"""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     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :str	)				->					List[Any]:
      '''simple docstring'''


      lowercase														=			old_name

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

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

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

                  lowercase														=			"""last_stage.""" + trimmed_name

      elif "network" in old_name and re.search(R""".\d."""      ,	lowerCAmelCase__	):
            lowercase														=			old_name.replace("""network"""      ,	"""intermediate_stages"""	)

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

      return new_name


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Tuple	)				->					Union[str, Any]:
      '''simple docstring'''


      for key in checkpoint.copy().keys():
            lowercase														=			checkpoint.pop(lowerCAmelCase__	)
            lowercase														=			val

      return checkpoint


def     UpperCAmelCase__ (       )				->					List[str]:
      '''simple docstring'''


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

      return image


def     UpperCAmelCase__ (       lowerCAmelCase__  :Path      ,	lowerCAmelCase__  :Path      ,	lowerCAmelCase__  :Path      ,	lowerCAmelCase__  :bool	)				->					Any:
      '''simple docstring'''


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

      lowercase														=			config.depths[-1] - config.num_metaad_blocks + 1
      lowercase														=			convert_torch_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__	)

      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

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

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

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

      assert torch.allclose(lowerCAmelCase__      ,	lowerCAmelCase__	)

      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      lowercase														=			(1, 1_0_0_0)

      if "l1" in model_name:
            lowercase														=			torch.Tensor(
                [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]	)
            assert torch.allclose(logits[0, :1_0]      ,	lowerCAmelCase__      ,	atol=1e-3	)
            assert logits.shape == expected_shape
      elif "l3" in model_name:
            lowercase														=			torch.Tensor(
                [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]	)
            assert torch.allclose(logits[0, :1_0]      ,	lowerCAmelCase__      ,	atol=1e-3	)
            assert logits.shape == expected_shape
      elif "l7" in model_name:
            lowercase														=			torch.Tensor(
                [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]	)
            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__":
       __lowerCAmelCase					: Dict        =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					: 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,
       )




"""simple docstring"""






from __future__ import annotations


def     UpperCAmelCase__ (       lowerCAmelCase__  :list[float]      ,	lowerCAmelCase__  :list[float]	)				->					float:
      '''simple docstring'''


      lowercase														=			sorted(numsa + numsa	)
      lowercase							,							lowercase														=			divmod(len(lowerCAmelCase__	)      ,	2	)
      if mod == 1:
            return all_numbers[div]
      else:
            return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of first array: """).split()]
       __lowerCAmelCase					: List[Any]        =[float(x) for x in input("""Enter the elements of second array: """).split()]
       print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")



