


"""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          =							{
    "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						(				_a:     int							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		{}
      state_dict.pop('pixel_mean'		,   _a							)
      state_dict.pop('pixel_std'		,   _a							)

      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(_a		,   _a							)

            if re.match(_a		,   _a							):
                  lowerCamelCase__           =		int(re.match(_a		,   _a							).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   snake_case						(				_a:     Dict		,   _a:     Optional[Any]		,   _a:     Union[str, Any]		,   _a:     Optional[Any]="ybelkada/segment-anything"							)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		hf_hub_download(_a		,   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=1024		,   num_hidden_layers=24		,   num_attention_heads=16		,   global_attn_indexes=[5, 11, 17, 23]		,   )

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

            lowerCamelCase__           =		SamConfig(
                vision_config=_a		,   )

      lowerCamelCase__           =		torch.load(_a		,   map_location='cpu'							)
      lowerCamelCase__           =		replace_keys(_a							)

      lowerCamelCase__           =		SamImageProcessor()

      lowerCamelCase__           =		SamProcessor(image_processor=_a							)
      lowerCamelCase__           =		SamModel(_a							)

      hf_model.load_state_dict(_a							)
      lowerCamelCase__           =		hf_model.to('cuda'							)

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

      lowerCamelCase__           =		[[[400, 650]]]
      lowerCamelCase__           =		[[1]]

      lowerCamelCase__           =		processor(images=np.array(_a							)		,   return_tensors='pt'							).to('cuda'							)

      with torch.no_grad():
            lowerCamelCase__           =		hf_model(**_a							)
      lowerCamelCase__           =		output.iou_scores.squeeze()

      if model_name == "sam_vit_h_4b8939":
            assert scores[-1].item() == 0.579_8902_5115_9668

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

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9712_6030_9219_3604

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

            lowerCamelCase__           =		processor(images=np.array(_a							)		,   input_boxes=_a		,   return_tensors='pt'							).to('cuda'							)

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.8686_0156_0592_6514

            # Test with 2 points and 1 image.
            lowerCamelCase__           =		[[[400, 650], [800, 650]]]
            lowerCamelCase__           =		[[1, 1]]

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

            with torch.no_grad():
                  lowerCamelCase__           =		hf_model(**_a							)
            lowerCamelCase__           =		output.iou_scores.squeeze()

            assert scores[-1].item() == 0.9936_0477_9243_4692


if __name__ == "__main__":
   _snake_case          =							argparse.ArgumentParser()
   _snake_case          =							["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          =							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						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations(_a:     int							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            return sum(count_of_possible_combinations(target - item							) for item in array							)

      return count_of_possible_combinations(_a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations_with_dp_array(
          _a:     int		,   _a:     list[int]							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            if dp_array[target] != -1:
                  return dp_array[target]
            lowerCamelCase__           =		sum(
                count_of_possible_combinations_with_dp_array(target - item		,   _a							)
                for item in array							)
            lowerCamelCase__           =		answer
            return answer

      lowerCamelCase__           =		[-1] * (target + 1)
      return count_of_possible_combinations_with_dp_array(_a		,   _a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0] * (target + 1)
      lowerCamelCase__           =		1

      for i in range(1		,   target + 1							):
            for j in range(_a							):
                  if i - array[j] >= 0:
                        dp_array[i] += dp_array[i - array[j]]

      return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case          =							3
   _snake_case          =							5
   _snake_case          =							[1, 2, 5]
   print(combination_sum_iv(n, array, target))



