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__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     old_name

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

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

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

                  SCREAMING_SNAKE_CASE_      =     "last_stage." + trimmed_name

      elif "network" in old_name and re.search(r".\d."   ,					__UpperCamelCase ):
            SCREAMING_SNAKE_CASE_      =     old_name.replace("network"   ,					"intermediate_stages" )

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

      return new_name


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase ):
      for key in checkpoint.copy().keys():
            SCREAMING_SNAKE_CASE_      =     checkpoint.pop(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     val

      return checkpoint


def    a__       (						):
      SCREAMING_SNAKE_CASE_      =     "http://images.cocodataset.org/val2017/000000039769.jpg"
      SCREAMING_SNAKE_CASE_      =     Image.open(requests.get(__UpperCamelCase   ,					stream=__UpperCamelCase ).raw )

      return image


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

      SCREAMING_SNAKE_CASE_      =     config.depths[-1] - config.num_metaad_blocks + 1
      SCREAMING_SNAKE_CASE_      =     convert_torch_checkpoint(__UpperCamelCase   ,					__UpperCamelCase )

      model.load_state_dict(__UpperCamelCase )
      model.eval()

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

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

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

      assert torch.allclose(__UpperCamelCase   ,					__UpperCamelCase )

      SCREAMING_SNAKE_CASE_      =     model(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     outputs.logits

      SCREAMING_SNAKE_CASE_      =     (1, 1_0_0_0)

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


if __name__ == "__main__":
      A   :     Any       =				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)

      A   :     List[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 functools import lru_cache


@lru_cache
def    a__       (						__UpperCamelCase ):
      if num < 0:
            raise ValueError("Number should not be negative." )

      return 1 if num in (0, 1) else num * factorial(num - 1 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


