




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    )      ->       List[str]:
	a										=							old_name

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

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

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

			a										=							"""last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(R""".\d."""							,			__lowerCamelCase    ):
		a										=							old_name.replace("""network"""							,			"""intermediate_stages"""    )

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

	return new_name





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for key in checkpoint.copy().keys():
		a										=							checkpoint.pop(__lowerCamelCase    )
		a										=							val

	return checkpoint





def 		__A		(	)      ->       Union[str, Any]:
	a										=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a										=							Image.open(requests.get(__lowerCamelCase							,			stream=__lowerCamelCase    ).raw    )

	return image





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       int:
	a										=							torch.load(__lowerCamelCase							,			map_location="""cpu"""    )["""model"""]
	a										=							EfficientFormerConfig.from_json_file(__lowerCamelCase    )
	a										=							EfficientFormerForImageClassificationWithTeacher(__lowerCamelCase    )
	a										=							"""_""".join(checkpoint_path.split("""/"""    )[-1].split("""."""    )[0].split("""_"""    )[:-1]    )

	a										=							config.depths[-1] - config.num_metaad_blocks + 1
	a										=							convert_torch_checkpoint(__lowerCamelCase							,			__lowerCamelCase    )

	model.load_state_dict(__lowerCamelCase    )
	model.eval()

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

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

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

	assert torch.allclose(__lowerCamelCase							,			__lowerCamelCase    )

	a										=							model(__lowerCamelCase    )
	a										=							outputs.logits

	a										=							(1, 1000)

	if "l1" in model_name:
		a										=							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										=							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										=							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__":
		__UpperCamelCase					:       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)

		__UpperCamelCase					:       int    					=      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,
		)







from math import pi, sqrt





def 		__A		(	__lowerCamelCase    )      ->       float:
	if num <= 0:
		raise ValueError("""math domain error"""    )
	if num > 171.5:
		raise OverflowError("""math range error"""    )
	elif num - int(__lowerCamelCase    ) not in (0, 0.5):
		raise NotImplementedError("""num must be an integer or a half-integer"""    )
	elif num == 0.5:
		return sqrt(__lowerCamelCase    )
	else:
		return 1.0 if num == 1 else (num - 1) * gamma(num - 1    )





def 		__A		(	)      ->       None:
	assert gamma(0.5    ) == sqrt(__lowerCamelCase    )
	assert gamma(1    ) == 1.0
	assert gamma(2    ) == 1.0


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__UpperCamelCase					:       str    					=      1.0
		while num:
				__UpperCamelCase					:       Dict    					=      float(input("Gamma of: "))
				print(F'gamma({num}) = {gamma(num)}')
				print("\nEnter 0 to exit...")

