

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	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= old_name

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

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

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

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

	elif "network" in old_name and re.search(R".\d."	,SCREAMING_SNAKE_CASE      ):
		UpperCAmelCase__:	Optional[Any]									= old_name.replace("network"	,"intermediate_stages"      )

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

	return new_name



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	for key in checkpoint.copy().keys():
		UpperCAmelCase__:	Union[str, Any]									= checkpoint.pop(SCREAMING_SNAKE_CASE      )
		UpperCAmelCase__:	Union[str, Any]									= val

	return checkpoint



def 			_A	(      ):
	UpperCAmelCase__:	Tuple									= "http://images.cocodataset.org/val2017/000000039769.jpg"
	UpperCAmelCase__:	str									= Image.open(requests.get(SCREAMING_SNAKE_CASE	,stream=SCREAMING_SNAKE_CASE      ).raw      )

	return image



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Dict									= torch.load(SCREAMING_SNAKE_CASE	,map_location="cpu"      )["model"]
	UpperCAmelCase__:	Tuple									= EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Union[str, Any]									= EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Dict									= "_".join(checkpoint_path.split("/"      )[-1].split("."      )[0].split("_"      )[:-1]      )

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

	model.load_state_dict(SCREAMING_SNAKE_CASE      )
	model.eval()

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

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

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

	assert torch.allclose(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )

	UpperCAmelCase__:	Any									= model(SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Union[str, Any]									= outputs.logits

	UpperCAmelCase__:	int									= (1, 1_0_0_0)

	if "l1" in model_name:
		UpperCAmelCase__:	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, :1_0]	,SCREAMING_SNAKE_CASE	,atol=1e-3      )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		UpperCAmelCase__:	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, :1_0]	,SCREAMING_SNAKE_CASE	,atol=1e-3      )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		UpperCAmelCase__:	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(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__":
					_lowerCAmelCase  :      Tuple			 =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  :      Tuple			 =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,
					)


import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes