


'''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__						:   str				=    logging.get_logger(__name__)

A__						:   Tuple				=    torch.device("""cpu""")



def      UpperCAmelCase__   (      )	->						Any:
     __lowerCamelCase  :      List[str]														=						'http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowerCamelCase  :      int														=						Image.open(requests.get(UpperCAmelCase_ ,       stream=UpperCAmelCase_				).raw				)
     return im



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any]				)	->						Tuple:
     if swiftformer_name == "swiftformer_xs":
          return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]				)

     elif swiftformer_name == "swiftformer_s":
          return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]				)

     elif swiftformer_name == "swiftformer_l1":
          return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]				)

     elif swiftformer_name == "swiftformer_l3":
          return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any] ,       UpperCAmelCase_       :							List[str] ,       UpperCAmelCase_       :							List[str]				)	->						int:
     __lowerCamelCase  :      Optional[Any]														=						dct.pop(UpperCAmelCase_				)
     __lowerCamelCase  :      List[str]														=						val



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Tuple				)	->						Dict:
     __lowerCamelCase  :      str														=						[]
     for k in state_dict.keys():
          __lowerCamelCase  :      Optional[Any]														=						k
          if ".pwconv" in k:
               __lowerCamelCase  :      int														=						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  :      List[Any]														=						k_new.replace('.Proj.' ,       '.proj.'				)
          if "patch_embed" in k_new:
               __lowerCamelCase  :      Union[str, Any]														=						k_new.replace('patch_embed' ,       'swiftformer.patch_embed.patch_embedding'				)
          if "network" in k_new:
               __lowerCamelCase  :      Optional[Any]														=						k_new.split('.'				)
               if ls[2].isdigit():
                    __lowerCamelCase  :      int														=						'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      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[Any] ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							Any				)	->						Optional[int]:
     __lowerCamelCase  :      Any														=						SwiftFormerConfig()

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

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

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

     elif swiftformer_name == "swiftformer_l1":
          __lowerCamelCase  :      List[str]														=						[4, 3, 10, 5]
          __lowerCamelCase  :      Union[str, Any]														=						[48, 96, 1_92, 3_84]

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

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

     # load HuggingFace model
     __lowerCamelCase  :      List[str]														=						SwiftFormerForImageClassification(UpperCAmelCase_				).eval()
     hf_model.load_state_dict(UpperCAmelCase_				)

     # prepare test inputs
     __lowerCamelCase  :      Dict														=						prepare_img()
     __lowerCamelCase  :      Tuple														=						ViTImageProcessor.from_pretrained('preprocessor_config'				)
     __lowerCamelCase  :      Any														=						processor(images=UpperCAmelCase_ ,       return_tensors='pt'				)

     # compare outputs from both models
     __lowerCamelCase  :      List[Any]														=						get_expected_output(UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						hf_model(inputs['pixel_values']				).logits

     assert hf_logits.shape == torch.Size([1, 10_00]				)
     assert torch.allclose(hf_logits[0, 0:5] ,       UpperCAmelCase_ ,       atol=1e-3				)

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


if __name__ == "__main__":
      A__						:   List[Any]				=    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__						:   Union[str, Any]				=    parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

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


A__						:   List[str]				=    logging.get_logger(__name__)

# TODO Update this
A__						:   Tuple				=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      str														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Any														=						intermediate_size
         __lowerCamelCase  :      Optional[Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[int]														=						max_position_embeddings
         __lowerCamelCase  :      str														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						position_embedding_type
         __lowerCamelCase  :      int														=						use_cache
         __lowerCamelCase  :      Optional[Any]														=						emb_layer_norm_before
         __lowerCamelCase  :      Optional[Any]														=						token_dropout
         __lowerCamelCase  :      str														=						is_folding_model
         if is_folding_model:
              if esmfold_config is None:
                   logger.info('No esmfold_config supplied for folding model, using default values.'      )
                   __lowerCamelCase  :      Dict														=						EsmFoldConfig()
              elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Optional[int]														=						EsmFoldConfig(**SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						esmfold_config
              if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'      )
                   __lowerCamelCase  :      List[str]														=						get_default_vocab_list()
              else:
                   __lowerCamelCase  :      Optional[Any]														=						vocab_list
         else:
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
         if self.esmfold_config is not None and getattr(self.esmfold_config     ,	'use_esm_attn_map'     ,	SCREAMING_SNAKE_CASE_      ):
              raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   "TrunkConfig"					=     None







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[Any]														=						StructureModuleConfig(**self.structure_module      )

         if self.max_recycles <= 0:
              raise ValueError(f'`max_recycles` should be positive, got {self.max_recycles}.'      )
         if self.sequence_state_dim % self.sequence_state_dim != 0:
              raise ValueError(
                  '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                  f' {self.sequence_state_dim} and {self.sequence_state_dim}.'      )
         if self.pairwise_state_dim % self.pairwise_state_dim != 0:
              raise ValueError(
                  '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                  f' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						self.pairwise_state_dim // self.pairwise_head_width

         if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
              raise ValueError(
                  '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                  f' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'      )
         if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
              raise ValueError(
                  '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                  f' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'      )
         if self.pairwise_state_dim % 2 != 0:
              raise ValueError(f'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'      )

         if self.dropout >= 0.4:
              raise ValueError(f'`dropout` should not be greater than 0.4, got {self.dropout}.'      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[Any]:
     return (
         "<cls>",
         "<pad>",
         "<eos>",
         "<unk>",
         "L",
         "A",
         "G",
         "V",
         "S",
         "E",
         "R",
         "T",
         "I",
         "D",
         "P",
         "K",
         "Q",
         "N",
         "F",
         "Y",
         "M",
         "H",
         "W",
         "C",
         "X",
         "B",
         "U",
         "Z",
         "O",
         ".",
         "-",
         "<null_1>",
         "<mask>",
     )






