





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  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Dict      		=  old_name

   if "patch_embed" in old_name:
      a__		,     a__		,     a__	:						Union[str, Any]      		=  old_name.split("."   )

      if layer == "0":
         a__	:						Union[str, Any]      		=  old_name.replace("0"							, "convolution1"   )
      elif layer == "1":
         a__	:						Dict      		=  old_name.replace("1"							, "batchnorm_before"   )
      elif layer == "3":
         a__	:						List[str]      		=  old_name.replace("3"							, "convolution2"   )
      else:
         a__	:						Optional[Any]      		=  old_name.replace("4"							, "batchnorm_after"   )

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

         a__	:						Any      		=  "last_stage." + trimmed_name

   elif "network" in old_name and re.search(r".\d."							, lowerCamelCase   ):
      a__	:						List[str]      		=  old_name.replace("network"							, "intermediate_stages"   )

   if "fc" in new_name:
      a__	:						str      		=  new_name.replace("fc"							, "convolution"   )
   elif ("norm1" in new_name) and ("layernorm1" not in new_name):
      a__	:						str      		=  new_name.replace("norm1"							, "batchnorm_before"   )
   elif ("norm2" in new_name) and ("layernorm2" not in new_name):
      a__	:						Any      		=  new_name.replace("norm2"							, "batchnorm_after"   )
   if "proj" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("proj"							, "projection"   )
   if "dist_head" in new_name:
      a__	:						Tuple      		=  new_name.replace("dist_head"							, "distillation_classifier"   )
   elif "head" in new_name:
      a__	:						Optional[int]      		=  new_name.replace("head"							, "classifier"   )
   elif "patch_embed" in new_name:
      a__	:						Tuple      		=  "efficientformer." + new_name
   elif new_name == "norm.weight" or new_name == "norm.bias":
      a__	:						Union[str, Any]      		=  new_name.replace("norm"							, "layernorm"   )
      a__	:						Optional[int]      		=  "efficientformer." + new_name
   else:
      a__	:						List[Any]      		=  "efficientformer.encoder." + new_name

   return new_name






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for key in checkpoint.copy().keys():
      a__	:						Optional[Any]      		=  checkpoint.pop(lowerCamelCase   )
      a__	:						Dict      		=  val

   return checkpoint






def       _A  (      ):
   a__	:						Tuple      		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
   a__	:						List[Any]      		=  Image.open(requests.get(lowerCamelCase							, stream=lowerCamelCase   ).raw   )

   return image






def       _A  (      lowerCamelCase							, lowerCamelCase							, lowerCamelCase							, lowerCamelCase   ):
   a__	:						List[str]      		=  torch.load(lowerCamelCase							, map_location="cpu"   )["model"]
   a__	:						str      		=  EfficientFormerConfig.from_json_file(lowerCamelCase   )
   a__	:						int      		=  EfficientFormerForImageClassificationWithTeacher(lowerCamelCase   )
   a__	:						Optional[Any]      		=  "_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

   a__	:						Tuple      		=  config.depths[-1] - config.num_metaad_blocks + 1
   a__	:						Union[str, Any]      		=  convert_torch_checkpoint(lowerCamelCase							, lowerCamelCase   )

   model.load_state_dict(lowerCamelCase   )
   model.eval()

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

   # prepare image
   a__	:						str      		=  prepare_img()
   a__	:						Dict      		=  256
   a__	:						Union[str, Any]      		=  224
   a__	:						List[str]      		=  EfficientFormerImageProcessor(
       size={"shortest_edge": image_size}							, crop_size={"height": crop_size, "width": crop_size}							, resample=pillow_resamplings["bicubic"]							, )
   a__	:						List[str]      		=  processor(images=lowerCamelCase							, return_tensors="pt"   ).pixel_values

   # original processing pipeline
   a__	:						List[str]      		=  Compose(
       [
           Resize(lowerCamelCase							, interpolation=pillow_resamplings["bicubic"]   ),
           CenterCrop(lowerCamelCase   ),
           ToTensor(),
           Normalize(lowerCamelCase							, lowerCamelCase   ),
       ]   )
   a__	:						List[Any]      		=  image_transforms(lowerCamelCase   ).unsqueeze(0   )

   assert torch.allclose(lowerCamelCase							, lowerCamelCase   )

   a__	:						Optional[int]      		=  model(lowerCamelCase   )
   a__	:						Any      		=  outputs.logits

   a__	:						Optional[Any]      		=  (1, 1000)

   if "l1" in model_name:
      a__	:						Tuple      		=  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, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__	:						int      		=  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, :10]							, lowerCamelCase							, atol=1E-3   )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__	:						Optional[Any]      		=  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(lowerCamelCase   ).mkdir(exist_ok=lowerCamelCase   )
   model.save_pretrained(lowerCamelCase   )
   print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""   )
   processor.save_pretrained(lowerCamelCase   )
   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=lowerCamelCase							, )
      processor.push_to_hub(
          repo_id=F"""Bearnardd/{pytorch_dump_path}"""							, commit_message="Add image processor"							, use_temp_dir=lowerCamelCase							, )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			str			      =						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)

  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						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

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

