UpperCamelCase       =  SamConfig(
          vision_config=_UpperCAmelCase,      )
   elif "sam_vit_h" in model_name:
      UpperCamelCase       =  SamVisionConfig(
          hidden_size=1280,      num_hidden_layers=32,      num_attention_heads=16,      global_attn_indexes=[7, 15, 23, 31],      )

      UpperCamelCase       =  SamConfig(
          vision_config=_UpperCAmelCase,      )

   UpperCamelCase       =  torch.load(_UpperCAmelCase,      map_location='''cpu''')
   UpperCamelCase       =  replace_keys(_UpperCAmelCase)

   UpperCamelCase       =  SamImageProcessor()

   UpperCamelCase       =  SamProcessor(image_processor=_UpperCAmelCase)
   UpperCamelCase       =  SamModel(_UpperCAmelCase)

   hf_model.load_state_dict(_UpperCAmelCase)
   UpperCamelCase       =  hf_model.to('''cuda''')

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

   UpperCamelCase       =  [[[400, 650]]]
   UpperCamelCase       =  [[1]]

   UpperCamelCase       =  processor(images=np.array(_UpperCAmelCase),      return_tensors='''pt''').to('''cuda''')

   with torch.no_grad():
      UpperCamelCase       =  hf_model(**_UpperCAmelCase)
   UpperCamelCase       =  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

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

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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

      UpperCamelCase       =  ((75, 275, 1725, 850),)

      UpperCamelCase       =  processor(images=np.array(_UpperCAmelCase),      input_boxes=_UpperCAmelCase,      return_tensors='''pt''').to('''cuda''')

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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.
      UpperCamelCase       =  [[[400, 650], [800, 650]]]
      UpperCamelCase       =  [[1, 1]]

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

      with torch.no_grad():
         UpperCamelCase       =  hf_model(**_UpperCAmelCase)
      UpperCamelCase       =  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__":
      snake_case_ :  Optional[Any]     =					argparse.ArgumentParser()
      snake_case_ :  Dict     =					["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',
      )

      snake_case_ :  int     =					parser.parse_args()

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







'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  []
   for data in source_data:
      for i, el in enumerate(_UpperCAmelCase):
         if len(_UpperCAmelCase) < i + 1:
            data_lists.append([])
         data_lists[i].append(float(_UpperCAmelCase))
   return data_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  []
   for dlist, weight in zip(_UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  min(_UpperCAmelCase)
      UpperCamelCase       =  max(_UpperCAmelCase)

      UpperCamelCase       =  []
      # for weight 0 score is 1 - actual score
      if weight == 0:
         for item in dlist:
            try:
               score.append(1 - ((item - mind) / (maxd - mind)))
            except ZeroDivisionError:
               score.append(1)

      elif weight == 1:
         for item in dlist:
            try:
               score.append((item - mind) / (maxd - mind))
            except ZeroDivisionError:
               score.append(0)

        # weight not 0 or 1
      else:
         UpperCamelCase       =  f'Invalid weight of {weight:f} provided'
         raise ValueError(_UpperCAmelCase)

      score_lists.append(_UpperCAmelCase)

   return score_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  [0 for i in range(len(score_lists[0]))]

   for slist in score_lists:
      for j, ele in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  final_scores[j] + ele

   return final_scores



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  get_data(_UpperCAmelCase)
   UpperCamelCase       =  calculate_each_score(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  generate_final_scores(_UpperCAmelCase)

   # append scores to source data
   for i, ele in enumerate(_UpperCAmelCase):
      source_data[i].append(_UpperCAmelCase)

   return source_data





