



"""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   :							Tuple										=   logging.get_logger(__name__)

__A   :							List[Any]										=   torch.device("cpu")







def  lowercase	(      ):






				'''simple docstring'''



				_UpperCAmelCase					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_UpperCAmelCase					=    Image.open(requests.get(_SCREAMING_SNAKE_CASE					,     stream=_SCREAMING_SNAKE_CASE				).raw				)
				return im







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[str]				):






				'''simple docstring'''



				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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    dct.pop(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    val







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    []
				for k in state_dict.keys():
								_UpperCAmelCase					=    k
								if ".pwconv" in k:
												_UpperCAmelCase					=    k_new.replace('''.pwconv'''					,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												_UpperCAmelCase					=    k_new.replace('''.dwconv'''					,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												_UpperCAmelCase					=    k_new.replace('''.Proj.'''					,     '''.proj.'''				)
								if "patch_embed" in k_new:
												_UpperCAmelCase					=    k_new.replace('''patch_embed'''					,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												_UpperCAmelCase					=    k_new.split('''.'''				)
												if ls[2].isdigit():
																_UpperCAmelCase					=    '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																_UpperCAmelCase					=    k_new.replace('''network'''					,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys







@torch.no_grad()
def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Optional[int]				):






				'''simple docstring'''



				_UpperCAmelCase					=    SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				_UpperCAmelCase					=    1000
				_UpperCAmelCase					=    '''huggingface/label-files'''
				_UpperCAmelCase					=    '''imagenet-1k-id2label.json'''
				_UpperCAmelCase					=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
				_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
				_UpperCAmelCase					=    idalabel
				_UpperCAmelCase					=    {v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								_UpperCAmelCase					=    [3, 3, 6, 4]
								_UpperCAmelCase					=    [48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								_UpperCAmelCase					=    [3, 3, 9, 6]
								_UpperCAmelCase					=    [48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								_UpperCAmelCase					=    [4, 3, 10, 5]
								_UpperCAmelCase					=    [48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								_UpperCAmelCase					=    [4, 4, 12, 6]
								_UpperCAmelCase					=    [64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												_UpperCAmelCase					=    torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE					,     map_location='''cpu'''					,     check_hash=_SCREAMING_SNAKE_CASE				)
								else:
												_UpperCAmelCase					=    torch.load(_SCREAMING_SNAKE_CASE					,     map_location='''cpu'''				)
				_UpperCAmelCase					=    checkpoint

				_UpperCAmelCase					=    create_rename_keys(_SCREAMING_SNAKE_CASE				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# load HuggingFace model
				_UpperCAmelCase					=    SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE				).eval()
				hf_model.load_state_dict(_SCREAMING_SNAKE_CASE				)

				# prepare test inputs
				_UpperCAmelCase					=    prepare_img()
				_UpperCAmelCase					=    ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				_UpperCAmelCase					=    processor(images=_SCREAMING_SNAKE_CASE					,     return_tensors='''pt'''				)

				# compare outputs from both models
				_UpperCAmelCase					=    get_expected_output(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]					,     _SCREAMING_SNAKE_CASE					,     atol=1E-3				)

				Path(_SCREAMING_SNAKE_CASE				).mkdir(exist_ok=_SCREAMING_SNAKE_CASE				)
				print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'				)
				hf_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							int										=   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   :							Optional[Any]										=   parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Tuple   )->int:
							_UpperCAmelCase					=    inspect.getfile(accelerate.test_utils   )
							_UpperCAmelCase					=    os.path.sep.join(
							    mod_file.split(os.path.sep   )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']   )

							from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

							_UpperCAmelCase					=    test_metrics



			@require_cpu
			def  lowercase__						(		self     :    Any   )->int:
							debug_launcher(self.test_metrics.main				,   num_processes=1   )



			@require_cpu
			def  lowercase__						(		self     :    List[str]   )->List[str]:
							debug_launcher(self.test_metrics.main   )



			@require_single_gpu
			def  lowercase__						(		self     :    List[Any]   )->Dict:
							self.test_metrics.main()



			@require_multi_gpu
			def  lowercase__						(		self     :    str   )->int:
							print(F'Found {torch.cuda.device_count()} devices.'   )
							_UpperCAmelCase					=    ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
							with patch_environment(omp_num_threads=1   ):
											execute_subprocess_async(__UpperCamelCase				,   env=os.environ.copy()   )



