
'''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,
)


_lowercase :  List[str]    							=       {
    """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_		(				__SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						lowercase_	:     str													=       {}
						state_dict.pop('''pixel_mean'''      ,      A__							)
						state_dict.pop('''pixel_std'''      ,      A__							)

						lowercase_	:     Any													=       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:
																								lowercase_	:     str													=       key.replace(A__      ,      A__							)

												if re.match(A__      ,      A__							):
																		lowercase_	:     Any													=       int(re.match(A__      ,      A__							).group(2							)							)
																		if layer_nb == 0:
																								lowercase_	:     List[str]													=       key.replace('''layers.0'''      ,      '''proj_in'''							)
																		elif layer_nb == 1:
																								lowercase_	:     Optional[int]													=       key.replace('''layers.1'''      ,      '''layers.0'''							)
																		elif layer_nb == 2:
																								lowercase_	:     List[str]													=       key.replace('''layers.2'''      ,      '''proj_out'''							)

												lowercase_	:     Optional[Any]													=       value

						lowercase_	:     List[Any]													=       model_state_dict[
						    'prompt_encoder.shared_embedding.positional_embedding'
						]

						return model_state_dict
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: int="ybelkada/segment-anything"							):



						"""simple docstring"""





						lowercase_	:     int													=       hf_hub_download(A__      ,      F'''checkpoints/{model_name}.pth'''							)

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

												lowercase_	:     List[str]													=       SamConfig(
												    vision_config=A__      ,      )
						elif "sam_vit_h" in model_name:
												lowercase_	:     Tuple													=       SamVisionConfig(
												    hidden_size=1280      ,      num_hidden_layers=32      ,      num_attention_heads=16      ,      global_attn_indexes=[7, 15, 23, 31]      ,      )

												lowercase_	:     Optional[int]													=       SamConfig(
												    vision_config=A__      ,      )

						lowercase_	:     Tuple													=       torch.load(A__      ,      map_location='''cpu'''							)
						lowercase_	:     Optional[int]													=       replace_keys(A__							)

						lowercase_	:     Union[str, Any]													=       SamImageProcessor()

						lowercase_	:     Optional[Any]													=       SamProcessor(image_processor=A__							)
						lowercase_	:     List[Any]													=       SamModel(A__							)

						hf_model.load_state_dict(A__							)
						lowercase_	:     Optional[Any]													=       hf_model.to('''cuda'''							)

						lowercase_	:     List[Any]													=       'https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'
						lowercase_	:     str													=       Image.open(requests.get(A__      ,      stream=A__							).raw							).convert('''RGB'''							)

						lowercase_	:     int													=       [[[400, 650]]]
						lowercase_	:     List[str]													=       [[1]]

						lowercase_	:     str													=       processor(images=np.array(A__							)      ,      return_tensors='''pt'''							).to('''cuda'''							)

						with torch.no_grad():
												lowercase_	:     Union[str, Any]													=       hf_model(**A__							)
						lowercase_	:     Any													=       output.iou_scores.squeeze()

						if model_name == "sam_vit_h_4b8939":
												assert scores[-1].item() == 0.579890251159668

												lowercase_	:     Any													=       processor(
												    images=np.array(A__							)      ,      input_points=A__      ,      input_labels=A__      ,      return_tensors='''pt'''							).to('''cuda'''							)

												with torch.no_grad():
																		lowercase_	:     Union[str, Any]													=       hf_model(**A__							)
												lowercase_	:     Dict													=       output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9712603092193604

												lowercase_	:     str													=       ((75, 275, 1725, 850),)

												lowercase_	:     Union[str, Any]													=       processor(images=np.array(A__							)      ,      input_boxes=A__      ,      return_tensors='''pt'''							).to('''cuda'''							)

												with torch.no_grad():
																		lowercase_	:     List[Any]													=       hf_model(**A__							)
												lowercase_	:     Any													=       output.iou_scores.squeeze()

												assert scores[-1].item() == 0.8686015605926514

												# Test with 2 points and 1 image.
												lowercase_	:     Dict													=       [[[400, 650], [800, 650]]]
												lowercase_	:     List[str]													=       [[1, 1]]

												lowercase_	:     Any													=       processor(
												    images=np.array(A__							)      ,      input_points=A__      ,      input_labels=A__      ,      return_tensors='''pt'''							).to('''cuda'''							)

												with torch.no_grad():
																		lowercase_	:     Optional[Any]													=       hf_model(**A__							)
												lowercase_	:     str													=       output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9936047792434692


if __name__ == "__main__":
							_lowercase :  Dict    							=       argparse.ArgumentParser()
							_lowercase :  List[str]    							=       ["""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",
							)

							_lowercase :  List[Any]    							=       parser.parse_args()

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










def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


if __name__ == "__main__":
    lowercase      :       Dict									=  input("""Enter numbers separated by a comma:\n""").strip()
    lowercase      :       Dict									=  [int(item) for item in user_input.split(""",""")]
    print(stooge_sort(unsorted))


