

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_:				List[str]					,   lowercase_:				Optional[int]      )  ->   Optional[int]:
	A__							:					Optional[int]       =	old_name

	if "patch_embed" in old_name:
		A__     ,     A__     ,     A__							:					Optional[int]       =	old_name.split("""."""      )

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

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

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

	elif "network" in old_name and re.search(r""".\d."""					,   UpperCAmelCase__      ):
		A__							:					Dict       =	old_name.replace("""network"""					,   """intermediate_stages"""      )

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

	return new_name

def 		UpperCamelCase      (lowercase_:				str					,   lowercase_:				List[Any]      )  ->   Tuple:






	for key in checkpoint.copy().keys():
		A__							:					Optional[int]       =	checkpoint.pop(UpperCAmelCase__      )
		A__							:					Any       =	val

	return checkpoint

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

	return image

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

	A__							:					Optional[Any]       =	config.depths[-1] - config.num_metaad_blocks + 1
	A__							:					Any       =	convert_torch_checkpoint(UpperCAmelCase__					,   UpperCAmelCase__      )

	model.load_state_dict(UpperCAmelCase__      )
	model.eval()

	A__							:					List[str]       =	{
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

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

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

	assert torch.allclose(UpperCAmelCase__					,   UpperCAmelCase__      )

	A__							:					Union[str, Any]       =	model(UpperCAmelCase__      )
	A__							:					Optional[Any]       =	outputs.logits

	A__							:					Tuple       =	(1, 1000)

	if "l1" in model_name:
		A__							:					Union[str, Any]       =	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]					,   UpperCAmelCase__					,   atol=1E-3      )
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A__							:					List[str]       =	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]					,   UpperCAmelCase__					,   atol=1E-3      )
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A__							:					Optional[int]       =	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(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
	model.save_pretrained(UpperCAmelCase__      )
	print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""      )
	processor.save_pretrained(UpperCAmelCase__      )
	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=UpperCAmelCase__					,   )
		processor.push_to_hub(
		    repo_id=f"""Bearnardd/{pytorch_dump_path}"""					,   commit_message="""Add image processor"""					,   use_temp_dir=UpperCAmelCase__					,   )


if __name__ == "__main__":
				A_ :				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_ :				Optional[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,
				)






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	0
			for cola in range(col + 1					,   size + 1      ):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   size + 1      ):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]					,   10      )] for row in range(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

	def interpolated_func(lowercase_:				int      ) -> int:
		return sum(
		    round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase_      )      )

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


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



