





"""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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

A	     =					torch.device('''cpu''')

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

def       __A (     a_  :List[str])						->						List[Any]:
				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01])

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01])

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02])

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02])

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

def       __A (     a_  :str)						->						List[Any]:
				__a	:  List[str]         =					[]
				for k in state_dict.keys():
								__a	:  Optional[Any]         =					k
								if ".pwconv" in k:
												__a	:  List[str]         =					k_new.replace('''.pwconv'''				,    '''.point_wise_conv''')
								if ".dwconv" in k:
												__a	:  int         =					k_new.replace('''.dwconv'''				,    '''.depth_wise_conv''')
								if ".Proj." in k:
												__a	:  Union[str, Any]         =					k_new.replace('''.Proj.'''				,    '''.proj.''')
								if "patch_embed" in k_new:
												__a	:  str         =					k_new.replace('''patch_embed'''				,    '''swiftformer.patch_embed.patch_embedding''')
								if "network" in k_new:
												__a	:  int         =					k_new.split('''.''')
												if ls[2].isdigit():
																__a	:  Optional[int]         =					'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:])
												else:
																__a	:  Any         =					k_new.replace('''network'''				,    '''swiftformer.encoder.network''')
								rename_keys.append((k, k_new))
				return rename_keys

@torch.no_grad()
def       __A (     a_  :Optional[Any]				,    a_  :Union[str, Any]				,    a_  :str)						->						Dict:
				__a	:  Tuple         =					SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__a	:  Optional[Any]         =					10_00
				__a	:  str         =					'''huggingface/label-files'''
				__a	:  List[Any]         =					'''imagenet-1k-id2label.json'''
				__a	:  Tuple         =					json.load(open(hf_hub_download(a_				,    a_				,    repo_type='''dataset''')				,    '''r'''))
				__a	:  Optional[int]         =					{int(a_): v for k, v in idalabel.items()}
				__a	:  List[Any]         =					idalabel
				__a	:  int         =					{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__a	:  str         =					[3, 3, 6, 4]
								__a	:  Union[str, Any]         =					[48, 56, 1_12, 2_20]

				elif swiftformer_name == "swiftformer_s":
								__a	:  str         =					[3, 3, 9, 6]
								__a	:  Dict         =					[48, 64, 1_68, 2_24]

				elif swiftformer_name == "swiftformer_l1":
								__a	:  int         =					[4, 3, 10, 5]
								__a	:  str         =					[48, 96, 1_92, 3_84]

				elif swiftformer_name == "swiftformer_l3":
								__a	:  Optional[Any]         =					[4, 4, 12, 6]
								__a	:  Tuple         =					[64, 1_28, 3_20, 5_12]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''):
												__a	:  Union[str, Any]         =					torch.hub.load_state_dict_from_url(a_				,    map_location='''cpu'''				,    check_hash=a_)
								else:
												__a	:  Tuple         =					torch.load(a_				,    map_location='''cpu''')
				__a	:  Union[str, Any]         =					checkpoint

				__a	:  Any         =					create_rename_keys(a_)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(a_				,    a_				,    a_)

				# load HuggingFace model
				__a	:  Dict         =					SwiftFormerForImageClassification(a_).eval()
				hf_model.load_state_dict(a_)

				# prepare test inputs
				__a	:  Optional[Any]         =					prepare_img()
				__a	:  Dict         =					ViTImageProcessor.from_pretrained('''preprocessor_config''')
				__a	:  int         =					processor(images=a_				,    return_tensors='''pt''')

				# compare outputs from both models
				__a	:  List[str]         =					get_expected_output(a_)
				__a	:  str         =					hf_model(inputs['''pixel_values''']).logits

				assert hf_logits.shape == torch.Size([1, 10_00])
				assert torch.allclose(hf_logits[0, 0:5]				,    a_				,    atol=1e-3)

				Path(a_).mkdir(exist_ok=a_)
				print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}""")
				hf_model.save_pretrained(a_)


if __name__ == "__main__":
		A	     =					argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swiftformer_name''',
		    default='''swiftformer_xs''',
		    choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
		    type=str,
		    help='''Name of the SwiftFormer model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''',
		    default='''./converted_outputs/''',
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

		A	     =					parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






"""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)