





"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
A	     =					logging.get_logger(__name__)

def       __A (     a_  :Union[str, Any])						->						Tuple:
				# initialize config
				if "resnet-50" in model_name:
								__a	:  List[Any]         =					ResNetConfig.from_pretrained('''microsoft/resnet-50''')
				elif "resnet-101" in model_name:
								__a	:  Optional[Any]         =					ResNetConfig.from_pretrained('''microsoft/resnet-101''')
				else:
								raise ValueError('''Model name should include either resnet50 or resnet101''')

				__a	:  Tuple         =					DetrConfig(use_timm_backbone=a_				,    backbone_config=a_)

				# set label attributes
				__a	:  Union[str, Any]         =					'''panoptic''' in model_name
				if is_panoptic:
								__a	:  List[str]         =					2_50
				else:
								__a	:  List[Any]         =					91
								__a	:  Union[str, Any]         =					'''huggingface/label-files'''
								__a	:  List[Any]         =					'''coco-detection-id2label.json'''
								__a	:  int         =					json.load(open(hf_hub_download(a_				,    a_				,    repo_type='''dataset''')				,    '''r'''))
								__a	:  Optional[Any]         =					{int(a_): v for k, v in idalabel.items()}
								__a	:  Dict         =					idalabel
								__a	:  Union[str, Any]         =					{v: k for k, v in idalabel.items()}

				return config, is_panoptic

def       __A (     a_  :Optional[int])						->						Optional[Any]:
				# here we list all keys to be renamed (original name on the left, our name on the right)
				__a	:  Tuple         =					[]

				# stem
				# fmt: off
				rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight'''))
				rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight'''))
				rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias'''))
				rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean'''))
				rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var'''))
				# stages
				for stage_idx in range(len(config.backbone_config.depths)):
								for layer_idx in range(config.backbone_config.depths[stage_idx]):
												# shortcut
												if layer_idx == 0:
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
																    ))
												# 3 convs
												for i in range(3):
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
																    ))
																rename_keys.append(
																    (
																        F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
																        F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
																    ))
    # fmt: on

				for i in range(config.encoder_layers):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (
								        F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
								        F"""encoder.layers.{i}.self_attn.out_proj.weight""",
								    ))
								rename_keys.append(
								    (F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias"""))
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
								rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
								rename_keys.append(
								    (F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight"""))
								rename_keys.append(
								    (F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
								rename_keys.append(
								    (F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
								rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
								# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
								rename_keys.append(
								    (
								        F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
								        F"""decoder.layers.{i}.self_attn.out_proj.weight""",
								    ))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias"""))
								rename_keys.append(
								    (
								        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
								        F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
								    ))
								rename_keys.append(
								    (
								        F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
								        F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
								    ))
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
								rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight"""))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight"""))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias"""))
								rename_keys.append(
								    (F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
								rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))

				# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
				rename_keys.extend(
				    [
				        ('''input_proj.weight''', '''input_projection.weight'''),
				        ('''input_proj.bias''', '''input_projection.bias'''),
				        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
				        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
				        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
				        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
				        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
				        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
				        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
				        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
				        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
				        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
				        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
				    ])

				return rename_keys

def       __A (     a_  :Optional[Any]				,    a_  :Dict				,    a_  :List[str])						->						int:
				__a	:  Optional[Any]         =					state_dict.pop(a_)
				__a	:  Optional[int]         =					val

def       __A (     a_  :Optional[Any]				,    a_  :int=False)						->						Optional[int]:
				__a	:  List[Any]         =					''''''
				if is_panoptic:
								__a	:  Tuple         =					'''detr.'''

				# first: transformer encoder
				for i in range(6):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								__a	:  Any         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""")
								__a	:  Dict         =					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""")
								# next, add query, keys and values (in that order) to the state dict
								__a	:  str         =					in_proj_weight[:2_56, :]
								__a	:  Tuple         =					in_proj_bias[:2_56]
								__a	:  Optional[Any]         =					in_proj_weight[2_56:5_12, :]
								__a	:  Optional[int]         =					in_proj_bias[2_56:5_12]
								__a	:  Optional[int]         =					in_proj_weight[-2_56:, :]
								__a	:  List[Any]         =					in_proj_bias[-2_56:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6):
								# read in weights + bias of input projection layer of self-attention
								__a	:  str         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""")
								__a	:  Optional[int]         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""")
								# next, add query, keys and values (in that order) to the state dict
								__a	:  Optional[int]         =					in_proj_weight[:2_56, :]
								__a	:  List[str]         =					in_proj_bias[:2_56]
								__a	:  Dict         =					in_proj_weight[2_56:5_12, :]
								__a	:  Optional[Any]         =					in_proj_bias[2_56:5_12]
								__a	:  List[str]         =					in_proj_weight[-2_56:, :]
								__a	:  Tuple         =					in_proj_bias[-2_56:]
								# read in weights + bias of input projection layer of cross-attention
								__a	:  int         =					state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""")
								__a	:  str         =					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""")
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								__a	:  List[Any]         =					in_proj_weight_cross_attn[:2_56, :]
								__a	:  List[str]         =					in_proj_bias_cross_attn[:2_56]
								__a	:  Optional[Any]         =					in_proj_weight_cross_attn[2_56:5_12, :]
								__a	:  Union[str, Any]         =					in_proj_bias_cross_attn[2_56:5_12]
								__a	:  Optional[int]         =					in_proj_weight_cross_attn[-2_56:, :]
								__a	:  Tuple         =					in_proj_bias_cross_attn[-2_56:]

def       __A (     )						->						Optional[Any]:
				__a	:  Union[str, Any]         =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a	:  Optional[int]         =					Image.open(requests.get(a_				,    stream=a_).raw)

				return im

@torch.no_grad()
def       __A (     a_  :str				,    a_  :Optional[Any]=None				,    a_  :Optional[int]=False)						->						Dict:
				__a    ,  __a	:  Optional[int]         =					get_detr_config(a_)

				# load original model from torch hub
				__a	:  Any         =					{
				    '''detr-resnet-50''': '''detr_resnet50''',
				    '''detr-resnet-101''': '''detr_resnet101''',
				}
				logger.info(F"""Converting model {model_name}...""")
				__a	:  Tuple         =					torch.hub.load('''facebookresearch/detr'''				,    model_name_to_original_name[model_name]				,    pretrained=a_).eval()
				__a	:  Union[str, Any]         =					detr.state_dict()
				# rename keys
				for src, dest in create_rename_keys(a_):
								if is_panoptic:
												__a	:  Tuple         =					'''detr.''' + src
								rename_key(a_				,    a_				,    a_)
				# query, key and value matrices need special treatment
				read_in_q_k_v(a_				,    is_panoptic=a_)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				__a	:  int         =					'''detr.model.''' if is_panoptic else '''model.'''
				for key in state_dict.copy().keys():
								if is_panoptic:
												if (
												    key.startswith('''detr''')
												    and not key.startswith('''class_labels_classifier''')
												    and not key.startswith('''bbox_predictor''')
												):
																__a	:  Optional[Any]         =					state_dict.pop(a_)
																__a	:  Dict         =					val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																__a	:  List[Any]         =					state_dict.pop(a_)
																__a	:  List[Any]         =					val
												elif key.startswith('''bbox_attention''') or key.startswith('''mask_head'''):
																continue
												else:
																__a	:  Tuple         =					state_dict.pop(a_)
																__a	:  Optional[Any]         =					val
								else:
												if not key.startswith('''class_labels_classifier''') and not key.startswith('''bbox_predictor'''):
																__a	:  Any         =					state_dict.pop(a_)
																__a	:  Dict         =					val

    # finally, create HuggingFace model and load state dict
				__a	:  List[Any]         =					DetrForSegmentation(a_) if is_panoptic else DetrForObjectDetection(a_)
				model.load_state_dict(a_)
				model.eval()

				# verify our conversion on an image
				__a	:  Dict         =					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
				__a	:  int         =					DetrImageProcessor(format=a_)

				__a	:  List[Any]         =					processor(images=prepare_img()				,    return_tensors='''pt''')
				__a	:  Any         =					encoding['''pixel_values''']

				__a	:  int         =					detr(a_)
				__a	:  Any         =					model(a_)

				assert torch.allclose(outputs.logits				,    original_outputs['''pred_logits''']				,    atol=1e-3)
				assert torch.allclose(outputs.pred_boxes				,    original_outputs['''pred_boxes''']				,    atol=1e-3)
				if is_panoptic:
								assert torch.allclose(outputs.pred_masks				,    original_outputs['''pred_masks''']				,    atol=1e-4)
				print('''Looks ok!''')

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""")
								Path(a_).mkdir(exist_ok=a_)
								model.save_pretrained(a_)
								processor.save_pretrained(a_)

				if push_to_hub:
								# Upload model and image processor to the hub
								logger.info('''Uploading PyTorch model and image processor to the hub...''')
								model.push_to_hub(F"""nielsr/{model_name}""")
								processor.push_to_hub(F"""nielsr/{model_name}""")


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()

		parser.add_argument(
		    '''--model_name''',
		    default='''detr-resnet-50''',
		    type=str,
		    choices=['''detr-resnet-50''', '''detr-resnet-101'''],
		    help='''Name of the DETR model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
		A	     =					parser.parse_args()
		convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""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       __A (     a_  :List[Any])						->						List[Any]:
				__a	:  List[Any]         =					{}
				state_dict.pop('''pixel_mean'''				,    a_)
				state_dict.pop('''pixel_std'''				,    a_)

				__a	:  List[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	:  int         =					key.replace(a_				,    a_)

								if re.match(a_				,    a_):
												__a	:  Optional[Any]         =					int(re.match(a_				,    a_).group(2))
												if layer_nb == 0:
																__a	:  Any         =					key.replace('''layers.0'''				,    '''proj_in''')
												elif layer_nb == 1:
																__a	:  Dict         =					key.replace('''layers.1'''				,    '''layers.0''')
												elif layer_nb == 2:
																__a	:  Optional[int]         =					key.replace('''layers.2'''				,    '''proj_out''')

								__a	:  int         =					value

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

				return model_state_dict

def       __A (     a_  :Optional[int]				,    a_  :Optional[Any]				,    a_  :Dict				,    a_  :Optional[int]="ybelkada/segment-anything")						->						Dict:
				__a	:  Dict         =					hf_hub_download(a_				,    F"""checkpoints/{model_name}.pth""")

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

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

								__a	:  Optional[int]         =					SamConfig(
								    vision_config=a_				,    )

				__a	:  int         =					torch.load(a_				,    map_location='''cpu''')
				__a	:  Tuple         =					replace_keys(a_)

				__a	:  Optional[int]         =					SamImageProcessor()

				__a	:  Any         =					SamProcessor(image_processor=a_)
				__a	:  Any         =					SamModel(a_)

				hf_model.load_state_dict(a_)
				__a	:  Dict         =					hf_model.to('''cuda''')

				__a	:  Tuple         =					'''https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png'''
				__a	:  str         =					Image.open(requests.get(a_				,    stream=a_).raw).convert('''RGB''')

				__a	:  Tuple         =					[[[4_00, 6_50]]]
				__a	:  Tuple         =					[[1]]

				__a	:  Tuple         =					processor(images=np.array(a_)				,    return_tensors='''pt''').to('''cuda''')

				with torch.no_grad():
								__a	:  str         =					hf_model(**a_)
				__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	:  Any         =					processor(
								    images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  Optional[int]         =					hf_model(**a_)
								__a	:  Optional[int]         =					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	:  str         =					((75, 2_75, 17_25, 8_50),)

								__a	:  List[str]         =					processor(images=np.array(a_)				,    input_boxes=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  Any         =					hf_model(**a_)
								__a	:  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	:  int         =					[[[4_00, 6_50], [8_00, 6_50]]]
								__a	:  Dict         =					[[1, 1]]

								__a	:  Optional[Any]         =					processor(
								    images=np.array(a_)				,    input_points=a_				,    input_labels=a_				,    return_tensors='''pt''').to('''cuda''')

								with torch.no_grad():
												__a	:  int         =					hf_model(**a_)
								__a	:  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__":
		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)