





'''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()
lowercase__    :      Any						=    logging.get_logger(__name__)

lowercase__    :      Union[str, Any]						=    torch.device('''cpu''')






def        _lowerCAmelCase			(  )     ->     Optional[int]:
     __A						: Dict      =       'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A						: List[Any]      =       Image.open(requests.get(__snake_case ,							stream=__snake_case			).raw			)
     return im






def        _lowerCAmelCase			(  __snake_case  : List[str]			)     ->     Any:
     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        _lowerCAmelCase			(  __snake_case  : Optional[Any] ,							__snake_case  : List[str] ,							__snake_case  : List[Any]			)     ->     List[str]:
     __A						: Union[str, Any]      =       dct.pop(__snake_case			)
     __A						: Optional[Any]      =       val






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






@torch.no_grad()
def        _lowerCAmelCase			(  __snake_case  : Tuple ,							__snake_case  : str ,							__snake_case  : int			)     ->     Tuple:
     __A						: List[Any]      =       SwiftFormerConfig()

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

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

     elif swiftformer_name == "swiftformer_s":
          __A						: Optional[int]      =       [3, 3, 9, 6]
          __A						: str      =       [48, 64, 1_68, 2_24]

     elif swiftformer_name == "swiftformer_l1":
          __A						: str      =       [4, 3, 10, 5]
          __A						: Optional[int]      =       [48, 96, 1_92, 3_84]

     elif swiftformer_name == "swiftformer_l3":
          __A						: int      =       [4, 4, 12, 6]
          __A						: str      =       [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'			):
               __A						: Union[str, Any]      =       torch.hub.load_state_dict_from_url(__snake_case ,							map_location='cpu' ,							check_hash=__snake_case			)
          else:
               __A						: int      =       torch.load(__snake_case ,							map_location='cpu'			)
     __A						: str      =       checkpoint

     __A						: Dict      =       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
     __A						: List[Any]      =       SwiftFormerForImageClassification(__snake_case			).eval()
     hf_model.load_state_dict(__snake_case			)

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

     # compare outputs from both models
     __A						: Dict      =       get_expected_output(__snake_case			)
     __A						: Optional[int]      =       hf_model(inputs['pixel_values']			).logits

     assert hf_logits.shape == torch.Size([1, 10_00]			)
     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__":
  lowercase__    :      List[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.''')

  lowercase__    :      int						=    parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       ['''image_processor''', '''tokenizer''']
     lowerCAmelCase					=       '''BlipImageProcessor'''
     lowerCAmelCase					=       ('''BertTokenizer''', '''BertTokenizerFast''')


     def __init__(						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: int      =       False
          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)
          __A						: Optional[int]      =       self.image_processor


     def __call__(						self						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = True						,	_UpperCAmelCase = False						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = 0						,	_UpperCAmelCase = None						,	_UpperCAmelCase = None						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = False						,	_UpperCAmelCase = True						,	_UpperCAmelCase = None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          if images is None and text is None:
               raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
               __A						: int      =       self.tokenizer
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
               return text_encoding

          # add pixel_values
          __A						: List[Any]      =       self.image_processor(_UpperCAmelCase						,	return_tensors=_UpperCAmelCase)

          if text is not None:
               __A						: Optional[Any]      =       self.tokenizer(
                   text=_UpperCAmelCase						,	add_special_tokens=_UpperCAmelCase						,	padding=_UpperCAmelCase						,	truncation=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	stride=_UpperCAmelCase						,	pad_to_multiple_of=_UpperCAmelCase						,	return_attention_mask=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	return_special_tokens_mask=_UpperCAmelCase						,	return_offsets_mapping=_UpperCAmelCase						,	return_token_type_ids=_UpperCAmelCase						,	return_length=_UpperCAmelCase						,	verbose=_UpperCAmelCase						,	return_tensors=_UpperCAmelCase						,	**_UpperCAmelCase						,	)
          else:
               __A						: int      =       None

          if text_encoding is not None:
               encoding_image_processor.update(_UpperCAmelCase)

          return encoding_image_processor


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	*_UpperCAmelCase						,	**_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,	**_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       self.tokenizer.model_input_names
          __A						: Tuple      =       self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))