"""simple docstring"""






# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


__lowerCAmelCase					: Union[str, Any]        =subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__lowerCAmelCase					: List[str]        =subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode("""utf-8""").split()

__lowerCAmelCase					: List[str]        ="""|""".join(sys.argv[1:])
__lowerCAmelCase					: Dict        =re.compile(RF"""^({joined_dirs}).*?\.py$""")

__lowerCAmelCase					: int        =[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")




"""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()
__lowerCAmelCase					: Optional[Any]        =logging.get_logger(__name__)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


      lowercase														=			original_name.split("""."""	)[0]
      lowercase														=			key.split("""."""	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 2]	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 1]	)
      lowercase														=			orig_block_num - offset

      lowercase														=			key.replace(f'{orig_block_num}.{layer_num}.{original_name}'      ,	f'block.{new_block_num}.{layer_num}.{new_name}'	)
      return key


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(lowerCAmelCase__      ,	stream=lowerCAmelCase__	).raw	)

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

      # set config attributes
      lowercase														=			json.load(open(hf_hub_download(lowerCAmelCase__      ,	lowerCAmelCase__      ,	repo_type="""dataset"""	)      ,	"""r"""	)	)
      lowercase														=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
      lowercase														=			idalabel
      lowercase														=			{v: k for k, v in idalabel.items()}
      if size == "s12":
            lowercase														=			[2, 2, 6, 2]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s24":
            lowercase														=			[4, 4, 1_2, 4]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.9
      elif size == "m36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      elif size == "m48":
            lowercase														=			[8, 8, 2_4, 8]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      else:
            raise ValueError(f'Size {size} not supported'	)

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

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

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

      # Define image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	).pixel_values

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			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]      ,	lowerCAmelCase__      ,	atol=1e-2	)

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


if __name__ == "__main__":
       __lowerCAmelCase					: List[Any]        =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."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



