




'''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()
lowerCamelCase			:				Dict 			=					logging.get_logger(__name__)

lowerCamelCase			:				Dict 			=					torch.device("cpu")


def 	_lowerCAmelCase (						)				->      Optional[int]:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]    )				->      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 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Optional[int]    )				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =dct.pop(_lowerCAmelCase    )
     _SCREAMING_SNAKE_CASE							   =val


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      int:
     """simple docstring"""




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


@torch.no_grad()
def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					str    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     _SCREAMING_SNAKE_CASE							   =10_00
     _SCREAMING_SNAKE_CASE							   ="huggingface/label-files"
     _SCREAMING_SNAKE_CASE							   ="imagenet-1k-id2label.json"
     _SCREAMING_SNAKE_CASE							   =json.load(open(hf_hub_download(_lowerCAmelCase							,			_lowerCAmelCase							,			repo_type='dataset'    )							,			'r'    )    )
     _SCREAMING_SNAKE_CASE							   ={int(_lowerCAmelCase    ): v for k, v in idalabel.items()}
     _SCREAMING_SNAKE_CASE							   =idalabel
     _SCREAMING_SNAKE_CASE							   ={v: k for k, v in idalabel.items()}

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

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

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

     elif swiftformer_name == "swiftformer_l3":
          _SCREAMING_SNAKE_CASE							   =[4, 4, 12, 6]
          _SCREAMING_SNAKE_CASE							   =[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'    ):
               _SCREAMING_SNAKE_CASE							   =torch.hub.load_state_dict_from_url(_lowerCAmelCase							,			map_location='cpu'							,			check_hash=_lowerCAmelCase    )
          else:
               _SCREAMING_SNAKE_CASE							   =torch.load(_lowerCAmelCase							,			map_location='cpu'    )
     _SCREAMING_SNAKE_CASE							   =checkpoint

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

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

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

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

     assert hf_logits.shape == torch.Size([1, 10_00]    )
     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__":
     lowerCamelCase			:				Dict 			=					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.")

     lowerCamelCase			:				List[Any] 			=					parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase			:				Any 			=					logging.get_logger(__name__)

lowerCamelCase			:				int 			=					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}







class 							A__   (	A__  ):
  A__					=    'data2vec-vision'





  def __init__(		self				:     str							, _a				:     Optional[int]=768							, _a				:     List[str]=12							, _a				:     Any=12							, _a				:     Tuple=3072							, _a				:     Optional[Any]="gelu"							, _a				:     Tuple=0.0							, _a				:     str=0.0							, _a				:     Dict=0.02							, _a				:     int=1e-12							, _a				:     List[str]=224							, _a				:     str=16							, _a				:     Dict=3							, _a				:     int=False							, _a				:     List[str]=False							, _a				:     str=False							, _a				:     str=False							, _a				:     Optional[int]=0.1							, _a				:     List[str]=0.1							, _a				:     Dict=True							, _a				:     str=[3, 5, 7, 11]							, _a				:     List[Any]=[1, 2, 3, 6]							, _a				:     Any=True							, _a				:     Optional[Any]=0.4							, _a				:     str=256							, _a				:     Dict=1							, _a				:     List[str]=False							, _a				:     List[Any]=255							, **_a				:     Any							, )     ->   Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a			)

       _SCREAMING_SNAKE_CASE							   =hidden_size
       _SCREAMING_SNAKE_CASE							   =num_hidden_layers
       _SCREAMING_SNAKE_CASE							   =num_attention_heads
       _SCREAMING_SNAKE_CASE							   =intermediate_size
       _SCREAMING_SNAKE_CASE							   =hidden_act
       _SCREAMING_SNAKE_CASE							   =hidden_dropout_prob
       _SCREAMING_SNAKE_CASE							   =attention_probs_dropout_prob
       _SCREAMING_SNAKE_CASE							   =initializer_range
       _SCREAMING_SNAKE_CASE							   =layer_norm_eps

       _SCREAMING_SNAKE_CASE							   =image_size
       _SCREAMING_SNAKE_CASE							   =patch_size
       _SCREAMING_SNAKE_CASE							   =num_channels
       _SCREAMING_SNAKE_CASE							   =use_mask_token
       _SCREAMING_SNAKE_CASE							   =use_absolute_position_embeddings
       _SCREAMING_SNAKE_CASE							   =use_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =use_shared_relative_position_bias
       _SCREAMING_SNAKE_CASE							   =layer_scale_init_value
       _SCREAMING_SNAKE_CASE							   =drop_path_rate
       _SCREAMING_SNAKE_CASE							   =use_mean_pooling
       # decode head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =out_indices
       _SCREAMING_SNAKE_CASE							   =pool_scales
       # auxiliary head attributes (semantic segmentation)
       _SCREAMING_SNAKE_CASE							   =use_auxiliary_head
       _SCREAMING_SNAKE_CASE							   =auxiliary_loss_weight
       _SCREAMING_SNAKE_CASE							   =auxiliary_channels
       _SCREAMING_SNAKE_CASE							   =auxiliary_num_convs
       _SCREAMING_SNAKE_CASE							   =auxiliary_concat_input
       _SCREAMING_SNAKE_CASE							   =semantic_loss_ignore_index







class 							A__   (	A__  ):
  A__					=    version.parse('1.11'  )





  @property
  def 						A  (		self				:     str			)     ->   Mapping[str, Mapping[int, str]]:





       '''simple docstring'''






       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]			)





  @property
  def 						A  (		self				:     List[Any]			)     ->   float:





       '''simple docstring'''






       return 1e-4
