
'''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,
)


lowercase__						       =      {
    "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						(       SCREAMING_SNAKE_CASE__			)  ->							Tuple:


		'''simple docstring'''


		snake_case  :       List[str]     			=						{}
		state_dict.pop('''pixel_mean''' ,       SCREAMING_SNAKE_CASE__			)
		state_dict.pop('''pixel_std''' ,       SCREAMING_SNAKE_CASE__			)

		snake_case  :       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:
								snake_case  :       int     			=						key.replace(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

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

				snake_case  :       Tuple     			=						value

		snake_case  :       Union[str, Any]     			=						model_state_dict[
		    '''prompt_encoder.shared_embedding.positional_embedding'''
		]

		return model_state_dict




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__="ybelkada/segment-anything"			)  ->							int:


		'''simple docstring'''


		snake_case  :       Tuple     			=						hf_hub_download(SCREAMING_SNAKE_CASE__ ,       F'checkpoints/{model_name}.pth'			)

		if "sam_vit_b" in model_name:
				snake_case  :       List[str]     			=						SamConfig()
		elif "sam_vit_l" in model_name:
				snake_case  :       Union[str, Any]     			=						SamVisionConfig(
				    hidden_size=1024 ,       num_hidden_layers=24 ,       num_attention_heads=16 ,       global_attn_indexes=[5, 11, 17, 23] ,       )

				snake_case  :       Tuple     			=						SamConfig(
				    vision_config=SCREAMING_SNAKE_CASE__ ,       )
		elif "sam_vit_h" in model_name:
				snake_case  :       str     			=						SamVisionConfig(
				    hidden_size=1280 ,       num_hidden_layers=32 ,       num_attention_heads=16 ,       global_attn_indexes=[7, 15, 23, 31] ,       )

				snake_case  :       List[Any]     			=						SamConfig(
				    vision_config=SCREAMING_SNAKE_CASE__ ,       )

		snake_case  :       Dict     			=						torch.load(SCREAMING_SNAKE_CASE__ ,       map_location='''cpu'''			)
		snake_case  :       int     			=						replace_keys(SCREAMING_SNAKE_CASE__			)

		snake_case  :       Tuple     			=						SamImageProcessor()

		snake_case  :       Optional[Any]     			=						SamProcessor(image_processor=SCREAMING_SNAKE_CASE__			)
		snake_case  :       Dict     			=						SamModel(SCREAMING_SNAKE_CASE__			)

		hf_model.load_state_dict(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Any     			=						hf_model.to('''cuda'''			)

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

		snake_case  :       Any     			=						[[[400, 650]]]
		snake_case  :       Union[str, Any]     			=						[[1]]

		snake_case  :       Dict     			=						processor(images=np.array(SCREAMING_SNAKE_CASE__			) ,       return_tensors='''pt'''			).to('''cuda'''			)

		with torch.no_grad():
				snake_case  :       int     			=						hf_model(**SCREAMING_SNAKE_CASE__			)
		snake_case  :       List[str]     			=						output.iou_scores.squeeze()

		if model_name == "sam_vit_h_4b8939":
				assert scores[-1].item() == 0.579_8902_5115_9668

				snake_case  :       Any     			=						processor(
				    images=np.array(SCREAMING_SNAKE_CASE__			) ,       input_points=SCREAMING_SNAKE_CASE__ ,       input_labels=SCREAMING_SNAKE_CASE__ ,       return_tensors='''pt'''			).to('''cuda'''			)

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

				assert scores[-1].item() == 0.9712_6030_9219_3604

				snake_case  :       Optional[int]     			=						((75, 275, 1725, 850),)

				snake_case  :       Union[str, Any]     			=						processor(images=np.array(SCREAMING_SNAKE_CASE__			) ,       input_boxes=SCREAMING_SNAKE_CASE__ ,       return_tensors='''pt'''			).to('''cuda'''			)

				with torch.no_grad():
						snake_case  :       str     			=						hf_model(**SCREAMING_SNAKE_CASE__			)
				snake_case  :       Optional[int]     			=						output.iou_scores.squeeze()

				assert scores[-1].item() == 0.8686_0156_0592_6514

				# Test with 2 points and 1 image.
				snake_case  :       int     			=						[[[400, 650], [800, 650]]]
				snake_case  :       str     			=						[[1, 1]]

				snake_case  :       str     			=						processor(
				    images=np.array(SCREAMING_SNAKE_CASE__			) ,       input_points=SCREAMING_SNAKE_CASE__ ,       input_labels=SCREAMING_SNAKE_CASE__ ,       return_tensors='''pt'''			).to('''cuda'''			)

				with torch.no_grad():
						snake_case  :       List[str]     			=						hf_model(**SCREAMING_SNAKE_CASE__			)
				snake_case  :       List[Any]     			=						output.iou_scores.squeeze()

				assert scores[-1].item() == 0.9936_0477_9243_4692


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

						lowercase__						       =      parser.parse_args()

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




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "hustvl/yolos-small": "https://huggingface.co/hustvl/yolos-small/resolve/main/config.json",
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""yolos"""



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Optional[Any]=768  ,	UpperCamelCase__				:			Any=12  ,	UpperCamelCase__				:			Optional[int]=12  ,	UpperCamelCase__				:			Union[str, Any]=3072  ,	UpperCamelCase__				:			str="gelu"  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.0  ,	UpperCamelCase__				:			Union[str, Any]=0.02  ,	UpperCamelCase__				:			List[Any]=1e-1_2  ,	UpperCamelCase__				:			List[Any]=[512, 864]  ,	UpperCamelCase__				:			int=16  ,	UpperCamelCase__				:			Union[str, Any]=3  ,	UpperCamelCase__				:			Union[str, Any]=True  ,	UpperCamelCase__				:			int=100  ,	UpperCamelCase__				:			Optional[Any]=True  ,	UpperCamelCase__				:			int=False  ,	UpperCamelCase__				:			Dict=1  ,	UpperCamelCase__				:			Optional[int]=5  ,	UpperCamelCase__				:			List[str]=2  ,	UpperCamelCase__				:			Union[str, Any]=5  ,	UpperCamelCase__				:			Union[str, Any]=2  ,	UpperCamelCase__				:			int=0.1  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Any:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       List[str]     			=						hidden_size
				snake_case  :       Union[str, Any]     			=						num_hidden_layers
				snake_case  :       List[str]     			=						num_attention_heads
				snake_case  :       Any     			=						intermediate_size
				snake_case  :       int     			=						hidden_act
				snake_case  :       Optional[Any]     			=						hidden_dropout_prob
				snake_case  :       int     			=						attention_probs_dropout_prob
				snake_case  :       Union[str, Any]     			=						initializer_range
				snake_case  :       List[Any]     			=						layer_norm_eps
				snake_case  :       Tuple     			=						image_size
				snake_case  :       Any     			=						patch_size
				snake_case  :       str     			=						num_channels
				snake_case  :       Optional[int]     			=						qkv_bias
				snake_case  :       Optional[Any]     			=						num_detection_tokens
				snake_case  :       Dict     			=						use_mid_position_embeddings
				snake_case  :       List[Any]     			=						auxiliary_loss
				# Hungarian matcher
				snake_case  :       List[str]     			=						class_cost
				snake_case  :       Optional[Any]     			=						bbox_cost
				snake_case  :       Tuple     			=						giou_cost
				# Loss coefficients
				snake_case  :       Union[str, Any]     			=						bbox_loss_coefficient
				snake_case  :       List[Any]     			=						giou_loss_coefficient
				snake_case  :       Tuple     			=						eos_coefficient







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			int      )						->			float:






				"""simple docstring"""







				return 1e-4



		@property
		def       lowerCAmelCase				(      self				:			Tuple      )						->			int:






				"""simple docstring"""







				return 12


