

"""simple docstring"""




import string

def 						UpperCamelCase (lowercase_:			str )					->				str:
	A__							:   List[Any]					       =		""""""
	for i in sequence:
		A__							:   Tuple					       =		ord(lowercase_ )
		if 65 <= extract <= 90:
			output += chr(155 - extract )
		elif 97 <= extract <= 122:
			output += chr(219 - extract )
		else:
			output += i
	return output

def 						UpperCamelCase (lowercase_:			str )					->				str:
	A__							:   Any					       =		string.ascii_letters
	A__							:   Optional[Any]					       =		string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
	return "".join(
	    letters_reversed[letters.index(lowercase_ )] if c in letters else c for c in sequence )

def 						UpperCamelCase ()					->				None:
	from timeit import timeit

	print("""Running performance benchmarks...""" )
	A__							:   Union[str, Any]					       =		"""from string import printable ; from __main__ import atbash, atbash_slow"""
	print(f"""> atbash_slow(): {timeit('atbash_slow(printable)'   ,				setup=lowercase_ )} seconds""" )
	print(f""">      atbash(): {timeit('atbash(printable)'   ,				setup=lowercase_ )} seconds""" )


if __name__ == "__main__":
	for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
		print(f'''{example} encrypted in atbash: {atbash(example)}''')
	benchmark()






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 (lowercase_:			str   ,				lowercase_:			Optional[int] )					->				str:
	A__							:   Union[str, Any]					       =		old_name

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

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

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

			A__							:   str					       =		"""last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(r""".\d."""   ,				lowercase_ ):
		A__							:   List[str]					       =		old_name.replace("""network"""   ,				"""intermediate_stages""" )

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

	return new_name

def 						UpperCamelCase (lowercase_:			Optional[Any]   ,				lowercase_:			Union[str, Any] )					->				Tuple:
	for key in checkpoint.copy().keys():
		A__							:   List[Any]					       =		checkpoint.pop(lowercase_ )
		A__							:   Dict					       =		val

	return checkpoint

def 						UpperCamelCase ()					->				Optional[int]:
	A__							:   List[str]					       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	A__							:   List[str]					       =		Image.open(requests.get(lowercase_   ,				stream=lowercase_ ).raw )

	return image

def 						UpperCamelCase (lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			Path   ,				lowercase_:			bool )					->				Tuple:
	A__							:   Any					       =		torch.load(lowercase_   ,				map_location="""cpu""" )["""model"""]
	A__							:   List[Any]					       =		EfficientFormerConfig.from_json_file(lowercase_ )
	A__							:   Any					       =		EfficientFormerForImageClassificationWithTeacher(lowercase_ )
	A__							:   List[str]					       =		"""_""".join(checkpoint_path.split("""/""" )[-1].split(""".""" )[0].split("""_""" )[:-1] )

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

	model.load_state_dict(lowercase_ )
	model.eval()

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

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

	# original processing pipeline
	A__							:   List[Any]					       =		Compose(
	    [
	        Resize(lowercase_   ,				interpolation=pillow_resamplings["""bicubic"""] ),
	        CenterCrop(lowercase_ ),
	        ToTensor(),
	        Normalize(lowercase_   ,				lowercase_ ),
	    ] )
	A__							:   Any					       =		image_transforms(lowercase_ ).unsqueeze(0 )

	assert torch.allclose(lowercase_   ,				lowercase_ )

	A__							:   Optional[int]					       =		model(lowercase_ )
	A__							:   List[str]					       =		outputs.logits

	A__							:   Tuple					       =		(1, 1000)

	if "l1" in model_name:
		A__							:   List[str]					       =		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]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A__							:   Any					       =		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]   ,				lowercase_   ,				atol=1E-3 )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A__							:   Union[str, Any]					       =		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(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__":
	A_	:			List[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)

	A_	:			List[Any]  	=      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,
	)



