





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	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Union[str, Any]   		=	old_name

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

      if layer == "0":
         a__						:    str   		=	old_name.replace("0"				,			"convolution1" )
      elif layer == "1":
         a__						:    int   		=	old_name.replace("1"				,			"batchnorm_before" )
      elif layer == "3":
         a__						:    Tuple   		=	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"				,			lowerCamelCase ):
      a__						:    List[str]   		=	r"\b\d{2}\b"
      if bool(re.search(lowerCamelCase				,			lowerCamelCase ) ):
         a__						:    Optional[int]   		=	re.search(r"\d\.\d\d."				,			lowerCamelCase ).group()
      else:
         a__						:    Dict   		=	re.search(r"\d\.\d."				,			lowerCamelCase ).group()
      if int(match[0] ) < 6:
         a__						:    Union[str, Any]   		=	old_name.replace(lowerCamelCase				,			"" )
         a__						:    List[str]   		=	trimmed_name.replace("network"				,			match[0] + ".meta4D_layers.blocks." + match[2:-1] )
         a__						:    List[Any]   		=	"intermediate_stages." + trimmed_name
      else:
         a__						:    List[Any]   		=	old_name.replace(lowerCamelCase				,			"" )
         if int(match[2] ) < num_meta4D_last_stage:
            a__						:    List[Any]   		=	trimmed_name.replace("network"				,			"meta4D_layers.blocks." + match[2] )
         else:
            a__						:    int   		=	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__						:    Any   		=	trimmed_name.replace("norm1"				,			"layernorm1" )
            elif "norm2" in old_name:
               a__						:    Optional[Any]   		=	trimmed_name.replace("norm2"				,			"layernorm2" )
            elif "fc1" in old_name:
               a__						:    List[str]   		=	trimmed_name.replace("fc1"				,			"linear_in" )
            elif "fc2" in old_name:
               a__						:    List[Any]   		=	trimmed_name.replace("fc2"				,			"linear_out" )

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

   elif "network" in old_name and re.search(r".\d."				,			lowerCamelCase ):
      a__						:    Any   		=	old_name.replace("network"				,			"intermediate_stages" )

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

   return new_name






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   for key in checkpoint.copy().keys():
      a__						:    str   		=	checkpoint.pop(lowerCamelCase )
      a__						:    List[str]   		=	val

   return checkpoint






def    _A	(   ):
   a__						:    List[str]   		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
   a__						:    Any   		=	Image.open(requests.get(lowerCamelCase				,			stream=lowerCamelCase ).raw )

   return image






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase ):
   a__						:    List[Any]   		=	torch.load(lowerCamelCase				,			map_location="cpu" )["model"]
   a__						:    Dict   		=	EfficientFormerConfig.from_json_file(lowerCamelCase )
   a__						:    Dict   		=	EfficientFormerForImageClassificationWithTeacher(lowerCamelCase )
   a__						:    List[Any]   		=	"_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )

   a__						:    List[str]   		=	config.depths[-1] - config.num_metaad_blocks + 1
   a__						:    List[Any]   		=	convert_torch_checkpoint(lowerCamelCase				,			lowerCamelCase )

   model.load_state_dict(lowerCamelCase )
   model.eval()

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

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

   # original processing pipeline
   a__						:    Dict   		=	Compose(
       [
           Resize(lowerCamelCase				,			interpolation=pillow_resamplings["bicubic"] ),
           CenterCrop(lowerCamelCase ),
           ToTensor(),
           Normalize(lowerCamelCase				,			lowerCamelCase ),
       ] )
   a__						:    int   		=	image_transforms(lowerCamelCase ).unsqueeze(0 )

   assert torch.allclose(lowerCamelCase				,			lowerCamelCase )

   a__						:    Dict   		=	model(lowerCamelCase )
   a__						:    List[str]   		=	outputs.logits

   a__						:    Optional[Any]   		=	(1, 1000)

   if "l1" in model_name:
      a__						:    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]				,			lowerCamelCase				,			atol=1E-3 )
      assert logits.shape == expected_shape
   elif "l3" in model_name:
      a__						:    List[Any]   		=	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]				,			lowerCamelCase				,			atol=1E-3 )
      assert logits.shape == expected_shape
   elif "l7" in model_name:
      a__						:    Dict   		=	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(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__":
  SCREAMING_SNAKE_CASE__							:      str         =    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__							:      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,
  )








# Function to print upper half of diamond (pyramid)






def    _A	(   lowerCamelCase ):
   for i in range(0				,			lowerCamelCase ):
      for _ in range(0				,			n - i - 1 ):  # printing spaces
         print(" "				,			end="" )
      for _ in range(0				,			i + 1 ):  # printing stars
         print("* "				,			end="" )
      print()






def    _A	(   lowerCamelCase ):
   for i in range(lowerCamelCase				,			0				,			-1 ):
      for _ in range(lowerCamelCase				,			0				,			-1 ):  # printing stars
         print("* "				,			end="" )
      print()
      for _ in range(n - i + 1				,			0				,			-1 ):  # printing spaces
         print(" "				,			end="" )






def    _A	(   lowerCamelCase ):
   if n <= 0:
      print("       ...       ....        nothing printing :(" )
      return
   floyd(lowerCamelCase )  # upper half
   reverse_floyd(lowerCamelCase )  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE__							:      Dict         =    1
  while K:
    SCREAMING_SNAKE_CASE__							:      str         =    int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE__							:      Tuple         =    int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")

