



"""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        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Dict:
  a_     :      List[Any] 						=     old_name

  if "patch_embed" in old_name:
    a_     :      Tuple 						=     old_name.split("."	)

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

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

      a_     :      List[str] 						=     "last_stage." + trimmed_name

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

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

  return new_name



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Optional[int]:
  for key in checkpoint.copy().keys():
    a_     :      str 						=     checkpoint.pop(SCREAMING_SNAKE_CASE__	)
    a_     :      List[Any] 						=     val

  return checkpoint



def        lowerCAmelCase_						(							)			->      str:
  a_     :      int 						=     "http://images.cocodataset.org/val2017/000000039769.jpg"
  a_     :      Any 						=     Image.open(requests.get(SCREAMING_SNAKE_CASE__, stream=SCREAMING_SNAKE_CASE__	).raw	)

  return image



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      Tuple:
  a_     :      List[Any] 						=     torch.load(SCREAMING_SNAKE_CASE__, map_location="cpu"	)["model"]
  a_     :      List[str] 						=     EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
  a_     :      int 						=     EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__	)
  a_     :      Union[str, Any] 						=     "_".join(checkpoint_path.split("/"	)[-1].split("."	)[0].split("_"	)[:-1]	)

  a_     :      Any 						=     config.depths[-1] - config.num_metaad_blocks + 1
  a_     :      Union[str, Any] 						=     convert_torch_checkpoint(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

  model.load_state_dict(SCREAMING_SNAKE_CASE__	)
  model.eval()

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

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

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

  assert torch.allclose(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

  a_     :      int 						=     model(SCREAMING_SNAKE_CASE__	)
  a_     :      List[str] 						=     outputs.logits

  a_     :      Optional[int] 						=     (1, 1_000)

  if "l1" in model_name:
    a_     :      str 						=     torch.Tensor(
        [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]	)
    assert torch.allclose(logits[0, :10], SCREAMING_SNAKE_CASE__, atol=1e-3	)
    assert logits.shape == expected_shape
  elif "l3" in model_name:
    a_     :      str 						=     torch.Tensor(
        [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]	)
    assert torch.allclose(logits[0, :10], SCREAMING_SNAKE_CASE__, atol=1e-3	)
    assert logits.shape == expected_shape
  elif "l7" in model_name:
    a_     :      Dict 						=     torch.Tensor(
        [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]	)
    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(SCREAMING_SNAKE_CASE__	).mkdir(exist_ok=SCREAMING_SNAKE_CASE__	)
  model.save_pretrained(SCREAMING_SNAKE_CASE__	)
  print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)
  processor.save_pretrained(SCREAMING_SNAKE_CASE__	)
  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=SCREAMING_SNAKE_CASE__, )
    processor.push_to_hub(
        repo_id=F"""Bearnardd/{pytorch_dump_path}""", commit_message="Add image processor", use_temp_dir=SCREAMING_SNAKE_CASE__, )


if __name__ == "__main__":
     SCREAMING_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)

     SCREAMING_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,
     )




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




@slow
@require_torch_gpu
class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2