





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()
__lowercase	: Union[str, Any]       =logging.get_logger(__name__)

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




def      a__			(       ):






   '''simple docstring'''






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




def      a__			(       lowercase__	):






   '''simple docstring'''






   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]	)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]	)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]	)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =dct.pop(lowercase__	)
   UpperCAmelCase_           =val




def      a__			(       lowercase__	):






   '''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      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCAmelCase_           =1_0_0_0
   UpperCAmelCase_           ="huggingface/label-files"
   UpperCAmelCase_           ="imagenet-1k-id2label.json"
   UpperCAmelCase_           =json.load(open(hf_hub_download(lowercase__	,     lowercase__	,     repo_type="dataset"	)	,     "r"	)	)
   UpperCAmelCase_           ={int(lowercase__	): 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_           =[4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      UpperCAmelCase_           =[3, 3, 9, 6]
      UpperCAmelCase_           =[4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      UpperCAmelCase_           =[4, 3, 1_0, 5]
      UpperCAmelCase_           =[4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      UpperCAmelCase_           =[4, 4, 1_2, 6]
      UpperCAmelCase_           =[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"	):
         UpperCAmelCase_           =torch.hub.load_state_dict_from_url(lowercase__	,     map_location="cpu"	,     check_hash=lowercase__	)
      else:
         UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =checkpoint

   UpperCAmelCase_           =create_rename_keys(lowercase__	)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowercase__	,     lowercase__	,     lowercase__	)

   # load HuggingFace model
   UpperCAmelCase_           =SwiftFormerForImageClassification(lowercase__	).eval()
   hf_model.load_state_dict(lowercase__	)

   # prepare test inputs
   UpperCAmelCase_           =prepare_img()
   UpperCAmelCase_           =ViTImageProcessor.from_pretrained("preprocessor_config"	)
   UpperCAmelCase_           =processor(images=lowercase__	,     return_tensors="pt"	)

   # compare outputs from both models
   UpperCAmelCase_           =get_expected_output(lowercase__	)
   UpperCAmelCase_           =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]	,     lowercase__	,     atol=1E-3	)

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


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

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






"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_a								=						logging.get_logger(__name__)

_a								=						{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = 'detr'
		lowercase__         = ['past_key_values']
		lowercase__         = {
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}



		def __init__(   self							,						__a=True							,						__a=None							,						__a=3							,						__a=1_00							,						__a=6							,						__a=20_48							,						__a=8							,						__a=6							,						__a=20_48							,						__a=8							,						__a=0.0							,						__a=0.0							,						__a=True							,						__a="relu"							,						__a=2_56							,						__a=0.1							,						__a=0.0							,						__a=0.0							,						__a=0.02							,						__a=1.0							,						__a=False							,						__a="sine"							,						__a="resnet50"							,						__a=True							,						__a=False							,						__a=1							,						__a=5							,						__a=2							,						__a=1							,						__a=1							,						__a=5							,						__a=2							,						__a=0.1							,						**__a							,						)			->			int:






							'''simple docstring'''
							if backbone_config is not None and use_timm_backbone:
												raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

							if not use_timm_backbone:
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
																	_UpperCamelCase									=    CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
												elif isinstance(__a							,						__a):
																	_UpperCamelCase									=    backbone_config.get('''model_type''')
																	_UpperCamelCase									=    CONFIG_MAPPING[backbone_model_type]
																	_UpperCamelCase									=    config_class.from_dict(__a)
												# set timm attributes to None
												_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    None, None, None

							_UpperCamelCase									=    use_timm_backbone
							_UpperCamelCase									=    backbone_config
							_UpperCamelCase									=    num_channels
							_UpperCamelCase									=    num_queries
							_UpperCamelCase									=    d_model
							_UpperCamelCase									=    encoder_ffn_dim
							_UpperCamelCase									=    encoder_layers
							_UpperCamelCase									=    encoder_attention_heads
							_UpperCamelCase									=    decoder_ffn_dim
							_UpperCamelCase									=    decoder_layers
							_UpperCamelCase									=    decoder_attention_heads
							_UpperCamelCase									=    dropout
							_UpperCamelCase									=    attention_dropout
							_UpperCamelCase									=    activation_dropout
							_UpperCamelCase									=    activation_function
							_UpperCamelCase									=    init_std
							_UpperCamelCase									=    init_xavier_std
							_UpperCamelCase									=    encoder_layerdrop
							_UpperCamelCase									=    decoder_layerdrop
							_UpperCamelCase									=    encoder_layers
							_UpperCamelCase									=    auxiliary_loss
							_UpperCamelCase									=    position_embedding_type
							_UpperCamelCase									=    backbone
							_UpperCamelCase									=    use_pretrained_backbone
							_UpperCamelCase									=    dilation
							# Hungarian matcher
							_UpperCamelCase									=    class_cost
							_UpperCamelCase									=    bbox_cost
							_UpperCamelCase									=    giou_cost
							# Loss coefficients
							_UpperCamelCase									=    mask_loss_coefficient
							_UpperCamelCase									=    dice_loss_coefficient
							_UpperCamelCase									=    bbox_loss_coefficient
							_UpperCamelCase									=    giou_loss_coefficient
							_UpperCamelCase									=    eos_coefficient
							super().__init__(is_encoder_decoder=__a							,						**__a)



		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return self.d_model



		@classmethod
		def 		UpperCAmelCase				(   cls							,						__a							,						**__a)			->			int:






							'''simple docstring'''
							return cls(backbone_config=__a							,						**__a)




		def 		UpperCAmelCase				(   self)			->			Dict[str, any]:






							'''simple docstring'''
							_UpperCamelCase									=    copy.deepcopy(self.__dict__)
							if output["backbone_config"] is not None:
												_UpperCamelCase									=    self.backbone_config.to_dict()
							_UpperCamelCase									=    self.__class__.model_type
							return output






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = version.parse('1.11'      )



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






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



		@property
		def 		UpperCAmelCase				(   self)			->			float:






							'''simple docstring'''
							return 1e-5




		@property
		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							return 12






