



"""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()
_lowerCAmelCase    : int							=							logging.get_logger(__name__)

_lowerCAmelCase    : Union[str, Any]							=							torch.device("cpu")

def 						__snake_case						(				)  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[int]        =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     _UpperCAmelCase      :     Dict        =   Image.open(requests.get(SCREAMING_SNAKE_CASE__						,  stream=SCREAMING_SNAKE_CASE__    ).raw    )
     return im

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple    )  ->       Dict:





     '''simple docstring'''

     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						(				SCREAMING_SNAKE_CASE__     :						List[Any]						,  SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						Dict    )  ->       int:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   dct.pop(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Any        =   val

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]    )  ->       Union[str, Any]:





     '''simple docstring'''

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

@torch.no_grad()
def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						int    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     _UpperCAmelCase      :     Optional[int]        =   1_000
     _UpperCAmelCase      :     str        =   "huggingface/label-files"
     _UpperCAmelCase      :     List[Any]        =   "imagenet-1k-id2label.json"
     _UpperCAmelCase      :     List[str]        =   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  repo_type="dataset"    )						,  "r"    )    )
     _UpperCAmelCase      :     Union[str, Any]        =   {int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}
     _UpperCAmelCase      :     Optional[int]        =   idalabel
     _UpperCAmelCase      :     Any        =   {v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          _UpperCAmelCase      :     Tuple        =   [3, 3, 6, 4]
          _UpperCAmelCase      :     str        =   [48, 56, 112, 220]

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

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

     elif swiftformer_name == "swiftformer_l3":
          _UpperCAmelCase      :     Tuple        =   [4, 4, 12, 6]
          _UpperCAmelCase      :     Dict        =   [64, 128, 320, 512]

     # load state_dict of original model, remove and rename some keys
     if original_ckpt:
          if original_ckpt.startswith("https"    ):
               _UpperCAmelCase      :     str        =   torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__						,  map_location="cpu"						,  check_hash=SCREAMING_SNAKE_CASE__    )
          else:
               _UpperCAmelCase      :     Any        =   torch.load(SCREAMING_SNAKE_CASE__						,  map_location="cpu"    )
     _UpperCAmelCase      :     str        =   checkpoint

     _UpperCAmelCase      :     List[Any]        =   create_rename_keys(SCREAMING_SNAKE_CASE__    )
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     # load HuggingFace model
     _UpperCAmelCase      :     Union[str, Any]        =   SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE__    ).eval()
     hf_model.load_state_dict(SCREAMING_SNAKE_CASE__    )

     # prepare test inputs
     _UpperCAmelCase      :     Tuple        =   prepare_img()
     _UpperCAmelCase      :     List[Any]        =   ViTImageProcessor.from_pretrained("preprocessor_config"    )
     _UpperCAmelCase      :     Optional[Any]        =   processor(images=SCREAMING_SNAKE_CASE__						,  return_tensors="pt"    )

     # compare outputs from both models
     _UpperCAmelCase      :     List[Any]        =   get_expected_output(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     str        =   hf_model(inputs["pixel_values"]    ).logits

     assert hf_logits.shape == torch.Size([1, 1_000]    )
     assert torch.allclose(hf_logits[0, 0:5]						,  SCREAMING_SNAKE_CASE__						,  atol=1E-3    )

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


if __name__ == "__main__":
 _lowerCAmelCase    : 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.")

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





"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						List[str]=7    )  ->       Tuple:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   None
     if token is not None:
          _UpperCAmelCase      :     str        =   {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}

     # The id of a workflow (not of a workflow run)
     _UpperCAmelCase      :     Any        =   "636036"

     _UpperCAmelCase      :     Dict        =   f'https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'
     # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
     url += f'?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'

     _UpperCAmelCase      :     Tuple        =   requests.get(SCREAMING_SNAKE_CASE__						,  headers=SCREAMING_SNAKE_CASE__    ).json()

     return result["workflow_runs"]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   get_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     _UpperCAmelCase      :     Optional[int]        =   None
     for workflow_run in workflow_runs:
          if workflow_run["status"] == "completed":
               _UpperCAmelCase      :     str        =   workflow_run["id"]
               break

     return workflow_run_id

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						str						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[str]        =   get_last_daily_ci_runs(SCREAMING_SNAKE_CASE__    )
     if workflow_run_id is not None:
          _UpperCAmelCase      :     Any        =   get_artifacts_links(worflow_run_id=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )
          for artifact_name in artifact_names:
               if artifact_name in artifacts_links:
                    _UpperCAmelCase      :     List[str]        =   artifacts_links[artifact_name]
                    download_artifact(
                        artifact_name=SCREAMING_SNAKE_CASE__						,  artifact_url=SCREAMING_SNAKE_CASE__						,  output_dir=SCREAMING_SNAKE_CASE__						,  token=SCREAMING_SNAKE_CASE__    )

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Dict						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]    )  ->       Union[str, Any]:





     '''simple docstring'''

     get_last_daily_ci_artifacts(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )

     _UpperCAmelCase      :     Any        =   {}
     for artifact_name in artifact_names:
          _UpperCAmelCase      :     Dict        =   os.path.join(SCREAMING_SNAKE_CASE__						,  f'{artifact_name}.zip'    )
          if os.path.isfile(SCREAMING_SNAKE_CASE__    ):
               _UpperCAmelCase      :     str        =   {}
               with zipfile.ZipFile(SCREAMING_SNAKE_CASE__    ) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(SCREAMING_SNAKE_CASE__    ):
                              # read the file
                              with z.open(SCREAMING_SNAKE_CASE__    ) as f:
                                   _UpperCAmelCase      :     List[str]        =   f.read().decode("UTF-8"    )

     return results
