





import argparse
import re
from pathlib import Path

import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor

from transformers import (
    EfficientFormerConfig,
    EfficientFormerForImageClassificationWithTeacher,
    EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=old_name

   if "patch_embed" in old_name:
      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=old_name.split("."  )

      if layer == "0":
         UpperCAmelCase_											=old_name.replace("0"  ,    "convolution1"  )
      elif layer == "1":
         UpperCAmelCase_											=old_name.replace("1"  ,    "batchnorm_before"  )
      elif layer == "3":
         UpperCAmelCase_											=old_name.replace("3"  ,    "convolution2"  )
      else:
         UpperCAmelCase_											=old_name.replace("4"  ,    "batchnorm_after"  )

   if "network" in old_name and re.search(R"\d\.\d"  ,    lowercase__  ):
      UpperCAmelCase_											=R"\b\d{2}\b"
      if bool(re.search(lowercase__  ,    lowercase__  )  ):
         UpperCAmelCase_											=re.search(R"\d\.\d\d."  ,    lowercase__  ).group()
      else:
         UpperCAmelCase_											=re.search(R"\d\.\d."  ,    lowercase__  ).group()
      if int(match[0]  ) < 6:
         UpperCAmelCase_											=old_name.replace(lowercase__  ,    ""  )
         UpperCAmelCase_											=trimmed_name.replace("network"  ,    match[0] + ".meta4D_layers.blocks." + match[2:-1]  )
         UpperCAmelCase_											="intermediate_stages." + trimmed_name
      else:
         UpperCAmelCase_											=old_name.replace(lowercase__  ,    ""  )
         if int(match[2]  ) < num_meta4D_last_stage:
            UpperCAmelCase_											=trimmed_name.replace("network"  ,    "meta4D_layers.blocks." + match[2]  )
         else:
            UpperCAmelCase_											=str(int(match[2]  ) - num_meta4D_last_stage  )
            UpperCAmelCase_											=trimmed_name.replace("network"  ,    "meta3D_layers.blocks." + layer_index  )
            if "norm1" in old_name:
               UpperCAmelCase_											=trimmed_name.replace("norm1"  ,    "layernorm1"  )
            elif "norm2" in old_name:
               UpperCAmelCase_											=trimmed_name.replace("norm2"  ,    "layernorm2"  )
            elif "fc1" in old_name:
               UpperCAmelCase_											=trimmed_name.replace("fc1"  ,    "linear_in"  )
            elif "fc2" in old_name:
               UpperCAmelCase_											=trimmed_name.replace("fc2"  ,    "linear_out"  )

         UpperCAmelCase_											="last_stage." + trimmed_name

   elif "network" in old_name and re.search(R".\d."  ,    lowercase__  ):
      UpperCAmelCase_											=old_name.replace("network"  ,    "intermediate_stages"  )

   if "fc" in new_name:
      UpperCAmelCase_											=new_name.replace("fc"  ,    "convolution"  )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      UpperCAmelCase_											=new_name.replace("norm1"  ,    "batchnorm_before"  )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      UpperCAmelCase_											=new_name.replace("norm2"  ,    "batchnorm_after"  )
   if "proj" in new_name:
      UpperCAmelCase_											=new_name.replace("proj"  ,    "projection"  )
   if "dist_head" in new_name:
      UpperCAmelCase_											=new_name.replace("dist_head"  ,    "distillation_classifier"  )
   elif "head" in new_name:
      UpperCAmelCase_											=new_name.replace("head"  ,    "classifier"  )
   elif "patch_embed" in new_name:
      UpperCAmelCase_											="efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      UpperCAmelCase_											=new_name.replace("norm"  ,    "layernorm"  )
      UpperCAmelCase_											="efficientformer." + new_name
   else:
      UpperCAmelCase_											="efficientformer.encoder." + new_name

   return new_name




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   for key in checkpoint.copy().keys():
      UpperCAmelCase_											=checkpoint.pop(lowercase__  )
      UpperCAmelCase_											=val

   return checkpoint




def     a__ (					):






   '''simple docstring'''






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

   return image




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=torch.load(lowercase__  ,    map_location="cpu"  )["model"]
   UpperCAmelCase_											=EfficientFormerConfig.from_json_file(lowercase__  )
   UpperCAmelCase_											=EfficientFormerForImageClassificationWithTeacher(lowercase__  )
   UpperCAmelCase_											="_".join(checkpoint_path.split("/"  )[-1].split("."  )[0].split("_"  )[:-1]  )

   UpperCAmelCase_											=config.depths[-1] - config.num_metaad_blocks + 1
   UpperCAmelCase_											=convert_torch_checkpoint(lowercase__  ,    lowercase__  )

   model.load_state_dict(lowercase__  )
   model.eval()

   UpperCAmelCase_											={
       "bilinear": PILImageResampling.BILINEAR,
       "bicubic": PILImageResampling.BICUBIC,
       "nearest": PILImageResampling.NEAREST,
   }

   # prepare image
   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=2_5_6
   UpperCAmelCase_											=2_2_4
   UpperCAmelCase_											=EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}  ,    crop_size={"height": crop_size, "width": crop_size}  ,    resample=pillow_resamplings["bicubic"]  ,    )
   UpperCAmelCase_											=processor(images=lowercase__  ,    return_tensors="pt"  ).pixel_values

   # original processing pipeline
   UpperCAmelCase_											=Compose(
       [
           Resize(lowercase__  ,    interpolation=pillow_resamplings["bicubic"]  ),
           CenterCrop(lowercase__  ),
           ToTensor(),
           Normalize(lowercase__  ,    lowercase__  ),
       ]  )
   UpperCAmelCase_											=image_transforms(lowercase__  ).unsqueeze(0  )

   assert torch.allclose(lowercase__  ,    lowercase__  )

   UpperCAmelCase_											=model(lowercase__  )
   UpperCAmelCase_											=outputs.logits

   UpperCAmelCase_											=(1, 1_0_0_0)

   if "l1" in model_name:
      UpperCAmelCase_											=torch.Tensor(
          [-0.1312, 0.4353, -1.0499, -0.5124, 0.4183, -0.6793, -1.3777, -0.0893, -0.7358, -2.4328]  )
      assert torch.allclose(logits[0, :1_0]  ,    lowercase__  ,    atol=1E-3  )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      UpperCAmelCase_											=torch.Tensor(
          [-1.3150, -1.5456, -1.2556, -0.8496, -0.7127, -0.7897, -0.9728, -0.3052, 0.3751, -0.3127]  )
      assert torch.allclose(logits[0, :1_0]  ,    lowercase__  ,    atol=1E-3  )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      UpperCAmelCase_											=torch.Tensor(
          [-1.0283, -1.4131, -0.5644, -1.3115, -0.5785, -1.2049, -0.7528, 0.1992, -0.3822, -0.0878]  )
      assert logits.shape == expected_shape
   else:
      raise ValueError(
          F'Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7'  )

   # Save Checkpoints
   Path(lowercase__  ).mkdir(exist_ok=lowercase__  )
   model.save_pretrained(lowercase__  )
   print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'  )
   processor.save_pretrained(lowercase__  )
   print(F'Processor successfuly saved at {pytorch_dump_path}'  )

   if push_to_hub:
      print("Pushing model to the hub..."  )

      model.push_to_hub(
          repo_id=F'Bearnardd/{pytorch_dump_path}'  ,    commit_message="Add model"  ,    use_temp_dir=lowercase__  ,    )
      processor.push_to_hub(
          repo_id=F'Bearnardd/{pytorch_dump_path}'  ,    commit_message="Add image processor"  ,    use_temp_dir=lowercase__  ,    )


if __name__ == "__main__":
     __lowercase  :   Dict						      =argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--pytorch_model_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to EfficientFormer pytorch checkpoint.""",
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The json file for EfficientFormer model config.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )

     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
     parser.add_argument(
         """--no-push_to_hub""",
         dest="""push_to_hub""",
         action="""store_false""",
         help="""Do not push model and image processor to the hub""",
     )
     parser.set_defaults(push_to_hub=True)

     __lowercase  :   str						      =parser.parse_args()
     convert_efficientformer_checkpoint(
         checkpoint_path=args.pytorch_model_path,
         efficientformer_config_file=args.config_file,
         pytorch_dump_path=args.pytorch_dump_path,
         push_to_hub=args.push_to_hub,
     )











from __future__ import annotations




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=set(lowercase__  ), [start]

   while stack:
      UpperCAmelCase_											=stack.pop()
      explored.add(lowercase__  )
      # Differences from BFS:
      # 1) pop last element instead of first one
      # 2) add adjacent elements to stack without exploring them
      for adj in reversed(graph[v]  ):
         if adj not in explored:
            stack.append(lowercase__  )
   return explored


__lowercase  :   Dict						      ={
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(depth_first_search(G, """A"""))




