



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,
)


__A   			={
    "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        a						(      _UpperCAmelCase							:					str						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	{}
		state_dict.pop('''pixel_mean''' ,   _UpperCAmelCase						)
		state_dict.pop('''pixel_std''' ,   _UpperCAmelCase						)

		__UpperCAmelCase    :     int													=	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    :     Tuple													=	key.replace(_UpperCAmelCase ,   _UpperCAmelCase						)

				if re.match(_UpperCAmelCase ,   _UpperCAmelCase						):
						__UpperCAmelCase    :     Tuple													=	int(re.match(_UpperCAmelCase ,   _UpperCAmelCase						).group(2						)						)
						if layer_nb == 0:
								__UpperCAmelCase    :     List[Any]													=	key.replace('''layers.0''' ,   '''proj_in'''						)
						elif layer_nb == 1:
								__UpperCAmelCase    :     List[Any]													=	key.replace('''layers.1''' ,   '''layers.0'''						)
						elif layer_nb == 2:
								__UpperCAmelCase    :     Tuple													=	key.replace('''layers.2''' ,   '''proj_out'''						)

				__UpperCAmelCase    :     List[str]													=	value

		__UpperCAmelCase    :     Optional[Any]													=	model_state_dict[
		    '''prompt_encoder.shared_embedding.positional_embedding'''
		]

		return model_state_dict

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					List[str]="ybelkada/segment-anything"						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	hf_hub_download(_UpperCAmelCase ,   f'checkpoints/{model_name}.pth'						)

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

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

				__UpperCAmelCase    :     Optional[Any]													=	SamConfig(
				    vision_config=_UpperCAmelCase ,   )

		__UpperCAmelCase    :     List[Any]													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)
		__UpperCAmelCase    :     List[str]													=	replace_keys(_UpperCAmelCase						)

		__UpperCAmelCase    :     Dict													=	SamImageProcessor()

		__UpperCAmelCase    :     Optional[int]													=	SamProcessor(image_processor=_UpperCAmelCase						)
		__UpperCAmelCase    :     Optional[Any]													=	SamModel(_UpperCAmelCase						)

		hf_model.load_state_dict(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	hf_model.to('''cuda'''						)

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

		__UpperCAmelCase    :     List[Any]													=	[[[4_00, 6_50]]]
		__UpperCAmelCase    :     int													=	[[1]]

		__UpperCAmelCase    :     Optional[Any]													=	processor(images=np.array(_UpperCAmelCase						) ,   return_tensors='''pt'''						).to('''cuda'''						)

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

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.5_79_89_02_51_15_96_68

				__UpperCAmelCase    :     Union[str, Any]													=	processor(
				    images=np.array(_UpperCAmelCase						) ,   input_points=_UpperCAmelCase ,   input_labels=_UpperCAmelCase ,   return_tensors='''pt'''						).to('''cuda'''						)

				with torch.no_grad():
						__UpperCAmelCase    :     Union[str, Any]													=	hf_model(**_UpperCAmelCase						)
				__UpperCAmelCase    :     Optional[Any]													=	output.iou_scores.squeeze()

				assert scores[-1].item() == 0.97_12_60_30_92_19_36_04

				__UpperCAmelCase    :     Tuple													=	((75, 2_75, 17_25, 8_50),)

				__UpperCAmelCase    :     Optional[int]													=	processor(images=np.array(_UpperCAmelCase						) ,   input_boxes=_UpperCAmelCase ,   return_tensors='''pt'''						).to('''cuda'''						)

				with torch.no_grad():
						__UpperCAmelCase    :     Tuple													=	hf_model(**_UpperCAmelCase						)
				__UpperCAmelCase    :     Union[str, Any]													=	output.iou_scores.squeeze()

				assert scores[-1].item() == 0.86_86_01_56_05_92_65_14

				# Test with 2 points and 1 image.
				__UpperCAmelCase    :     List[str]													=	[[[4_00, 6_50], [8_00, 6_50]]]
				__UpperCAmelCase    :     Tuple													=	[[1, 1]]

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

				with torch.no_grad():
						__UpperCAmelCase    :     Optional[Any]													=	hf_model(**_UpperCAmelCase						)
				__UpperCAmelCase    :     List[Any]													=	output.iou_scores.squeeze()

				assert scores[-1].item() == 0.99_36_04_77_92_43_46_92


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				__A   			=["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",
				)

				__A   			=parser.parse_args()

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










import argparse
import shlex

import runhouse as rh


if __name__ == "__main__":
				# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
				# setup instructions, if using on-demand hardware

				# If user passes --user <user> --host <host> --key_path <key_path> <example> <args>, fill them in as BYO cluster
				# If user passes --instance <instance> --provider <provider> <example> <args>, fill them in as on-demand cluster
				# Throw an error if user passes both BYO and on-demand cluster args
				# Otherwise, use default values
				__A   			=argparse.ArgumentParser()
				parser.add_argument("--user", type=str, default="ubuntu")
				parser.add_argument("--host", type=str, default="localhost")
				parser.add_argument("--key_path", type=str, default=None)
				parser.add_argument("--instance", type=str, default="V100:1")
				parser.add_argument("--provider", type=str, default="cheapest")
				parser.add_argument("--use_spot", type=bool, default=False)
				parser.add_argument("--example", type=str, default="pytorch/text-generation/run_generation.py")
				__A			,			__A   			=parser.parse_known_args()
				if args.host != "localhost":
								if args.instance != "V100:1" or args.provider != "cheapest":
												raise ValueError("Cannot specify both BYO and on-demand cluster args")
								__A   			=rh.cluster(
								    name="rh-cluster", ips=[args.host], ssh_creds={"ssh_user": args.user, "ssh_private_key": args.key_path}
								)
				else:
								__A   			=rh.cluster(
								    name="rh-cluster", instance_type=args.instance, provider=args.provider, use_spot=args.use_spot
								)
				__A   			=args.example.rsplit("/", 1)[0]

				# Set up remote environment
				cluster.install_packages(["pip:./"])  # Installs transformers from local source
				# Note transformers is copied into the home directory on the remote machine, so we can install from there
				cluster.run([f'''pip install -r transformers/examples/{example_dir}/requirements.txt'''])
				cluster.run(["pip install torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117"])

				# Run example. You can bypass the CLI wrapper and paste your own code here.
				cluster.run([f'''python transformers/examples/{args.example} {" ".join(shlex.quote(arg) for arg in unknown)}'''])

				# Alternatively, we can just import and run a training function (especially if there's no wrapper CLI):
				# from my_script... import train
				# reqs = ['pip:./', 'torch', 'datasets', 'accelerate', 'evaluate', 'tqdm', 'scipy', 'scikit-learn', 'tensorboard']
				# launch_train_gpu = rh.function(fn=train,
				#                                system=gpu,
				#                                reqs=reqs,
				#                                name='train_bert_glue')
				#
				# We can pass in arguments just like we would to a function:
				# launch_train_gpu(num_epochs = 3, lr = 2e-5, seed = 42, batch_size = 16
				#                  stream_logs=True)





