





"""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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	old_name

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

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

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

						UpperCamelCase   =	"last_stage." + trimmed_name

		elif "network" in old_name and re.search(r".\d."  ,	_SCREAMING_SNAKE_CASE    ):
				UpperCamelCase   =	old_name.replace("network"  ,	"intermediate_stages"    )

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

		return new_name


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in checkpoint.copy().keys():
				UpperCamelCase   =	checkpoint.pop(_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	val

		return checkpoint


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	"http://images.cocodataset.org/val2017/000000039769.jpg"
		UpperCamelCase   =	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    ):




		"""simple docstring"""


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

		UpperCamelCase   =	config.depths[-1] - config.num_metaad_blocks + 1
		UpperCamelCase   =	convert_torch_checkpoint(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		model.load_state_dict(_SCREAMING_SNAKE_CASE    )
		model.eval()

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

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

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

		assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	model(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	outputs.logits

		UpperCamelCase   =	(1, 1_000)

		if "l1" in model_name:
				UpperCamelCase   =	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]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1e-3    )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				UpperCamelCase   =	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]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1e-3    )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				UpperCamelCase   =	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__     			=			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,
						)













"""simple docstring"""





from diffusers.utils.testing_utils import require_onnxruntime





@require_onnxruntime
class        _lowerCamelCase      :
						pass






