


"""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()
snake_case__						:   Tuple			 = logging.get_logger(__name__)

snake_case__						:   int			 = torch.device('''cpu''')


def    _snake_case  (  ):
      lowerCAmelCase    :		int				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		Tuple				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)
      return im


def    _snake_case  (  _snake_case :	str	):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]	)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]	)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]	)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]	)


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Any       ,      _snake_case :	int	):
      lowerCAmelCase    :		Union[str, Any]				=      dct.pop(_snake_case	)
      lowerCAmelCase    :		int				=      val


def    _snake_case  (  _snake_case :	Any	):
      lowerCAmelCase    :		Optional[Any]				=      []
      for k in state_dict.keys():
            lowerCAmelCase    :		Tuple				=      k
            if ".pwconv" in k:
                  lowerCAmelCase    :		Optional[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    :		str				=      k_new.replace('''.Proj.'''       ,      '''.proj.'''	)
            if "patch_embed" in k_new:
                  lowerCAmelCase    :		Tuple				=      k_new.replace('''patch_embed'''       ,      '''swiftformer.patch_embed.patch_embedding'''	)
            if "network" in k_new:
                  lowerCAmelCase    :		int				=      k_new.split('''.'''	)
                  if ls[2].isdigit():
                        lowerCAmelCase    :		List[str]				=      '''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]	)
                  else:
                        lowerCAmelCase    :		List[Any]				=      k_new.replace('''network'''       ,      '''swiftformer.encoder.network'''	)
            rename_keys.append((k, k_new)	)
      return rename_keys


@torch.no_grad()
def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	Any       ,      _snake_case :	Optional[Any]	):
      lowerCAmelCase    :		Union[str, Any]				=      SwiftFormerConfig()

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

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            lowerCAmelCase    :		Optional[int]				=      [3, 3, 6, 4]
            lowerCAmelCase    :		Optional[Any]				=      [48, 56, 112, 220]

      elif swiftformer_name == "swiftformer_s":
            lowerCAmelCase    :		str				=      [3, 3, 9, 6]
            lowerCAmelCase    :		Any				=      [48, 64, 168, 224]

      elif swiftformer_name == "swiftformer_l1":
            lowerCAmelCase    :		List[Any]				=      [4, 3, 10, 5]
            lowerCAmelCase    :		List[str]				=      [48, 96, 192, 384]

      elif swiftformer_name == "swiftformer_l3":
            lowerCAmelCase    :		Tuple				=      [4, 4, 12, 6]
            lowerCAmelCase    :		List[Any]				=      [64, 128, 320, 512]

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

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

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

      # prepare test inputs
      lowerCAmelCase    :		Dict				=      prepare_img()
      lowerCAmelCase    :		Optional[Any]				=      ViTImageProcessor.from_pretrained('''preprocessor_config'''	)
      lowerCAmelCase    :		Optional[int]				=      processor(images=_snake_case       ,      return_tensors='''pt'''	)

      # compare outputs from both models
      lowerCAmelCase    :		Union[str, Any]				=      get_expected_output(_snake_case	)
      lowerCAmelCase    :		List[str]				=      hf_model(inputs['''pixel_values''']	).logits

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

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


if __name__ == "__main__":
 snake_case__						:   Optional[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.''')

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






"""simple docstring"""





import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
snake_case__						:   Optional[Any]			 = '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
snake_case__						:   Dict			 = direct_transformers_import(PATH_TO_TRANSFORMERS)

snake_case__						:   Optional[int]			 = transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
snake_case__						:   Optional[int]			 = re.compile(R'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')


snake_case__						:   int			 = {
    '''DecisionTransformerConfig''',
    '''EncoderDecoderConfig''',
    '''MusicgenConfig''',
    '''RagConfig''',
    '''SpeechEncoderDecoderConfig''',
    '''TimmBackboneConfig''',
    '''VisionEncoderDecoderConfig''',
    '''VisionTextDualEncoderConfig''',
    '''LlamaConfig''',
}


def    _snake_case  (  _snake_case :	List[str]	):
      lowerCAmelCase    :		Dict				=      None

      # source code of `config_class`
      lowerCAmelCase    :		Union[str, Any]				=      inspect.getsource(_snake_case	)
      lowerCAmelCase    :		List[Any]				=      _re_checkpoint.findall(_snake_case	)

      # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
      # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
      for ckpt_name, ckpt_link in checkpoints:
            # allow the link to end with `/`
            if ckpt_link.endswith('''/'''	):
                  lowerCAmelCase    :		List[str]				=      ckpt_link[:-1]

            # verify the checkpoint name corresponds to the checkpoint link
            lowerCAmelCase    :		Optional[int]				=      f'''https://huggingface.co/{ckpt_name}'''
            if ckpt_link == ckpt_link_from_name:
                  lowerCAmelCase    :		List[str]				=      ckpt_name
                  break

      return checkpoint


def    _snake_case  (  ):
      lowerCAmelCase    :		List[Any]				=      []

      for config_class in list(CONFIG_MAPPING.values()	):
            # Skip deprecated models
            if "models.deprecated" in config_class.__module__:
                  continue
            lowerCAmelCase    :		int				=      get_checkpoint_from_config_class(_snake_case	)

            lowerCAmelCase    :		int				=      config_class.__name__
            if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
                  configs_without_checkpoint.append(_snake_case	)

      if len(_snake_case	) > 0:
            lowerCAmelCase    :		Dict				=      '''\n'''.join(sorted(_snake_case	)	)
            raise ValueError(f'''The following configurations don\'t contain any valid checkpoint:\n{message}'''	)


if __name__ == "__main__":
 check_config_docstrings_have_checkpoints()


