





"""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      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			old_name

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

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

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

																		UpperCamelCase				:		List[str]				   =			"""last_stage.""" + trimmed_name

						elif "network" in old_name and re.search(r""".\d."""							,     SCREAMING_SNAKE_CASE     ):
												UpperCamelCase				:		List[Any]				   =			old_name.replace("""network"""							,     """intermediate_stages"""     )

						if "fc" in new_name:
												UpperCamelCase				:		Optional[int]				   =			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				:		Tuple				   =			new_name.replace("""norm2"""							,     """batchnorm_after"""     )
						if "proj" in new_name:
												UpperCamelCase				:		Tuple				   =			new_name.replace("""proj"""							,     """projection"""     )
						if "dist_head" in new_name:
												UpperCamelCase				:		Any				   =			new_name.replace("""dist_head"""							,     """distillation_classifier"""     )
						elif "head" in new_name:
												UpperCamelCase				:		List[str]				   =			new_name.replace("""head"""							,     """classifier"""     )
						elif "patch_embed" in new_name:
												UpperCamelCase				:		Dict				   =			"""efficientformer.""" + new_name
						elif new_name == "norm.weight" or new_name == "norm.bias":
												UpperCamelCase				:		Dict				   =			new_name.replace("""norm"""							,     """layernorm"""     )
												UpperCamelCase				:		Union[str, Any]				   =			"""efficientformer.""" + new_name
						else:
												UpperCamelCase				:		Tuple				   =			"""efficientformer.encoder.""" + new_name

						return new_name

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						for key in checkpoint.copy().keys():
												UpperCamelCase				:		Optional[int]				   =			checkpoint.pop(SCREAMING_SNAKE_CASE     )
												UpperCamelCase				:		int				   =			val

						return checkpoint

def      UpperCamelCase							():
						UpperCamelCase				:		List[str]				   =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase				:		List[Any]				   =			Image.open(requests.get(SCREAMING_SNAKE_CASE							,     stream=SCREAMING_SNAKE_CASE     ).raw     )

						return image

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

						UpperCamelCase				:		List[str]				   =			config.depths[-1] - config.num_metaad_blocks + 1
						UpperCamelCase				:		Tuple				   =			convert_torch_checkpoint(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

						model.load_state_dict(SCREAMING_SNAKE_CASE     )
						model.eval()

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

						# prepare image
						UpperCamelCase				:		Tuple				   =			prepare_img()
						UpperCamelCase				:		List[Any]				   =			256
						UpperCamelCase				:		Union[str, Any]				   =			224
						UpperCamelCase				:		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				:		Optional[Any]				   =			Compose(
						    [
						        Resize(SCREAMING_SNAKE_CASE							,     interpolation=pillow_resamplings["""bicubic"""]     ),
						        CenterCrop(SCREAMING_SNAKE_CASE     ),
						        ToTensor(),
						        Normalize(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ),
						    ]     )
						UpperCamelCase				:		str				   =			image_transforms(SCREAMING_SNAKE_CASE     ).unsqueeze(0     )

						assert torch.allclose(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

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

						UpperCamelCase				:		Dict				   =			(1, 1000)

						if "l1" in model_name:
												UpperCamelCase				:		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, :10]							,     SCREAMING_SNAKE_CASE							,     atol=1E-3     )
												assert logits.shape == expected_shape
						elif "l3" in model_name:
												UpperCamelCase				:		Optional[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, :10]							,     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__":
		__magic_name__       :					Optional[Any]						=			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)

		__magic_name__       :					Union[str, 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,
		)










"""simple docstring"""


def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			""""""
						for i in table:
												res += inp[i - 1]
						return res

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						return data[1:] + data[0]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Union[str, Any]				   =			""""""
						for i in range(len(SCREAMING_SNAKE_CASE     )     ):
												if a[i] == b[i]:
																		res += "0"
												else:
																		res += "1"
						return res

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			int("""0b""" + data[0] + data[-1]							,     2     )
						UpperCamelCase				:		Any				   =			int("""0b""" + data[1:3]							,     2     )
						return bin(s[row][col]     )[2:]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			message[:4]
						UpperCamelCase				:		int				   =			message[4:]
						UpperCamelCase				:		int				   =			apply_table(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		str				   =			xor(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		Union[str, Any]				   =			apply_sbox(SCREAMING_SNAKE_CASE							,     temp[:4]     )  # noqa: E741
						UpperCamelCase				:		str				   =			apply_sbox(SCREAMING_SNAKE_CASE							,     temp[4:]     )
						UpperCamelCase				:		Union[str, Any]				   =			"""0""" * (2 - len(SCREAMING_SNAKE_CASE     )) + l  # noqa: E741
						UpperCamelCase				:		Union[str, Any]				   =			"""0""" * (2 - len(SCREAMING_SNAKE_CASE     )) + r
						UpperCamelCase				:		int				   =			apply_table(l + r							,     SCREAMING_SNAKE_CASE     )
						UpperCamelCase				:		int				   =			xor(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )
						return temp + right


if __name__ == "__main__":
		__magic_name__       :					Union[str, Any]						=			input("""Enter 10 bit key: """)
		__magic_name__       :					Union[str, Any]						=			input("""Enter 8 bit message: """)

		__magic_name__       :					Dict						=			[6, 3, 7, 4, 8, 5, 1_0, 9]
		__magic_name__       :					str						=			[3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
		__magic_name__       :					Optional[Any]						=			[2, 4, 3, 1]
		__magic_name__       :					Tuple						=			[2, 6, 3, 1, 4, 8, 5, 7]
		__magic_name__       :					str						=			[4, 1, 3, 5, 7, 2, 8, 6]
		__magic_name__       :					str						=			[4, 1, 2, 3, 2, 3, 4, 1]
		__magic_name__       :					Any						=			[[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
		__magic_name__       :					Any						=			[[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]

		# key generation
		__magic_name__       :					Dict						=			apply_table(key, paa_table)
		__magic_name__       :					Union[str, Any]						=			temp[:5]
		__magic_name__       :					Optional[Any]						=			temp[5:]
		__magic_name__       :					str						=			left_shift(left)
		__magic_name__       :					List[str]						=			left_shift(right)
		__magic_name__       :					Tuple						=			apply_table(left + right, pa_table)
		__magic_name__       :					Dict						=			left_shift(left)
		__magic_name__       :					Union[str, Any]						=			left_shift(right)
		__magic_name__       :					List[Any]						=			left_shift(left)
		__magic_name__       :					str						=			left_shift(right)
		__magic_name__       :					List[str]						=			apply_table(left + right, pa_table)

		# encryption
		__magic_name__       :					List[str]						=			apply_table(message, IP)
		__magic_name__       :					str						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					str						=			temp[4:] + temp[:4]
		__magic_name__       :					Optional[Any]						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Any						=			apply_table(temp, IP_inv)
		print("""Cipher text is:""", CT)

		# decryption
		__magic_name__       :					Tuple						=			apply_table(CT, IP)
		__magic_name__       :					List[str]						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Dict						=			temp[4:] + temp[:4]
		__magic_name__       :					int						=			function(expansion, sa, sa, keya, temp)
		__magic_name__       :					Any						=			apply_table(temp, IP_inv)
		print("""Plain text after decypting is:""", PT)



