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 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Dict       ):
	snake_case__			:				Union[str, Any]       =  old_name

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

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

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

			snake_case__			:				int       =  "last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."					,  snake_case_       ):
		snake_case__			:				Tuple       =  old_name.replace("network"					,  "intermediate_stages"       )

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

	return new_name







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Optional[Any]					,  snake_case_ :							Dict       ):
	for key in checkpoint.copy().keys():
		snake_case__			:				Tuple       =  checkpoint.pop(snake_case_       )
		snake_case__			:				List[str]       =  val

	return checkpoint







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

	return image







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							Path					,  snake_case_ :							Path					,  snake_case_ :							Path					,  snake_case_ :							bool       ):
	snake_case__			:				Optional[int]       =  torch.load(snake_case_					,  map_location="cpu"       )["model"]
	snake_case__			:				List[Any]       =  EfficientFormerConfig.from_json_file(snake_case_       )
	snake_case__			:				Tuple       =  EfficientFormerForImageClassificationWithTeacher(snake_case_       )
	snake_case__			:				List[Any]       =  "_".join(checkpoint_path.split("/"       )[-1].split("."       )[0].split("_"       )[:-1]       )

	snake_case__			:				Optional[Any]       =  config.depths[-1] - config.num_metaad_blocks + 1
	snake_case__			:				Dict       =  convert_torch_checkpoint(snake_case_					,  snake_case_       )

	model.load_state_dict(snake_case_       )
	model.eval()

	snake_case__			:				Union[str, Any]       =  {
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

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

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

	assert torch.allclose(snake_case_					,  snake_case_       )

	snake_case__			:				Tuple       =  model(snake_case_       )
	snake_case__			:				int       =  outputs.logits

	snake_case__			:				List[str]       =  (1, 1000)

	if "l1" in model_name:
		snake_case__			:				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]					,  snake_case_					,  atol=1E-3       )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		snake_case__			:				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, :10]					,  snake_case_					,  atol=1E-3       )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		snake_case__			:				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(snake_case_       ).mkdir(exist_ok=snake_case_       )
	model.save_pretrained(snake_case_       )
	print(F'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''       )
	processor.save_pretrained(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=snake_case_					,  )
		processor.push_to_hub(
		    repo_id=F'''Bearnardd/{pytorch_dump_path}'''					,  commit_message="Add image processor"					,  use_temp_dir=snake_case_					,  )


if __name__ == "__main__":
	__lowerCamelCase :      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)

	__lowerCamelCase :      List[str]										=				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 sys

__lowerCamelCase :      List[str]										=				(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							str = N       ):
	snake_case__			:				Any       =  -sys.maxsize - 1
	for i in range(len(snake_case_       ) - 12       ):
		snake_case__			:				Tuple       =  1
		for j in range(13       ):
			product *= int(n[i + j]       )
		if product > largest_product:
			snake_case__			:				Dict       =  product
	return largest_product


if __name__ == "__main__":
	print(f"{solution() = }")



