





"""simple docstring"""


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        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							old_name

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

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

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

                  _UpperCamelCase   :						int    =							"last_stage." + trimmed_name

      elif "network" in old_name and re.search(r".\d."		,lowercase_      ):
            _UpperCamelCase   :						Union[str, Any]    =							old_name.replace("network"		,"intermediate_stages"      )

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

      return new_name




def        lowercase__     (       lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      for key in checkpoint.copy().keys():
            _UpperCamelCase   :						List[Any]    =							checkpoint.pop(lowercase_      )
            _UpperCamelCase   :						Optional[int]    =							val

      return checkpoint




def        lowercase__     (       )       ->		int:




      """simple docstring"""

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

      return image




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      _UpperCamelCase   :						Dict    =							EfficientFormerConfig.from_json_file(lowercase_      )
      _UpperCamelCase   :						Union[str, Any]    =							EfficientFormerForImageClassificationWithTeacher(lowercase_      )
      _UpperCamelCase   :						List[str]    =							"_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

      _UpperCamelCase   :						int    =							config.depths[-1] - config.num_metaad_blocks + 1
      _UpperCamelCase   :						Union[str, Any]    =							convert_torch_checkpoint(lowercase_		,lowercase_      )

      model.load_state_dict(lowercase_      )
      model.eval()

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

      # prepare image
      _UpperCamelCase   :						Any    =							prepare_img()
      _UpperCamelCase   :						List[Any]    =							256
      _UpperCamelCase   :						List[Any]    =							224
      _UpperCamelCase   :						Optional[int]    =							EfficientFormerImageProcessor(
          size={"shortest_edge": image_size}		,crop_size={"height": crop_size, "width": crop_size}		,resample=pillow_resamplings["bicubic"]		,)
      _UpperCamelCase   :						List[Any]    =							processor(images=lowercase_		,return_tensors="pt"      ).pixel_values

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

      assert torch.allclose(lowercase_		,lowercase_      )

      _UpperCamelCase   :						List[str]    =							model(lowercase_      )
      _UpperCamelCase   :						Tuple    =							outputs.logits

      _UpperCamelCase   :						str    =							(1, 1_000)

      if "l1" in model_name:
            _UpperCamelCase   :						Dict    =							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]		,lowercase_		,atol=1e-3      )
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            _UpperCamelCase   :						Optional[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]		,lowercase_		,atol=1e-3      )
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            _UpperCamelCase   :						Optional[int]    =							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__":
  lowerCamelCase__						= 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)

  lowerCamelCase__						= 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,
  )







"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
