





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()
_UpperCAmelCase       : Any			     =       logging.get_logger(__name__)

_UpperCAmelCase       : Optional[Any]			     =       torch.device("""cpu""")


def  SCREAMING_SNAKE_CASE   ( )    ->  List[str]:
    lowerCamelCase__					:   Union[str, Any]      				=	'http://images.cocodataset.org/val2017/000000039769.jpg'
    lowerCamelCase__					:   int      				=	Image.open(requests.get(_UpperCAmelCase		,			stream=_UpperCAmelCase				).raw				)
    return im


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  int:
    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]				)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]				)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]				)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Union[str, Any]:
    lowerCamelCase__					:   List[str]      				=	dct.pop(_UpperCAmelCase				)
    lowerCamelCase__					:   Optional[Any]      				=	val


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


@torch.no_grad()
def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  List[Any]:
    lowerCamelCase__					:   str      				=	SwiftFormerConfig()

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

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

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

    elif swiftformer_name == "swiftformer_l1":
        lowerCamelCase__					:   Optional[int]      				=	[4, 3, 10, 5]
        lowerCamelCase__					:   Any      				=	[48, 96, 192, 384]

    elif swiftformer_name == "swiftformer_l3":
        lowerCamelCase__					:   Optional[Any]      				=	[4, 4, 12, 6]
        lowerCamelCase__					:   Dict      				=	[64, 128, 320, 512]

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

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

    # load HuggingFace model
    lowerCamelCase__					:   Optional[Any]      				=	SwiftFormerForImageClassification(_UpperCAmelCase				).eval()
    hf_model.load_state_dict(_UpperCAmelCase				)

    # prepare test inputs
    lowerCamelCase__					:   List[Any]      				=	prepare_img()
    lowerCamelCase__					:   Dict      				=	ViTImageProcessor.from_pretrained('preprocessor_config'				)
    lowerCamelCase__					:   Any      				=	processor(images=_UpperCAmelCase		,			return_tensors='pt'				)

    # compare outputs from both models
    lowerCamelCase__					:   Optional[int]      				=	get_expected_output(_UpperCAmelCase				)
    lowerCamelCase__					:   Any      				=	hf_model(inputs['pixel_values']				).logits

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

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


if __name__ == "__main__":
    _UpperCAmelCase       : 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.""")

    _UpperCAmelCase       : str			     =       parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)












_UpperCAmelCase       : str			     =       """Tobias Carryer"""

from time import time

class      lowerCAmelCase							:
 def __init__(   self						:   Optional[Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Any    ,  UpperCAmelCase						:   str=int(time()			)			)    ->			Optional[int]:  # noqa: B008
     lowerCamelCase__					:   List[Any]      				=	multiplier
     lowerCamelCase__					:   Tuple      				=	increment
     lowerCamelCase__					:   int      				=	modulo
     lowerCamelCase__					:   Any      				=	seed







 def   A_			(   self						:   Optional[int]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	(self.multiplier * self.seed + self.increment) % self.modulo
     return self.seed


if __name__ == "__main__":
    # Show the LCG in action.
    _UpperCAmelCase       : List[Any]			     =       LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
    while True:
        print(lcg.next_number())





