



"""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  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = "http://images.cocodataset.org/val2017/000000039769.jpg"
						snake_case   :			Optional[int]						    = Image.open(requests.get(lowerCamelCase_     ,		stream=lowerCamelCase_ ).raw )
						return im

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						if swiftformer_name == "swiftformer_xs":
												return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01] )

						elif swiftformer_name == "swiftformer_s":
												return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01] )

						elif swiftformer_name == "swiftformer_l1":
												return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02] )

						elif swiftformer_name == "swiftformer_l3":
												return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02] )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = dct.pop(lowerCamelCase_ )
						snake_case   :			Union[str, Any]						    = val

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






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

@torch.no_grad()
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = SwiftFormerConfig()

						# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
						snake_case   :			Optional[int]						    = 1_0_0_0
						snake_case   :			Any						    = "huggingface/label-files"
						snake_case   :			Union[str, Any]						    = "imagenet-1k-id2label.json"
						snake_case   :			int						    = json.load(open(hf_hub_download(lowerCamelCase_     ,		lowerCamelCase_     ,		repo_type="dataset" )     ,		"r" ) )
						snake_case   :			Any						    = {int(lowerCamelCase_ ): v for k, v in idalabel.items()}
						snake_case   :			Dict						    = idalabel
						snake_case   :			Any						    = {v: k for k, v in idalabel.items()}

						# size of the architecture
						if swiftformer_name == "swiftformer_xs":
												snake_case   :			Optional[Any]						    = [3, 3, 6, 4]
												snake_case   :			List[str]						    = [4_8, 5_6, 1_1_2, 2_2_0]

						elif swiftformer_name == "swiftformer_s":
												snake_case   :			Optional[Any]						    = [3, 3, 9, 6]
												snake_case   :			Union[str, Any]						    = [4_8, 6_4, 1_6_8, 2_2_4]

						elif swiftformer_name == "swiftformer_l1":
												snake_case   :			Union[str, Any]						    = [4, 3, 1_0, 5]
												snake_case   :			int						    = [4_8, 9_6, 1_9_2, 3_8_4]

						elif swiftformer_name == "swiftformer_l3":
												snake_case   :			int						    = [4, 4, 1_2, 6]
												snake_case   :			List[Any]						    = [6_4, 1_2_8, 3_2_0, 5_1_2]

						# load state_dict of original model, remove and rename some keys
						if original_ckpt:
												if original_ckpt.startswith("https" ):
																		snake_case   :			Any						    = torch.hub.load_state_dict_from_url(lowerCamelCase_     ,		map_location="cpu"     ,		check_hash=lowerCamelCase_ )
												else:
																		snake_case   :			Optional[int]						    = torch.load(lowerCamelCase_     ,		map_location="cpu" )
						snake_case   :			Dict						    = checkpoint

						snake_case   :			List[Any]						    = create_rename_keys(lowerCamelCase_ )
						for rename_key_src, rename_key_dest in rename_keys:
												rename_key(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

						# load HuggingFace model
						snake_case   :			Dict						    = SwiftFormerForImageClassification(lowerCamelCase_ ).eval()
						hf_model.load_state_dict(lowerCamelCase_ )

						# prepare test inputs
						snake_case   :			int						    = prepare_img()
						snake_case   :			Any						    = ViTImageProcessor.from_pretrained("preprocessor_config" )
						snake_case   :			Dict						    = processor(images=lowerCamelCase_     ,		return_tensors="pt" )

						# compare outputs from both models
						snake_case   :			str						    = get_expected_output(lowerCamelCase_ )
						snake_case   :			List[str]						    = hf_model(inputs["pixel_values"] ).logits

						assert hf_logits.shape == torch.Size([1, 1_0_0_0] )
						assert torch.allclose(hf_logits[0, 0:5]     ,		lowerCamelCase_     ,		atol=1e-3 )

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


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"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


A		=						{}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['GPTSw3Tokenizer']


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

else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



