

"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase =				logging.get_logger(__name__)
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  List[Any]					   =					original_name.split("""."""      )[0]
 UpperCAmelCase__					:  List[str]					   =					key.split("""."""      )
 UpperCAmelCase__					:  Optional[int]					   =					int(key_list[key_list.index(__UpperCamelCase      ) - 2]      )
 UpperCAmelCase__					:  Union[str, Any]					   =					int(key_list[key_list.index(__UpperCamelCase      ) - 1]      )
 UpperCAmelCase__					:  Union[str, Any]					   =					orig_block_num - offset

 UpperCAmelCase__					:  Union[str, Any]					   =					key.replace(F"{orig_block_num}.{layer_num}.{original_name}"							,    F"block.{new_block_num}.{layer_num}.{new_name}"      )
 return key
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					OrderedDict()
 UpperCAmelCase__					:  Optional[int]					   =					0, 0
 for key, value in state_dict.items():
  if key.startswith("""network"""      ):
   UpperCAmelCase__					:  Optional[Any]					   =					key.replace("""network"""							,    """poolformer.encoder"""      )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith("""bias"""      ) and "patch_embed" not in key:
    patch_emb_offset += 1
   UpperCAmelCase__					:  List[Any]					   =					key[: key.find("""proj"""      )]
   UpperCAmelCase__					:  Tuple					   =					key.replace(__UpperCamelCase							,    F"patch_embeddings.{total_embed_found}."      )
   UpperCAmelCase__					:  int					   =					key.replace("""proj"""							,    """projection"""      )
   if key.endswith("""bias"""      ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   UpperCAmelCase__					:  Union[str, Any]					   =					"""poolformer.encoder.""" + key
  if "mlp.fc1" in key:
   UpperCAmelCase__					:  Optional[int]					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """mlp.fc1"""							,    """output.conv1"""      )
  if "mlp.fc2" in key:
   UpperCAmelCase__					:  Dict					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """mlp.fc2"""							,    """output.conv2"""      )
  if "norm1" in key:
   UpperCAmelCase__					:  Union[str, Any]					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """norm1"""							,    """before_norm"""      )
  if "norm2" in key:
   UpperCAmelCase__					:  Any					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """norm2"""							,    """after_norm"""      )
  if "layer_scale_1" in key:
   UpperCAmelCase__					:  int					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """layer_scale_1"""							,    """layer_scale_1"""      )
  if "layer_scale_2" in key:
   UpperCAmelCase__					:  Tuple					   =					replace_key_with_offset(__UpperCamelCase							,    __UpperCamelCase							,    """layer_scale_2"""							,    """layer_scale_2"""      )
  if "head" in key:
   UpperCAmelCase__					:  Optional[int]					   =					key.replace("""head"""							,    """classifier"""      )
  UpperCAmelCase__					:  Optional[int]					   =					value
 return new_state_dict
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  Union[str, Any]					   =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase__					:  Tuple					   =					Image.open(requests.get(__UpperCamelCase							,    stream=__UpperCamelCase      ).raw      )

 return image
@torch.no_grad()
def   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase      ):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					PoolFormerConfig()

 # set attributes based on model_name
 UpperCAmelCase__					:  int					   =					"""huggingface/label-files"""
 UpperCAmelCase__					:  Optional[int]					   =					model_name[-3:]
 UpperCAmelCase__					:  List[str]					   =					1000
 UpperCAmelCase__					:  List[Any]					   =					"""imagenet-1k-id2label.json"""
 UpperCAmelCase__					:  List[str]					   =					(1, 1000)

 # set config attributes
 UpperCAmelCase__					:  Optional[Any]					   =					json.load(open(hf_hub_download(__UpperCamelCase							,    __UpperCamelCase							,    repo_type="""dataset"""      )							,    """r"""      )      )
 UpperCAmelCase__					:  Optional[int]					   =					{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
 UpperCAmelCase__					:  Optional[Any]					   =					idalabel
 UpperCAmelCase__					:  Dict					   =					{v: k for k, v in idalabel.items()}
 if size == "s12":
  UpperCAmelCase__					:  str					   =					[2, 2, 6, 2]
  UpperCAmelCase__					:  Any					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Optional[int]					   =					4.0
  UpperCAmelCase__					:  Dict					   =					0.9
 elif size == "s24":
  UpperCAmelCase__					:  Tuple					   =					[4, 4, 12, 4]
  UpperCAmelCase__					:  str					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Tuple					   =					4.0
  UpperCAmelCase__					:  Dict					   =					0.9
 elif size == "s36":
  UpperCAmelCase__					:  List[str]					   =					[6, 6, 18, 6]
  UpperCAmelCase__					:  List[str]					   =					[64, 128, 320, 512]
  UpperCAmelCase__					:  Optional[Any]					   =					4.0
  UpperCAmelCase__					:  Tuple					   =					1e-6
  UpperCAmelCase__					:  str					   =					0.9
 elif size == "m36":
  UpperCAmelCase__					:  Any					   =					[6, 6, 18, 6]
  UpperCAmelCase__					:  Optional[int]					   =					[96, 192, 384, 768]
  UpperCAmelCase__					:  str					   =					4.0
  UpperCAmelCase__					:  List[Any]					   =					1e-6
  UpperCAmelCase__					:  Optional[Any]					   =					0.95
 elif size == "m48":
  UpperCAmelCase__					:  Union[str, Any]					   =					[8, 8, 24, 8]
  UpperCAmelCase__					:  Optional[int]					   =					[96, 192, 384, 768]
  UpperCAmelCase__					:  str					   =					4.0
  UpperCAmelCase__					:  Optional[int]					   =					1e-6
  UpperCAmelCase__					:  Optional[int]					   =					0.95
 else:
  raise ValueError(F"Size {size} not supported"      )

 # load image processor
 UpperCAmelCase__					:  Optional[int]					   =					PoolFormerImageProcessor(crop_pct=__UpperCamelCase      )

 # Prepare image
 UpperCAmelCase__					:  str					   =					prepare_img()
 UpperCAmelCase__					:  Optional[Any]					   =					image_processor(images=__UpperCamelCase							,    return_tensors="""pt"""      ).pixel_values

 logger.info(F"Converting model {model_name}..."      )

 # load original state dict
 UpperCAmelCase__					:  Union[str, Any]					   =					torch.load(__UpperCamelCase							,    map_location=torch.device("""cpu"""      )      )

 # rename keys
 UpperCAmelCase__					:  List[Any]					   =					rename_keys(__UpperCamelCase      )

 # create HuggingFace model and load state dict
 UpperCAmelCase__					:  Tuple					   =					PoolFormerForImageClassification(__UpperCamelCase      )
 model.load_state_dict(__UpperCamelCase      )
 model.eval()

 # Define image processor
 UpperCAmelCase__					:  str					   =					PoolFormerImageProcessor(crop_pct=__UpperCamelCase      )
 UpperCAmelCase__					:  Optional[int]					   =					image_processor(images=prepare_img()							,    return_tensors="""pt"""      ).pixel_values

 # forward pass
 UpperCAmelCase__					:  str					   =					model(__UpperCamelCase      )
 UpperCAmelCase__					:  Union[str, Any]					   =					outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([-0.3045, -0.6758, -0.4869]      )
 elif size == "s24":
  UpperCAmelCase__					:  Any					   =					torch.tensor([0.4402, -0.1374, -0.8045]      )
 elif size == "s36":
  UpperCAmelCase__					:  Any					   =					torch.tensor([-0.6080, -0.5133, -0.5898]      )
 elif size == "m36":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([0.3952, 0.2263, -1.2668]      )
 elif size == "m48":
  UpperCAmelCase__					:  Union[str, Any]					   =					torch.tensor([0.1167, -0.0656, -0.3423]      )
 else:
  raise ValueError(F"Size {size} not supported"      )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]							,    __UpperCamelCase							,    atol=1e-2      )

 # finally, save model and image processor
 logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."      )
 Path(__UpperCamelCase      ).mkdir(exist_ok=__UpperCamelCase      )
 model.save_pretrained(__UpperCamelCase      )
 print(F"Saving image processor to {pytorch_dump_folder_path}"      )
 image_processor.save_pretrained(__UpperCamelCase      )


if __name__ == "__main__":
      __UpperCAmelCase =				argparse.ArgumentParser()

      parser.add_argument(
          '--model_name',
          default='poolformer_s12',
          type=str,
          help='Name of the model you\'d like to convert.',
      )
      parser.add_argument(
          '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
      )
      __UpperCAmelCase =				parser.parse_args()
      convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)








"""simple docstring"""




from datetime import datetime as dt
import os

from github import Github


__UpperCAmelCase =				[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]
def   lowerCAmelCase						(					):
 '''simple docstring'''





 UpperCAmelCase__					:  str					   =					Github(os.environ["""GITHUB_TOKEN"""]      )
 UpperCAmelCase__					:  int					   =					g.get_repo("""huggingface/transformers"""      )
 UpperCAmelCase__					:  Optional[int]					   =					repo.get_issues(state="""open"""      )

 for issue in open_issues:
  UpperCAmelCase__					:  Any					   =					sorted([comment for comment in issue.get_comments()]							,    key=lambda __UpperCamelCase      : i.created_at							,    reverse=__UpperCamelCase      )
  UpperCAmelCase__					:  Union[str, Any]					   =					comments[0] if len(__UpperCamelCase      ) > 0 else None
  if (
      last_comment is not None
      and last_comment.user.login == "github-actions[bot]"
      and (dt.utcnow() - issue.updated_at).days > 7
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
   issue.edit(state="""closed"""      )
  elif (
      (dt.utcnow() - issue.updated_at).days > 23
      and (dt.utcnow() - issue.created_at).days >= 30
      and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()      )
  ):
   # print(f"Would add stale comment to {issue.number}")
   issue.create_comment(
       """This issue has been automatically marked as stale because it has not had """
       """recent activity. If you think this still needs to be addressed """
       """please comment on this thread.\n\nPlease note that issues that do not follow the """
       """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
       """are likely to be ignored."""      )


if __name__ == "__main__":
      main()





