




'''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,
)


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        _SCREAMING_SNAKE_CASE      (		snake_case       )						->				Union[str, Any]:
						_UpperCAmelCase      =			{}
						state_dict.pop("""pixel_mean"""			,     snake_case       )
						state_dict.pop("""pixel_std"""			,     snake_case       )

						_UpperCAmelCase      =			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      =			key.replace(snake_case			,     snake_case       )

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

												_UpperCAmelCase      =			value

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

						return model_state_dict




def        _SCREAMING_SNAKE_CASE      (		snake_case			,     snake_case			,     snake_case			,     snake_case="ybelkada/segment-anything"       )						->				Dict:
						_UpperCAmelCase      =			hf_hub_download(snake_case			,     f"checkpoints/{model_name}.pth"       )

						if "sam_vit_b" in model_name:
												_UpperCAmelCase      =			SamConfig()
						elif "sam_vit_l" in model_name:
												_UpperCAmelCase      =			SamVisionConfig(
												    hidden_size=1_0_2_4			,     num_hidden_layers=2_4			,     num_attention_heads=1_6			,     global_attn_indexes=[5, 1_1, 1_7, 2_3]			,     )

												_UpperCAmelCase      =			SamConfig(
												    vision_config=snake_case			,     )
						elif "sam_vit_h" in model_name:
												_UpperCAmelCase      =			SamVisionConfig(
												    hidden_size=1_2_8_0			,     num_hidden_layers=3_2			,     num_attention_heads=1_6			,     global_attn_indexes=[7, 1_5, 2_3, 3_1]			,     )

												_UpperCAmelCase      =			SamConfig(
												    vision_config=snake_case			,     )

						_UpperCAmelCase      =			torch.load(snake_case			,     map_location="""cpu"""       )
						_UpperCAmelCase      =			replace_keys(snake_case       )

						_UpperCAmelCase      =			SamImageProcessor()

						_UpperCAmelCase      =			SamProcessor(image_processor=snake_case       )
						_UpperCAmelCase      =			SamModel(snake_case       )

						hf_model.load_state_dict(snake_case       )
						_UpperCAmelCase      =			hf_model.to("""cuda"""       )

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

						_UpperCAmelCase      =			[[[4_0_0, 6_5_0]]]
						_UpperCAmelCase      =			[[1]]

						_UpperCAmelCase      =			processor(images=np.array(snake_case       )			,     return_tensors="""pt"""       ).to("""cuda"""       )

						with torch.no_grad():
												_UpperCAmelCase      =			hf_model(**snake_case       )
						_UpperCAmelCase      =			output.iou_scores.squeeze()

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

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

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

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

												_UpperCAmelCase      =			((7_5, 2_7_5, 1_7_2_5, 8_5_0),)

												_UpperCAmelCase      =			processor(images=np.array(snake_case       )			,     input_boxes=snake_case			,     return_tensors="""pt"""       ).to("""cuda"""       )

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

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

												# Test with 2 points and 1 image.
												_UpperCAmelCase      =			[[[4_0_0, 6_5_0], [8_0_0, 6_5_0]]]
												_UpperCAmelCase      =			[[1, 1]]

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

												with torch.no_grad():
																		_UpperCAmelCase      =			hf_model(**snake_case       )
												_UpperCAmelCase      =			output.iou_scores.squeeze()

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


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)





# flake8: noqa
# Lint as: python3
a													=			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental