




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    (			UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Dict		=					old_name

			if "patch_embed" in old_name:
						__UpperCAmelCase	: Union[str, Any]		=					old_name.split("."							)

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

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

									__UpperCAmelCase	: Union[str, Any]		=					"last_stage." + trimmed_name

			elif "network" in old_name and re.search(R".\d."				,       UpperCamelCase							):
						__UpperCAmelCase	: Tuple		=					old_name.replace("network"				,       "intermediate_stages"							)

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

			return new_name

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			Optional[Any]:


			"""simple docstring"""
			for key in checkpoint.copy().keys():
						__UpperCAmelCase	: int		=					checkpoint.pop(UpperCamelCase							)
						__UpperCAmelCase	: int		=					val

			return checkpoint

def 					_UpperCamelCase    (			)			->			Optional[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: int		=					"http://images.cocodataset.org/val2017/000000039769.jpg"
			__UpperCAmelCase	: str		=					Image.open(requests.get(UpperCamelCase				,       stream=UpperCamelCase							).raw							)

			return image

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			List[Any]:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					torch.load(UpperCamelCase				,       map_location="cpu"							)["model"]
			__UpperCAmelCase	: List[str]		=					EfficientFormerConfig.from_json_file(UpperCamelCase							)
			__UpperCAmelCase	: List[Any]		=					EfficientFormerForImageClassificationWithTeacher(UpperCamelCase							)
			__UpperCAmelCase	: List[Any]		=					"_".join(checkpoint_path.split("/"							)[-1].split("."							)[0].split("_"							)[:-1]							)

			__UpperCAmelCase	: Dict		=					config.depths[-1] - config.num_metaad_blocks + 1
			__UpperCAmelCase	: Dict		=					convert_torch_checkpoint(UpperCamelCase				,       UpperCamelCase							)

			model.load_state_dict(UpperCamelCase							)
			model.eval()

			__UpperCAmelCase	: Union[str, Any]		=					{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

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

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

			assert torch.allclose(UpperCamelCase				,       UpperCamelCase							)

			__UpperCAmelCase	: Tuple		=					model(UpperCamelCase							)
			__UpperCAmelCase	: Optional[int]		=					outputs.logits

			__UpperCAmelCase	: int		=					(1, 1000)

			if "l1" in model_name:
						__UpperCAmelCase	: List[Any]		=					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]				,       UpperCamelCase				,       atol=1e-3							)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__UpperCAmelCase	: Tuple		=					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]				,       UpperCamelCase				,       atol=1e-3							)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__UpperCAmelCase	: Optional[int]		=					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(UpperCamelCase							).mkdir(exist_ok=UpperCamelCase							)
			model.save_pretrained(UpperCamelCase							)
			print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"							)
			processor.save_pretrained(UpperCamelCase							)
			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=UpperCamelCase				,       )
						processor.push_to_hub(
						    repo_id=f"Bearnardd/{pytorch_dump_path}"				,       commit_message="Add image processor"				,       use_temp_dir=UpperCamelCase				,       )


if __name__ == "__main__":
					A   =       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)

					A   =       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"""





def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__UpperCAmelCase	: Union[str, Any]		=					_modexpt(UpperCamelCase				,       exponent // 2				,       UpperCamelCase							) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(UpperCamelCase				,       exponent - 1				,       UpperCamelCase							)) % modulo_value

def 					_UpperCamelCase    (			UpperCamelCase = 1777				,       UpperCamelCase = 1855				,       UpperCamelCase = 8							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					base
			for _ in range(1				,       UpperCamelCase							):
						__UpperCAmelCase	: str		=					_modexpt(UpperCamelCase				,       UpperCamelCase				,       10**digits							)

			return result


if __name__ == "__main__":
					print(f'''{solution() = }''')


