



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		       =      {
    """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(      __UpperCamelCase )	->						Optional[int]:
				a__	:							Optional[int]      						= {}
				state_dict.pop("pixel_mean"	,				__UpperCamelCase )
				state_dict.pop("pixel_std"	,				__UpperCamelCase )

				a__	:							Optional[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:
																a__	:							str      						= key.replace(__UpperCamelCase	,				__UpperCamelCase )

								if re.match(__UpperCamelCase	,				__UpperCamelCase ):
												a__	:							Union[str, Any]      						= int(re.match(__UpperCamelCase	,				__UpperCamelCase ).group(2 ) )
												if layer_nb == 0:
																a__	:							str      						= key.replace("layers.0"	,				"proj_in" )
												elif layer_nb == 1:
																a__	:							Optional[int]      						= key.replace("layers.1"	,				"layers.0" )
												elif layer_nb == 2:
																a__	:							Optional[int]      						= key.replace("layers.2"	,				"proj_out" )

								a__	:							int      						= value

				a__	:							int      						= model_state_dict[
				    "prompt_encoder.shared_embedding.positional_embedding"
				]

				return model_state_dict






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase="ybelkada/segment-anything" )	->						List[Any]:
				a__	:							Dict      						= hf_hub_download(__UpperCamelCase	,				F'checkpoints/{model_name}.pth' )

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

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

								a__	:							Dict      						= SamConfig(
								    vision_config=__UpperCamelCase	,				)

				a__	:							Optional[Any]      						= torch.load(__UpperCamelCase	,				map_location="cpu" )
				a__	:							int      						= replace_keys(__UpperCamelCase )

				a__	:							Any      						= SamImageProcessor()

				a__	:							Any      						= SamProcessor(image_processor=__UpperCamelCase )
				a__	:							int      						= SamModel(__UpperCamelCase )

				hf_model.load_state_dict(__UpperCamelCase )
				a__	:							Union[str, Any]      						= hf_model.to("cuda" )

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

				a__	:							Optional[int]      						= [[[4_00, 6_50]]]
				a__	:							Optional[int]      						= [[1]]

				a__	:							Optional[Any]      						= processor(images=np.array(__UpperCamelCase )	,				return_tensors="pt" ).to("cuda" )

				with torch.no_grad():
								a__	:							Optional[Any]      						= hf_model(**__UpperCamelCase )
				a__	:							Optional[int]      						= 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__	:							Union[str, Any]      						= processor(
								    images=np.array(__UpperCamelCase )	,				input_points=__UpperCamelCase	,				input_labels=__UpperCamelCase	,				return_tensors="pt" ).to("cuda" )

								with torch.no_grad():
												a__	:							Any      						= hf_model(**__UpperCamelCase )
								a__	:							Any      						= 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__	:							Optional[Any]      						= ((75, 2_75, 17_25, 8_50),)

								a__	:							Optional[Any]      						= processor(images=np.array(__UpperCamelCase )	,				input_boxes=__UpperCamelCase	,				return_tensors="pt" ).to("cuda" )

								with torch.no_grad():
												a__	:							List[str]      						= hf_model(**__UpperCamelCase )
								a__	:							Union[str, Any]      						= 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__	:							Dict      						= [[[4_00, 6_50], [8_00, 6_50]]]
								a__	:							Tuple      						= [[1, 1]]

								a__	:							str      						= processor(
								    images=np.array(__UpperCamelCase )	,				input_points=__UpperCamelCase	,				input_labels=__UpperCamelCase	,				return_tensors="pt" ).to("cuda" )

								with torch.no_grad():
												a__	:							Optional[int]      						= hf_model(**__UpperCamelCase )
								a__	:							Optional[Any]      						= 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		       =      argparse.ArgumentParser()
						lowerCamelCase		       =      ["""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		       =      parser.parse_args()

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


lowerCamelCase		       =      {}


try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      ["""MLukeTokenizer"""]

if TYPE_CHECKING:
						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mluke import MLukeTokenizer


else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


