






'''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 							snake_case					(     a_							: str    ,   a_							: List[Any]    ,   a_							: Union[str, Any]    ,   a_							: Union[str, Any]	)       ->				Optional[int]:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							original_name.split("""."""	)[0]
      UpperCamelCase_				:   str					    =							key.split("""."""	)
      UpperCamelCase_				:   int					    =							int(key_list[key_list.index(__snake_case	) - 2]	)
      UpperCamelCase_				:   Tuple					    =							int(key_list[key_list.index(__snake_case	) - 1]	)
      UpperCamelCase_				:   List[Any]					    =							orig_block_num - offset

      UpperCamelCase_				:   Tuple					    =							key.replace(f"{orig_block_num}.{layer_num}.{original_name}"    ,   f"block.{new_block_num}.{layer_num}.{new_name}"	)
      return key



def 							snake_case					(     a_							: Union[str, Any]	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							OrderedDict()
      UpperCamelCase_    ,			UpperCamelCase_				:   Dict					    =							0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""	):
                  UpperCamelCase_				:   List[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[str]					    =							key[: key.find("""proj"""	)]
                  UpperCamelCase_				:   Tuple					    =							key.replace(__snake_case    ,   f"patch_embeddings.{total_embed_found}."	)
                  UpperCamelCase_				:   Dict					    =							key.replace("""proj"""    ,   """projection"""	)
                  if key.endswith("""bias"""	):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  UpperCamelCase_				:   Tuple					    =							"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  UpperCamelCase_				:   str					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """mlp.fc1"""    ,   """output.conv1"""	)
            if "mlp.fc2" in key:
                  UpperCamelCase_				:   str					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """mlp.fc2"""    ,   """output.conv2"""	)
            if "norm1" in key:
                  UpperCamelCase_				:   Tuple					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """norm1"""    ,   """before_norm"""	)
            if "norm2" in key:
                  UpperCamelCase_				:   List[str]					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """norm2"""    ,   """after_norm"""	)
            if "layer_scale_1" in key:
                  UpperCamelCase_				:   int					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """layer_scale_1"""    ,   """layer_scale_1"""	)
            if "layer_scale_2" in key:
                  UpperCamelCase_				:   List[str]					    =							replace_key_with_offset(__snake_case    ,   __snake_case    ,   """layer_scale_2"""    ,   """layer_scale_2"""	)
            if "head" in key:
                  UpperCamelCase_				:   Optional[Any]					    =							key.replace("""head"""    ,   """classifier"""	)
            UpperCamelCase_				:   Optional[int]					    =							value
      return new_state_dict



def 							snake_case					(     )       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase_				:   Optional[Any]					    =							Image.open(requests.get(__snake_case    ,   stream=__snake_case	).raw	)

      return image



@torch.no_grad()
def 							snake_case					(     a_							: int    ,   a_							: List[str]    ,   a_							: Any	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							PoolFormerConfig()

      # set attributes based on model_name
      UpperCamelCase_				:   Optional[Any]					    =							"""huggingface/label-files"""
      UpperCamelCase_				:   Optional[int]					    =							model_name[-3:]
      UpperCamelCase_				:   int					    =							1_000
      UpperCamelCase_				:   Tuple					    =							"""imagenet-1k-id2label.json"""
      UpperCamelCase_				:   Optional[int]					    =							(1, 1_000)

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

      # load image processor
      UpperCamelCase_				:   Tuple					    =							PoolFormerImageProcessor(crop_pct=__snake_case	)

      # Prepare image
      UpperCamelCase_				:   str					    =							prepare_img()
      UpperCamelCase_				:   Any					    =							image_processor(images=__snake_case    ,   return_tensors="""pt"""	).pixel_values

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

      # load original state dict
      UpperCamelCase_				:   int					    =							torch.load(__snake_case    ,   map_location=torch.device("""cpu"""	)	)

      # rename keys
      UpperCamelCase_				:   Optional[Any]					    =							rename_keys(__snake_case	)

      # create HuggingFace model and load state dict
      UpperCamelCase_				:   Optional[Any]					    =							PoolFormerForImageClassification(__snake_case	)
      model.load_state_dict(__snake_case	)
      model.eval()

      # Define image processor
      UpperCamelCase_				:   Dict					    =							PoolFormerImageProcessor(crop_pct=__snake_case	)
      UpperCamelCase_				:   List[str]					    =							image_processor(images=prepare_img()    ,   return_tensors="""pt"""	).pixel_values

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

      # define expected logit slices for different models
      if size == "s12":
            UpperCamelCase_				:   Dict					    =							torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            UpperCamelCase_				:   str					    =							torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            UpperCamelCase_				:   Union[str, Any]					    =							torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            UpperCamelCase_				:   Tuple					    =							torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
      else:
            raise ValueError(f"Size {size} not supported"	)

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

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


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'''

import warnings

from .generation import TFGenerationMixin




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 warnings.warn(
     '''Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.''',   SCREAMING_SNAKE_CASE__,   )






