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_    :		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 							lowerCAmelCase(     __lowerCamelCase						):
					__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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase="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=1024				,    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=1280				,    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		 =       [[[400, 650]]]
					__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_79_89_02_51_15_96_68

										__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() == 0.97_12_60_30_92_19_36_04

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

										__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() == 0.86_86_01_56_05_92_65_14

										# Test with 2 points and 1 image.
										__a		 =       [[[400, 650], [800, 650]]]
										__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() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
	lowerCamelCase_    :		Optional[int]							=	argparse.ArgumentParser()
	lowerCamelCase_    :		Tuple							=	["""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)





def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# 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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					# 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_    :		str							=	[
    [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_    :		Union[str, Any]							=	0, 5



print(ford_fulkerson(graph, source, sink))




