






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   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    old_name

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

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

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

      SCREAMING_SNAKE_CASE       :      Optional[Any]							=    "last_stage." + trimmed_name

  elif "network" in old_name and re.search(R".\d."						,     lowercase				):
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    old_name.replace("network"						,     "intermediate_stages"				)

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

  return new_name


def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  for key in checkpoint.copy().keys():
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    checkpoint.pop(lowercase				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    val

  return checkpoint


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[int]							=    "http://images.cocodataset.org/val2017/000000039769.jpg"
  SCREAMING_SNAKE_CASE       :      str							=    Image.open(requests.get(lowercase						,     stream=lowercase				).raw				)

  return image


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    torch.load(lowercase						,     map_location="cpu"				)["model"]
  SCREAMING_SNAKE_CASE       :      Any							=    EfficientFormerConfig.from_json_file(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    EfficientFormerForImageClassificationWithTeacher(lowercase				)
  SCREAMING_SNAKE_CASE       :      List[Any]							=    "_".join(checkpoint_path.split("/"				)[-1].split("."				)[0].split("_"				)[:-1]				)

  SCREAMING_SNAKE_CASE       :      str							=    config.depths[-1] - config.num_metaad_blocks + 1
  SCREAMING_SNAKE_CASE       :      int							=    convert_torch_checkpoint(lowercase						,     lowercase				)

  model.load_state_dict(lowercase				)
  model.eval()

  SCREAMING_SNAKE_CASE       :      Optional[int]							=    {
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

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

  # original processing pipeline
  SCREAMING_SNAKE_CASE       :      Tuple							=    Compose(
      [
          Resize(lowercase						,     interpolation=pillow_resamplings["bicubic"]				),
          CenterCrop(lowercase				),
          ToTensor(),
          Normalize(lowercase						,     lowercase				),
      ]				)
  SCREAMING_SNAKE_CASE       :      str							=    image_transforms(lowercase				).unsqueeze(0				)

  assert torch.allclose(lowercase						,     lowercase				)

  SCREAMING_SNAKE_CASE       :      int							=    model(lowercase				)
  SCREAMING_SNAKE_CASE       :      Tuple							=    outputs.logits

  SCREAMING_SNAKE_CASE       :      Any							=    (1, 1000)

  if "l1" in model_name:
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    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:
    SCREAMING_SNAKE_CASE       :      str							=    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:
    SCREAMING_SNAKE_CASE       :      Tuple							=    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__":
   snake_case    				=		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)

   snake_case    				=		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 cmath import sqrt


def   lowerCamelCase__			(     lowercase						,     lowercase						,     lowercase				):

  """simple docstring"""






  if a == 0:
    raise ValueError("Coefficient 'a' must not be zero."				)
  SCREAMING_SNAKE_CASE       :      List[str]							=    b * b - 4 * a * c

  SCREAMING_SNAKE_CASE       :      str							=    (-b + sqrt(lowercase				)) / (2 * a)
  SCREAMING_SNAKE_CASE       :      Any							=    (-b - sqrt(lowercase				)) / (2 * a)

  return (
      root_a.real if not root_a.imag else root_a,
      root_a.real if not root_a.imag else root_a,
  )


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    quadratic_roots(a=5						,     b=6						,     c=1				)
  print(F'''The solutions are: {solutiona} and {solutiona}'''				)


if __name__ == "__main__":
   main()






