





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_      (	lowercase_   ,		lowercase_	)      ->    Any:
       _snake_case   :				Tuple  					=    old_name

       if "patch_embed" in old_name:
              _snake_case   :				Dict  					=    old_name.split('''.'''	)

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

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

                     _snake_case   :				List[Any]  					=    "last_stage." + trimmed_name

       elif "network" in old_name and re.search(r'''.\d.'''   ,		lowercase_	):
              _snake_case   :				Optional[Any]  					=    old_name.replace('''network'''   ,		'''intermediate_stages'''	)

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

       return new_name




def     A_      (	lowercase_   ,		lowercase_	)      ->    Union[str, Any]:
       for key in checkpoint.copy().keys():
              _snake_case   :				Union[str, Any]  					=    checkpoint.pop(lowercase_	)
              _snake_case   :				Union[str, Any]  					=    val

       return checkpoint




def     A_      (	)      ->    Optional[Any]:
       _snake_case   :				Tuple  					=    "http://images.cocodataset.org/val2017/000000039769.jpg"
       _snake_case   :				str  					=    Image.open(requests.get(lowercase_   ,		stream=lowercase_	).raw	)

       return image




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_	)      ->    Optional[Any]:
       _snake_case   :				Dict  					=    torch.load(lowercase_   ,		map_location='''cpu'''	)["model"]
       _snake_case   :				Tuple  					=    EfficientFormerConfig.from_json_file(lowercase_	)
       _snake_case   :				Union[str, Any]  					=    EfficientFormerForImageClassificationWithTeacher(lowercase_	)
       _snake_case   :				Dict  					=    "_".join(checkpoint_path.split('''/'''	)[-1].split('''.'''	)[0].split('''_'''	)[:-1]	)

       _snake_case   :				Union[str, Any]  					=    config.depths[-1] - config.num_metaad_blocks + 1
       _snake_case   :				int  					=    convert_torch_checkpoint(lowercase_   ,		lowercase_	)

       model.load_state_dict(lowercase_	)
       model.eval()

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

       # prepare image
       _snake_case   :				int  					=    prepare_img()
       _snake_case   :				Optional[Any]  					=    256
       _snake_case   :				Dict  					=    224
       _snake_case   :				Optional[Any]  					=    EfficientFormerImageProcessor(
           size={'''shortest_edge''': image_size}   ,		crop_size={'''height''': crop_size, '''width''': crop_size}   ,		resample=pillow_resamplings['''bicubic''']   ,		)
       _snake_case   :				Optional[Any]  					=    processor(images=lowercase_   ,		return_tensors='''pt'''	).pixel_values

       # original processing pipeline
       _snake_case   :				int  					=    Compose(
           [
               Resize(lowercase_   ,		interpolation=pillow_resamplings['''bicubic''']	),
               CenterCrop(lowercase_	),
               ToTensor(),
               Normalize(lowercase_   ,		lowercase_	),
           ]	)
       _snake_case   :				List[str]  					=    image_transforms(lowercase_	).unsqueeze(0	)

       assert torch.allclose(lowercase_   ,		lowercase_	)

       _snake_case   :				Any  					=    model(lowercase_	)
       _snake_case   :				Union[str, Any]  					=    outputs.logits

       _snake_case   :				int  					=    (1, 1000)

       if "l1" in model_name:
              _snake_case   :				Optional[int]  					=    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]   ,		lowercase_   ,		atol=1E-3	)
              assert logits.shape == expected_shape
       elif "l3" in model_name:
              _snake_case   :				List[Any]  					=    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]   ,		lowercase_   ,		atol=1E-3	)
              assert logits.shape == expected_shape
       elif "l7" in model_name:
              _snake_case   :				Any  					=    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(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__":
   lowerCAmelCase_				= 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_				= 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,
   )









def 			_A	(      SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__							,						UpperCAmelCase__:	int									= [], []
	while len(SCREAMING_SNAKE_CASE      ) > 1:
		UpperCAmelCase__							,						UpperCAmelCase__:	str									= min(SCREAMING_SNAKE_CASE      ), max(SCREAMING_SNAKE_CASE      )
		start.append(SCREAMING_SNAKE_CASE      )
		end.append(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
		collection.remove(SCREAMING_SNAKE_CASE      )
	end.reverse()
	return start + collection + end


if __name__ == "__main__":
					_lowerCAmelCase  :      Union[str, Any]			 =input("""Enter numbers separated by a comma:\n""").strip()
					_lowerCAmelCase  :      Dict			 =[int(item) for item in user_input.split(""",""")]
					print(*merge_sort(unsorted), sep=""",""")