





'''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__				:   Any      =torch.device('''cpu''')
def    UpperCamelCase__      (		):





      """simple docstring"""


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





      """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    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase							     =							dct.pop(lowerCAmelCase  )
      _lowerCAmelCase							     =							val
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


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





      """simple docstring"""


      _lowerCAmelCase							     =							SwiftFormerConfig()

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

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

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

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

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

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

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

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

      # compare outputs from both models
      _lowerCAmelCase							     =							get_expected_output(lowerCAmelCase  )
      _lowerCAmelCase							     =							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__":
      A__				:   str      =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__				:   Tuple      =parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)













import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git_vision_model"






     def __init__(							self						:    List[Any]						,lowerCamelCase__						:    Dict=768						,lowerCamelCase__						:    Union[str, Any]=3072						,lowerCamelCase__						:    Optional[int]=12						,lowerCamelCase__						:    Tuple=12						,lowerCamelCase__						:    Tuple=3						,lowerCamelCase__						:    Optional[Any]=224						,lowerCamelCase__						:    Union[str, Any]=16						,lowerCamelCase__						:    List[Any]="quick_gelu"						,lowerCamelCase__						:    Optional[Any]=1e-5						,lowerCamelCase__						:    str=0.0						,lowerCamelCase__						:    Optional[int]=0.02						,**lowerCamelCase__						:    Union[str, Any]						,)     ->		Optional[int]:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						hidden_act






     @classmethod
     def 				SCREAMING_SNAKE_CASE__  (							cls						:    Tuple						,lowerCamelCase__						:    Union[str, os.PathLike]						,**lowerCamelCase__						:    int       )     ->		"PretrainedConfig":




           '''simple docstring'''


           cls._set_token_in_kwargs(lowerCamelCase__       )

           SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						cls.get_config_dict(lowerCamelCase__						,**lowerCamelCase__       )

           # get the vision config dict if we are loading from GITConfig
           if config_dict.get("""model_type"""       ) == "git":
                 SCREAMING_SNAKE_CASE		=						config_dict["""vision_config"""]

           if "model_type" in config_dict and hasattr(cls						,"""model_type"""       ) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
                     F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""       )

           return cls.from_dict(lowerCamelCase__						,**lowerCamelCase__       )

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Dict			=			"git"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    int=None						,lowerCamelCase__						:    str=30522						,lowerCamelCase__						:    Tuple=768						,lowerCamelCase__						:    Union[str, Any]=6						,lowerCamelCase__						:    str=12						,lowerCamelCase__						:    List[str]=3072						,lowerCamelCase__						:    Dict="gelu"						,lowerCamelCase__						:    Tuple=0.1						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    List[str]=1024						,lowerCamelCase__						:    List[str]=0.02						,lowerCamelCase__						:    str=1e-1_2						,lowerCamelCase__						:    Optional[int]=0						,lowerCamelCase__						:    Optional[int]="absolute"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    str=False						,lowerCamelCase__						:    int=101						,lowerCamelCase__						:    int=102						,lowerCamelCase__						:    Dict=None						,**lowerCamelCase__						:    List[Any]						,)     ->		Optional[Any]:




           '''simple docstring'''


           super().__init__(bos_token_id=lowerCamelCase__						,eos_token_id=lowerCamelCase__						,pad_token_id=lowerCamelCase__						,**lowerCamelCase__       )

           if vision_config is None:
                 SCREAMING_SNAKE_CASE		=						{}
                 logger.info("""vision_config is None. initializing the GitVisionConfig with default values."""       )

           SCREAMING_SNAKE_CASE		=						GitVisionConfig(**lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						vocab_size
           SCREAMING_SNAKE_CASE		=						hidden_size
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						intermediate_size
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						attention_probs_dropout_prob
           SCREAMING_SNAKE_CASE		=						max_position_embeddings
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						use_cache
           SCREAMING_SNAKE_CASE		=						tie_word_embeddings
           SCREAMING_SNAKE_CASE		=						num_image_with_embedding

           SCREAMING_SNAKE_CASE		=						bos_token_id
           SCREAMING_SNAKE_CASE		=						eos_token_id






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						copy.deepcopy(self.__dict__       )
           SCREAMING_SNAKE_CASE		=						self.vision_config.to_dict()
           SCREAMING_SNAKE_CASE		=						self.__class__.model_type
           return output


