


'''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()
__UpperCAmelCase      :Optional[Any]			    =       logging.get_logger(__name__)

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




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase     :			Any  							=      Image.open(requests.get(_lowercase      , stream=_lowercase  ).raw  )
     return im




def      _a     (						_lowercase :     Optional[int]  ):





     '''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      _a     (						_lowercase :     str      , _lowercase :     List[str]      , _lowercase :     List[str]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      dct.pop(_lowercase  )
     __UpperCAmelCase     :			Dict  							=      val




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






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




@torch.no_grad()
def      _a     (						_lowercase :     List[Any]      , _lowercase :     str      , _lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      SwiftFormerConfig()

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

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

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

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

     elif swiftformer_name == "swiftformer_l3":
          __UpperCAmelCase     :			Optional[int]  							=      [4, 4, 12, 6]
          __UpperCAmelCase     :			Union[str, Any]  							=      [64, 128, 320, 512]

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

     __UpperCAmelCase     :			Tuple  							=      create_rename_keys(_lowercase  )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(_lowercase      , _lowercase      , _lowercase  )

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

     # prepare test inputs
     __UpperCAmelCase     :			List[str]  							=      prepare_img()
     __UpperCAmelCase     :			List[str]  							=      ViTImageProcessor.from_pretrained('''preprocessor_config'''  )
     __UpperCAmelCase     :			Optional[Any]  							=      processor(images=_lowercase      , return_tensors='''pt'''  )

     # compare outputs from both models
     __UpperCAmelCase     :			Union[str, Any]  							=      get_expected_output(_lowercase  )
     __UpperCAmelCase     :			int  							=      hf_model(inputs['''pixel_values''']  ).logits

     assert hf_logits.shape == torch.Size([1, 1000]  )
     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__":
      __UpperCAmelCase      :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.")

      __UpperCAmelCase      :Any			    =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)



'''simple docstring'''



import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :Dict			    =       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]




def      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

     if k.startswith('''encoder'''  ):
          __UpperCAmelCase     :			str  							=      k.replace('''.attn'''      , '''.self_attn'''  )
          __UpperCAmelCase     :			Any  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[str]  							=      k.replace('''norm2'''      , '''final_layer_norm'''  )
     elif k.startswith('''decoder'''  ):
          __UpperCAmelCase     :			int  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      k.replace('''norm2'''      , '''encoder_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[Any]  							=      k.replace('''norm3'''      , '''final_layer_norm'''  )
     return k




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''model.encoder.layernorm_embedding.weight''',
         '''model.encoder.layernorm_embedding.bias''',
         '''model.decoder.layernorm_embedding.weight''',
         '''model.decoder.layernorm_embedding.bias''',
     ]
     for k in keys:
          __UpperCAmelCase     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      torch.load(_lowercase      , map_location='''cpu'''  )
     __UpperCAmelCase     :			List[str]  							=      model['''model''']
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotConfig.from_json_file(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotForConditionalGeneration(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      m.model.state_dict().keys()
     __UpperCAmelCase     :			int  							=      []
     __UpperCAmelCase     :			List[str]  							=      {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          __UpperCAmelCase     :			int  							=      rename_state_dict_key(_lowercase  )
          if new_k not in valid_keys:
               failures.append([k, new_k]  )
          else:
               __UpperCAmelCase     :			Union[str, Any]  							=      v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(_lowercase  )
     m.model.load_state_dict(_lowercase      , strict=_lowercase  )
     m.half()
     m.save_pretrained(_lowercase  )


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[int]			    =       argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
      parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
      parser.add_argument(
          "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
      )
      __UpperCAmelCase      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)