






"""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	(		snake_case__   ,   snake_case__					):
	A_		:						List[Any]	    =   old_name

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

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

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

			A_		:						Union[str, Any]	    =   """last_stage.""" + trimmed_name

	elif "network" in old_name and re.search(R""".\d."""   ,   snake_case__					):
		A_		:						List[Any]	    =   old_name.replace("""network"""   ,   """intermediate_stages"""					)

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

	return new_name






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	for key in checkpoint.copy().keys():
		A_		:						Tuple	    =   checkpoint.pop(snake_case__					)
		A_		:						Dict	    =   val

	return checkpoint






def 			__UpperCamelCase	(		):
	A_		:						int	    =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
	A_		:						Dict	    =   Image.open(requests.get(snake_case__   ,   stream=snake_case__					).raw					)

	return image






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):
	A_		:						Any	    =   torch.load(snake_case__   ,   map_location="""cpu"""					)["""model"""]
	A_		:						int	    =   EfficientFormerConfig.from_json_file(snake_case__					)
	A_		:						Union[str, Any]	    =   EfficientFormerForImageClassificationWithTeacher(snake_case__					)
	A_		:						List[Any]	    =   """_""".join(checkpoint_path.split("""/"""					)[-1].split("""."""					)[0].split("""_"""					)[:-1]					)

	A_		:						Dict	    =   config.depths[-1] - config.num_metaad_blocks + 1
	A_		:						Tuple	    =   convert_torch_checkpoint(snake_case__   ,   snake_case__					)

	model.load_state_dict(snake_case__					)
	model.eval()

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

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

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

	assert torch.allclose(snake_case__   ,   snake_case__					)

	A_		:						Union[str, Any]	    =   model(snake_case__					)
	A_		:						Optional[Any]	    =   outputs.logits

	A_		:						int	    =   (1, 1_000)

	if "l1" in model_name:
		A_		:						Any	    =   torch.Tensor(
		    [-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328]					)
		assert torch.allclose(logits[0, :10]   ,   snake_case__   ,   atol=1E-3					)
		assert logits.shape == expected_shape
	elif "l3" in model_name:
		A_		:						str	    =   torch.Tensor(
		    [-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127]					)
		assert torch.allclose(logits[0, :10]   ,   snake_case__   ,   atol=1E-3					)
		assert logits.shape == expected_shape
	elif "l7" in model_name:
		A_		:						Optional[Any]	    =   torch.Tensor(
		    [-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878]					)
		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         =							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         =							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	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []

