




'''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,
)


__lowerCAmelCase							:     Dict		     ={
    """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 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      {}
    state_dict.pop("pixel_mean"						,	lowerCAmelCase__	)
    state_dict.pop("pixel_std"						,	lowerCAmelCase__	)

    A__    =      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:
                A__    =      key.replace(lowerCAmelCase__						,	lowerCAmelCase__	)

        if re.match(lowerCAmelCase__						,	lowerCAmelCase__	):
            A__    =      int(re.match(lowerCAmelCase__						,	lowerCAmelCase__	).group(2	)	)
            if layer_nb == 0:
                A__    =      key.replace("layers.0"						,	"proj_in"	)
            elif layer_nb == 1:
                A__    =      key.replace("layers.1"						,	"layers.0"	)
            elif layer_nb == 2:
                A__    =      key.replace("layers.2"						,	"proj_out"	)

        A__    =      value

    A__    =      model_state_dict[
        "prompt_encoder.shared_embedding.positional_embedding"
    ]

    return model_state_dict





def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : int						,	_lowerCamelCase : Any						,	_lowerCamelCase : Optional[int]="ybelkada/segment-anything"	):
    A__    =      hf_hub_download(lowerCAmelCase__						,	F"checkpoints/{model_name}.pth"	)

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

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

        A__    =      SamConfig(
            vision_config=lowerCAmelCase__						,	)

    A__    =      torch.load(lowerCAmelCase__						,	map_location="cpu"	)
    A__    =      replace_keys(lowerCAmelCase__	)

    A__    =      SamImageProcessor()

    A__    =      SamProcessor(image_processor=lowerCAmelCase__	)
    A__    =      SamModel(lowerCAmelCase__	)

    hf_model.load_state_dict(lowerCAmelCase__	)
    A__    =      hf_model.to("cuda"	)

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

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

    A__    =      processor(images=np.array(lowerCAmelCase__	)						,	return_tensors="pt"	).to("cuda"	)

    with torch.no_grad():
        A__    =      hf_model(**lowerCAmelCase__	)
    A__    =      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

        A__    =      processor(
            images=np.array(lowerCAmelCase__	)						,	input_points=lowerCAmelCase__						,	input_labels=lowerCAmelCase__						,	return_tensors="pt"	).to("cuda"	)

        with torch.no_grad():
            A__    =      hf_model(**lowerCAmelCase__	)
        A__    =      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

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

        A__    =      processor(images=np.array(lowerCAmelCase__	)						,	input_boxes=lowerCAmelCase__						,	return_tensors="pt"	).to("cuda"	)

        with torch.no_grad():
            A__    =      hf_model(**lowerCAmelCase__	)
        A__    =      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.
        A__    =      [[[4_00, 6_50], [8_00, 6_50]]]
        A__    =      [[1, 1]]

        A__    =      processor(
            images=np.array(lowerCAmelCase__	)						,	input_points=lowerCAmelCase__						,	input_labels=lowerCAmelCase__						,	return_tensors="pt"	).to("cuda"	)

        with torch.no_grad():
            A__    =      hf_model(**lowerCAmelCase__	)
        A__    =      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							:     Any		     =argparse.ArgumentParser()
  __lowerCAmelCase							:     List[Any]		     =["""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							:     Dict		     =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 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : List[str]						,	_lowerCamelCase : Tuple	):
    # Return True if there is node that has not iterated.
    A__    =      [False] * len(_lowerCamelCase	)
    A__    =      []
    queue.append(_lowerCamelCase	)
    A__    =      True

    while queue:
        A__    =      queue.pop(0	)
        for ind in range(len(graph[u]	)	):
            if visited[ind] is False and graph[u][ind] > 0:
                queue.append(_lowerCamelCase	)
                A__    =      True
                A__    =      u

    return visited[t]





def 						UpperCamelCase    (							_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[int]	):
    # This array is filled by BFS and to store path
    A__    =      [-1] * (len(_lowerCamelCase	))
    A__    =      0
    while bfs(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      float("Inf"	)
        A__    =      sink

        while s != source:
            # Find the minimum value in select path
            A__    =      min(_lowerCamelCase						,	graph[parent[s]][s]	)
            A__    =      parent[s]

        max_flow += path_flow
        A__    =      sink

        while v != source:
            A__    =      parent[v]
            graph[u][v] -= path_flow
            graph[v][u] += path_flow
            A__    =      parent[v]
    return max_flow


__lowerCAmelCase							:     Any		     =[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

__lowerCAmelCase     ,					__lowerCAmelCase							:     int		     =0, 5



print(ford_fulkerson(graph, source, sink))



