

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def      snake_case_				(__A					:						Optional[Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Optional[int]    =						SwinConfig()
   __lowerCAmelCase  :      List[Any]    =						swin_name.split("""_"""					)

   __lowerCAmelCase  :      Dict    =						name_split[1]
   __lowerCAmelCase  :      Optional[Any]    =						int(name_split[4]					)
   __lowerCAmelCase  :      List[Any]    =						int(name_split[3][-1]					)

   if model_size == "tiny":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      List[Any]    =						(2, 2, 6, 2)
      __lowerCAmelCase  :      Optional[Any]    =						(3, 6, 1_2, 2_4)
   elif model_size == "small":
      __lowerCAmelCase  :      List[Any]    =						9_6
      __lowerCAmelCase  :      Optional[int]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      Optional[int]    =						(3, 6, 1_2, 2_4)
   elif model_size == "base":
      __lowerCAmelCase  :      List[Any]    =						1_2_8
      __lowerCAmelCase  :      Tuple    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(4, 8, 1_6, 3_2)
   else:
      __lowerCAmelCase  :      List[Any]    =						1_9_2
      __lowerCAmelCase  :      List[str]    =						(2, 2, 1_8, 2)
      __lowerCAmelCase  :      int    =						(6, 1_2, 2_4, 4_8)

   if "in22k" in swin_name:
      __lowerCAmelCase  :      Dict    =						2_1_8_4_1
   else:
      __lowerCAmelCase  :      Optional[Any]    =						1_0_0_0
      __lowerCAmelCase  :      Union[str, Any]    =						"""huggingface/label-files"""
      __lowerCAmelCase  :      Any    =						"""imagenet-1k-id2label.json"""
      __lowerCAmelCase  :      Any    =						json.load(open(hf_hub_download(__A ,       __A ,       repo_type="""dataset"""					) ,       """r"""					)					)
      __lowerCAmelCase  :      int    =						{int(__A					): v for k, v in idalabel.items()}
      __lowerCAmelCase  :      str    =						idalabel
      __lowerCAmelCase  :      int    =						{v: k for k, v in idalabel.items()}

   __lowerCAmelCase  :      Optional[Any]    =						img_size
   __lowerCAmelCase  :      Optional[Any]    =						num_classes
   __lowerCAmelCase  :      Tuple    =						embed_dim
   __lowerCAmelCase  :      Union[str, Any]    =						depths
   __lowerCAmelCase  :      Optional[Any]    =						num_heads
   __lowerCAmelCase  :      Tuple    =						window_size

   return config





def      snake_case_				(__A					:						int					)      ->     Optional[Any]:
   if "patch_embed.proj" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""patch_embed.proj""" ,       """embeddings.patch_embeddings.projection"""					)
   if "patch_embed.norm" in name:
      __lowerCAmelCase  :      List[Any]    =						name.replace("""patch_embed.norm""" ,       """embeddings.norm"""					)
   if "layers" in name:
      __lowerCAmelCase  :      int    =						"""encoder.""" + name
   if "attn.proj" in name:
      __lowerCAmelCase  :      Tuple    =						name.replace("""attn.proj""" ,       """attention.output.dense"""					)
   if "attn" in name:
      __lowerCAmelCase  :      Optional[Any]    =						name.replace("""attn""" ,       """attention.self"""					)
   if "norm1" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm1""" ,       """layernorm_before"""					)
   if "norm2" in name:
      __lowerCAmelCase  :      Dict    =						name.replace("""norm2""" ,       """layernorm_after"""					)
   if "mlp.fc1" in name:
      __lowerCAmelCase  :      List[str]    =						name.replace("""mlp.fc1""" ,       """intermediate.dense"""					)
   if "mlp.fc2" in name:
      __lowerCAmelCase  :      str    =						name.replace("""mlp.fc2""" ,       """output.dense"""					)

   if name == "norm.weight":
      __lowerCAmelCase  :      Dict    =						"""layernorm.weight"""
   if name == "norm.bias":
      __lowerCAmelCase  :      Optional[int]    =						"""layernorm.bias"""

   if "head" in name:
      __lowerCAmelCase  :      int    =						name.replace("""head""" ,       """classifier"""					)
   else:
      __lowerCAmelCase  :      List[str]    =						"""swin.""" + name

   return name





def      snake_case_				(__A					:						List[Any] ,       __A					:						str					)      ->     int:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase  :      Tuple    =						orig_state_dict.pop(__A					)

      if "mask" in key:
         continue
      elif "qkv" in key:
         __lowerCAmelCase  :      Any    =						key.split("""."""					)
         __lowerCAmelCase  :      Union[str, Any]    =						int(key_split[1]					)
         __lowerCAmelCase  :      Optional[Any]    =						int(key_split[3]					)
         __lowerCAmelCase  :      Optional[int]    =						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

         if "weight" in key:
            __lowerCAmelCase  :      List[str]    =						val[:dim, :]
            __lowerCAmelCase  :      List[Any]    =						val[
                dim : dim * 2, :
            ]
            __lowerCAmelCase  :      str    =						val[-dim:, :]
         else:
            __lowerCAmelCase  :      str    =						val[
                :dim
            ]
            __lowerCAmelCase  :      int    =						val[
                dim : dim * 2
            ]
            __lowerCAmelCase  :      int    =						val[
                -dim:
            ]
      else:
         __lowerCAmelCase  :      Tuple    =						val

   return orig_state_dict





def      snake_case_				(__A					:						Union[str, Any] ,       __A					:						int					)      ->     Any:
   __lowerCAmelCase  :      List[Any]    =						timm.create_model(__A ,       pretrained=__A					)
   timm_model.eval()

   __lowerCAmelCase  :      str    =						get_swin_config(__A					)
   __lowerCAmelCase  :      Any    =						SwinForImageClassification(__A					)
   model.eval()

   __lowerCAmelCase  :      str    =						convert_state_dict(timm_model.state_dict() ,       __A					)
   model.load_state_dict(__A					)

   __lowerCAmelCase  :      str    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __lowerCAmelCase  :      Any    =						AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" ,       """-"""					)					)					)
   __lowerCAmelCase  :      List[Any]    =						Image.open(requests.get(__A ,       stream=__A					).raw					)
   __lowerCAmelCase  :      List[str]    =						image_processor(images=__A ,       return_tensors="""pt"""					)

   __lowerCAmelCase  :      Tuple    =						timm_model(inputs["""pixel_values"""]					)
   __lowerCAmelCase  :      Dict    =						model(**__A					).logits

   assert torch.allclose(__A ,       __A ,       atol=1e-3					)

   print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(__A					)

   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(__A					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swin_name""",
       default="""swin_tiny_patch4_window7_224""",
       type=str,
       help="""Name of the Swin timm model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
   )

   __UpperCAmelCase								=			parser.parse_args()
   convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)





import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__UpperCAmelCase								=			logging.get_logger(__name__)





def      snake_case_				(__A					:						List[str] ,       __A					:						str					)      ->     int:
   __lowerCAmelCase  :      str    =						set()
   __lowerCAmelCase  :      int    =						[]

   def parse_line(__A					:						List[Any]					):
      for line in fp:
         if isinstance(__A ,       __A					):
            __lowerCAmelCase  :      str    =						line.decode("""UTF-8"""					)
         if "warnings summary (final)" in line:
            continue
         # This means we are outside the body of a warning
         elif not line.startswith(""" """					):
            # process a single warning and move it to `selected_warnings`.
            if len(__A					) > 0:
               __lowerCAmelCase  :      Tuple    =						"""\n""".join(__A					)
               # Only keep the warnings specified in `targets`
               if any(f''': {x}: ''' in warning for x in targets					):
                  selected_warnings.add(__A					)
               buffer.clear()
            continue
         else:
            __lowerCAmelCase  :      Optional[int]    =						line.strip()
            buffer.append(__A					)

   if from_gh:
      for filename in os.listdir(__A					):
         __lowerCAmelCase  :      Optional[Any]    =						os.path.join(__A ,       __A					)
         if not os.path.isdir(__A					):
            # read the file
            if filename != "warnings.txt":
               continue
            with open(__A					) as fp:
               parse_line(__A					)
   else:
      try:
         with zipfile.ZipFile(__A					) as z:
            for filename in z.namelist():
               if not os.path.isdir(__A					):
                  # read the file
                  if filename != "warnings.txt":
                     continue
                  with z.open(__A					) as fp:
                     parse_line(__A					)
      except Exception:
         logger.warning(
             f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''					)

   return selected_warnings





def      snake_case_				(__A					:						Dict ,       __A					:						Union[str, Any]					)      ->     Dict:
   __lowerCAmelCase  :      Any    =						set()

   __lowerCAmelCase  :      Optional[int]    =						[os.path.join(__A ,       __A					) for p in os.listdir(__A					) if (p.endswith(""".zip"""					) or from_gh)]
   for p in paths:
      selected_warnings.update(extract_warnings_from_single_artifact(__A ,       __A					)					)

   return selected_warnings


if __name__ == "__main__":





   def      snake_case_				(__A					:						int					)      ->     Tuple:
      return values.split(""","""					)

   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
   parser.add_argument(
       """--output_dir""",
       type=str,
       required=True,
       help="""Where to store the downloaded artifacts and other result files.""",
   )
   parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
   # optional parameters
   parser.add_argument(
       """--targets""",
       default="""DeprecationWarning,UserWarning,FutureWarning""",
       type=list_str,
       help="""Comma-separated list of target warning(s) which we want to extract.""",
   )
   parser.add_argument(
       """--from_gh""",
       action="""store_true""",
       help="""If running from a GitHub action workflow and collecting warnings from its artifacts.""",
   )

   __UpperCAmelCase								=			parser.parse_args()

   __UpperCAmelCase								=			args.from_gh
   if from_gh:
      # The artifacts have to be downloaded using `actions/download-artifact@v3`
      pass
   else:
      os.makedirs(args.output_dir, exist_ok=True)

      # get download links
      __UpperCAmelCase								=			get_artifacts_links(args.workflow_run_id, token=args.token)
      with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
         json.dump(artifacts, fp, ensure_ascii=False, indent=4)

      # download artifacts
      for idx, (name, url) in enumerate(artifacts.items()):
         print(name)
         print(url)
         print("""=""" * 80)
         download_artifact(name, url, args.output_dir, args.token)
         # Be gentle to GitHub
         time.sleep(1)

    # extract warnings from artifacts
   __UpperCAmelCase								=			extract_warnings(args.output_dir, args.targets)
   __UpperCAmelCase								=			sorted(selected_warnings)
   with open(os.path.join(args.output_dir, """selected_warnings.json"""), """w""", encoding="""UTF-8""") as fp:
      json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)


