

"""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  (      A_						,     A_ ):
	lowerCAmelCase__	:						List[str]  				=	old_name

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

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

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

			lowerCAmelCase__	:						Any  				=	'''last_stage.''' + trimmed_name

	elif "network" in old_name and re.search(r'''.\d.'''						,     A_ ):
		lowerCAmelCase__	:						Tuple  				=	old_name.replace('''network'''						,     '''intermediate_stages''' )

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

	return new_name

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	for key in checkpoint.copy().keys():
		lowerCAmelCase__	:						List[Any]  				=	checkpoint.pop(A_ )
		lowerCAmelCase__	:						List[str]  				=	val

	return checkpoint

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Optional[int]  				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
	lowerCAmelCase__	:						Union[str, Any]  				=	Image.open(requests.get(A_						,     stream=A_ ).raw )

	return image

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Tuple  				=	torch.load(A_						,     map_location='''cpu''' )['''model''']
	lowerCAmelCase__	:						int  				=	EfficientFormerConfig.from_json_file(A_ )
	lowerCAmelCase__	:						Tuple  				=	EfficientFormerForImageClassificationWithTeacher(A_ )
	lowerCAmelCase__	:						Dict  				=	'''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )

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

	model.load_state_dict(A_ )
	model.eval()

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

	# prepare image
	lowerCAmelCase__	:						Union[str, Any]  				=	prepare_img()
	lowerCAmelCase__	:						Union[str, Any]  				=	2_56
	lowerCAmelCase__	:						List[str]  				=	2_24
	lowerCAmelCase__	:						int  				=	EfficientFormerImageProcessor(
	    size={'''shortest_edge''': image_size}						,     crop_size={'''height''': crop_size, '''width''': crop_size}						,     resample=pillow_resamplings['''bicubic''']						,     )
	lowerCAmelCase__	:						Optional[int]  				=	processor(images=A_						,     return_tensors='''pt''' ).pixel_values

	# original processing pipeline
	lowerCAmelCase__	:						Optional[int]  				=	Compose(
	    [
	        Resize(A_						,     interpolation=pillow_resamplings['''bicubic'''] ),
	        CenterCrop(A_ ),
	        ToTensor(),
	        Normalize(A_						,     A_ ),
	    ] )
	lowerCAmelCase__	:						List[str]  				=	image_transforms(A_ ).unsqueeze(0 )

	assert torch.allclose(A_						,     A_ )

	lowerCAmelCase__	:						Dict  				=	model(A_ )
	lowerCAmelCase__	:						int  				=	outputs.logits

	lowerCAmelCase__	:						Tuple  				=	(1, 10_00)

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


if __name__ == "__main__":
							__UpperCamelCase  :      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)

							__UpperCamelCase  :      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,
							)











from __future__ import annotations



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






      if index == len(lowerCamelCase ):
            print(lowerCamelCase )
            return

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





