





"""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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Dict   )					->int:






	'''simple docstring'''






	a :						List[Any]      =							old_name

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

		if layer == "0":
			a :						Union[str, Any]      =							old_name.replace("0"       ,			"convolution1"   )
		elif layer == "1":
			a :						List[str]      =							old_name.replace("1"       ,			"batchnorm_before"   )
		elif layer == "3":
			a :						Union[str, Any]      =							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 :						int      =							R"\b\d{2}\b"
		if bool(re.search(_lowercase       ,			_lowercase   )   ):
			a :						int      =							re.search(R"\d\.\d\d."       ,			_lowercase   ).group()
		else:
			a :						Optional[Any]      =							re.search(R"\d\.\d."       ,			_lowercase   ).group()
		if int(match[0]   ) < 6:
			a :						Tuple      =							old_name.replace(_lowercase       ,			""   )
			a :						Tuple      =							trimmed_name.replace("network"       ,			match[0] + ".meta4D_layers.blocks." + match[2:-1]   )
			a :						Optional[Any]      =							"intermediate_stages." + trimmed_name
		else:
			a :						Tuple      =							old_name.replace(_lowercase       ,			""   )
			if int(match[2]   ) < num_meta4D_last_stage:
				a :						str      =							trimmed_name.replace("network"       ,			"meta4D_layers.blocks." + match[2]   )
			else:
				a :						str      =							str(int(match[2]   ) - num_meta4D_last_stage   )
				a :						Optional[int]      =							trimmed_name.replace("network"       ,			"meta3D_layers.blocks." + layer_index   )
				if "norm1" in old_name:
					a :						Union[str, Any]      =							trimmed_name.replace("norm1"       ,			"layernorm1"   )
				elif "norm2" in old_name:
					a :						Optional[Any]      =							trimmed_name.replace("norm2"       ,			"layernorm2"   )
				elif "fc1" in old_name:
					a :						Tuple      =							trimmed_name.replace("fc1"       ,			"linear_in"   )
				elif "fc2" in old_name:
					a :						Optional[int]      =							trimmed_name.replace("fc2"       ,			"linear_out"   )

			a :						str      =							"last_stage." + trimmed_name

	elif "network" in old_name and re.search(R".\d."       ,			_lowercase   ):
		a :						str      =							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 :						Union[str, Any]      =							new_name.replace("norm1"       ,			"batchnorm_before"   )
	elif ("norm2" in new_name) and ("layernorm2" not in new_name):
		a :						Dict      =							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 :						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 :						Union[str, Any]      =							"efficientformer." + new_name
	elif new_name == "norm.weight" or new_name == "norm.bias":
		a :						int      =							new_name.replace("norm"       ,			"layernorm"   )
		a :						Union[str, Any]      =							"efficientformer." + new_name
	else:
		a :						int      =							"efficientformer.encoder." + new_name

	return new_name






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Dict       ,			_lowercase		:       Any   )					->Optional[int]:






	'''simple docstring'''






	for key in checkpoint.copy().keys():
		a :						List[str]      =							checkpoint.pop(_lowercase   )
		a :						str      =							val

	return checkpoint






def  _SCREAMING_SNAKE_CASE						(      )					->Union[str, Any]:






	'''simple docstring'''






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

	return image






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Path       ,			_lowercase		:       Path       ,			_lowercase		:       Path       ,			_lowercase		:       bool   )					->List[str]:






	'''simple docstring'''






	a :						str      =							torch.load(_lowercase       ,			map_location="cpu"   )["model"]
	a :						Union[str, Any]      =							EfficientFormerConfig.from_json_file(_lowercase   )
	a :						str      =							EfficientFormerForImageClassificationWithTeacher(_lowercase   )
	a :						str      =							"_".join(checkpoint_path.split("/"   )[-1].split("."   )[0].split("_"   )[:-1]   )

	a :						Dict      =							config.depths[-1] - config.num_metaad_blocks + 1
	a :						Dict      =							convert_torch_checkpoint(_lowercase       ,			_lowercase   )

	model.load_state_dict(_lowercase   )
	model.eval()

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

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

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

	assert torch.allclose(_lowercase       ,			_lowercase   )

	a :						Optional[int]      =							model(_lowercase   )
	a :						Dict      =							outputs.logits

	a :						Dict      =							(1, 1000)

	if "l1" in model_name:
		a :						int      =							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 :						int      =							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 :						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		:					Dict	      =     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		:					int	      =     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"""




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a		:					int	      =     numpy.array([0, 0])
a		:					Optional[Any]	      =     numpy.array([0.5, 0.866_0254])
a		:					Tuple	      =     numpy.array([1, 0])
a		:					List[str]	      =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]       ,			_lowercase		:       int   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						List[str]      =							initial_vectors
	for _ in range(_lowercase   ):
		a :						Optional[Any]      =							iteration_step(_lowercase   )
	return vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		a :						str      =							vectors[i + 1]
		new_vectors.append(_lowercase   )
		a :						Optional[int]      =							end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3       ,			60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       numpy.ndarray       ,			_lowercase		:       float   )					->numpy.ndarray:






	'''simple docstring'''






	a :						int      =							numpy.radians(_lowercase   )
	a,			a :						Optional[int]      =							numpy.cos(_lowercase   ), numpy.sin(_lowercase   )
	a :						Dict      =							numpy.array(((c, -s), (s, c))   )
	return numpy.dot(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->None:






	'''simple docstring'''






	a :						Dict      =							plt.gca()
	axes.set_aspect("equal"   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	a,			a :						Any      =							zip(*_lowercase   )
	plt.plot(_lowercase       ,			_lowercase   )
	plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a		:					Optional[int]	      =     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)


