



"""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   :							List[Any]										=   logging.get_logger(__name__)

__A   :							Dict										=   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   :						Tuple				):






    '''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   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]				):






    '''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   :						Optional[int]					,     _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   :							Dict										=   parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
					import transformers







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				random.seed(_SCREAMING_SNAKE_CASE				)
				np.random.seed(_SCREAMING_SNAKE_CASE				)
				torch.manual_seed(_SCREAMING_SNAKE_CASE				)
				torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE				)
				# ^^ safe to call this function even if cuda is not available
class 			_a :
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]				,   __UpperCamelCase     :    float = 0.9_9_9_9				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    int = 0				,   __UpperCamelCase     :    bool = False				,   __UpperCamelCase     :    Union[float, int] = 1.0				,   __UpperCamelCase     :    Union[float, int] = 2 / 3				,   __UpperCamelCase     :    Optional[Any] = None				,   __UpperCamelCase     :    Dict[str, Any] = None				,   **__UpperCamelCase     :    Optional[Any]				,   )->Tuple:

							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase				,   )
											_UpperCAmelCase					=    parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											_UpperCAmelCase					=    True

							if kwargs.get('''max_value'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `max_value` argument is deprecated. Please use `decay` instead.'''
											deprecate('''max_value'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    kwargs['''max_value''']

							if kwargs.get('''min_value'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
											deprecate('''min_value'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											_UpperCAmelCase					=    kwargs['''min_value''']

							_UpperCAmelCase					=    list(__UpperCamelCase   )
							_UpperCAmelCase					=    [p.clone().detach() for p in parameters]

							if kwargs.get('''device'''				,   __UpperCamelCase   ) is not None:
											_UpperCAmelCase					=    '''The `device` argument is deprecated. Please use `to` instead.'''
											deprecate('''device'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase   )
											self.to(device=kwargs['''device''']   )

							_UpperCAmelCase					=    None

							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    min_decay
							_UpperCAmelCase					=    update_after_step
							_UpperCAmelCase					=    use_ema_warmup
							_UpperCAmelCase					=    inv_gamma
							_UpperCAmelCase					=    power
							_UpperCAmelCase					=    0
							_UpperCAmelCase					=    None  # set in `step()`

							_UpperCAmelCase					=    model_cls
							_UpperCAmelCase					=    model_config



			@classmethod
			def  lowercase__						(		cls     :    List[str]				,   __UpperCamelCase     :    List[str]				,   __UpperCamelCase     :    Any   )->"EMAModel":
							_UpperCAmelCase		,						_UpperCAmelCase					=    model_cls.load_config(__UpperCamelCase				,   return_unused_kwargs=__UpperCamelCase   )
							_UpperCAmelCase					=    model_cls.from_pretrained(__UpperCamelCase   )

							_UpperCAmelCase					=    cls(model.parameters()				,   model_cls=__UpperCamelCase				,   model_config=model.config   )

							ema_model.load_state_dict(__UpperCamelCase   )
							return ema_model



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Any   )->Optional[Any]:
							if self.model_cls is None:
											raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''   )

							if self.model_config is None:
											raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''   )

							_UpperCAmelCase					=    self.model_cls.from_config(self.model_config   )
							_UpperCAmelCase					=    self.state_dict()
							state_dict.pop('''shadow_params'''				,   __UpperCamelCase   )

							model.register_to_config(**__UpperCamelCase   )
							self.copy_to(model.parameters()   )
							model.save_pretrained(__UpperCamelCase   )



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    int   )->float:
							_UpperCAmelCase					=    max(0				,   optimization_step - self.update_after_step - 1   )

							if step <= 0:
											return 0.0

							if self.use_ema_warmup:
											_UpperCAmelCase					=    1 - (1 + step / self.inv_gamma) ** -self.power
							else:
											_UpperCAmelCase					=    (1 + step) / (1_0 + step)

							_UpperCAmelCase					=    min(__UpperCamelCase				,   self.decay   )
							# make sure decay is not smaller than min_decay
							_UpperCAmelCase					=    max(__UpperCamelCase				,   self.min_decay   )
							return cur_decay_value



			@torch.no_grad()
			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->Optional[Any]:
							if isinstance(__UpperCamelCase				,   torch.nn.Module   ):
											_UpperCAmelCase					=    (
											    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
											    '''Please pass the parameters of the module instead.'''
											)
											deprecate(
											    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''				,   '''1.0.0'''				,   __UpperCamelCase				,   standard_warn=__UpperCamelCase				,   )
											_UpperCAmelCase					=    parameters.parameters()

							_UpperCAmelCase					=    list(__UpperCamelCase   )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							_UpperCAmelCase					=    self.get_decay(self.optimization_step   )
							_UpperCAmelCase					=    decay
							_UpperCAmelCase					=    1 - decay

							_UpperCAmelCase					=    contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
															_UpperCAmelCase					=    deepspeed.zero.GatheredParameters(__UpperCamelCase				,   modifier_rank=__UpperCamelCase   )

											with context_manager():
															if param.requires_grad:
																			s_param.sub_(one_minus_decay * (s_param - param)   )
															else:
																			s_param.copy_(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    list(__UpperCamelCase   )
							for s_param, param in zip(self.shadow_params				,   __UpperCamelCase   ):
											param.data.copy_(s_param.to(param.device   ).data   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Union[str, Any]=None   )->None:
							_UpperCAmelCase					=    [
							    p.to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   ) if p.is_floating_point() else p.to(device=__UpperCamelCase   )
							    for p in self.shadow_params
							]



			def  lowercase__						(		self     :    Optional[Any]   )->dict:
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							_UpperCAmelCase					=    [param.detach().cpu().clone() for param in parameters]



			def  lowercase__						(		self     :    List[Any]				,   __UpperCamelCase     :    Iterable[torch.nn.Parameter]   )->None:
							if self.temp_stored_params is None:
											raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''   )
							for c_param, param in zip(self.temp_stored_params				,   __UpperCamelCase   ):
											param.data.copy_(c_param.data   )

							# Better memory-wise.
							_UpperCAmelCase					=    None



			def  lowercase__						(		self     :    Any				,   __UpperCamelCase     :    dict   )->None:
							_UpperCAmelCase					=    copy.deepcopy(__UpperCamelCase   )

							_UpperCAmelCase					=    state_dict.get('''decay'''				,   self.decay   )
							if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError('''Decay must be between 0 and 1'''   )

							_UpperCAmelCase					=    state_dict.get('''min_decay'''				,   self.min_decay   )
							if not isinstance(self.min_decay				,   __UpperCamelCase   ):
											raise ValueError('''Invalid min_decay'''   )

							_UpperCAmelCase					=    state_dict.get('''optimization_step'''				,   self.optimization_step   )
							if not isinstance(self.optimization_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid optimization_step'''   )

							_UpperCAmelCase					=    state_dict.get('''update_after_step'''				,   self.update_after_step   )
							if not isinstance(self.update_after_step				,   __UpperCamelCase   ):
											raise ValueError('''Invalid update_after_step'''   )

							_UpperCAmelCase					=    state_dict.get('''use_ema_warmup'''				,   self.use_ema_warmup   )
							if not isinstance(self.use_ema_warmup				,   __UpperCamelCase   ):
											raise ValueError('''Invalid use_ema_warmup'''   )

							_UpperCAmelCase					=    state_dict.get('''inv_gamma'''				,   self.inv_gamma   )
							if not isinstance(self.inv_gamma				,   (float, int)   ):
											raise ValueError('''Invalid inv_gamma'''   )

							_UpperCAmelCase					=    state_dict.get('''power'''				,   self.power   )
							if not isinstance(self.power				,   (float, int)   ):
											raise ValueError('''Invalid power'''   )

							_UpperCAmelCase					=    state_dict.get('''shadow_params'''				,   __UpperCamelCase   )
							if shadow_params is not None:
											_UpperCAmelCase					=    shadow_params
											if not isinstance(self.shadow_params				,   __UpperCamelCase   ):
															raise ValueError('''shadow_params must be a list'''   )
											if not all(isinstance(__UpperCamelCase				,   torch.Tensor   ) for p in self.shadow_params   ):
															raise ValueError('''shadow_params must all be Tensors'''   )



