


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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   old_name

			if "patch_embed" in old_name:
						__lowercase					, __lowercase					, __lowercase 					=   old_name.split('''.'''  )

						if layer == "0":
									__lowercase 					=   old_name.replace('''0'''       ,			'''convolution1'''  )
						elif layer == "1":
									__lowercase 					=   old_name.replace('''1'''       ,			'''batchnorm_before'''  )
						elif layer == "3":
									__lowercase 					=   old_name.replace('''3'''       ,			'''convolution2'''  )
						else:
									__lowercase 					=   old_name.replace('''4'''       ,			'''batchnorm_after'''  )

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

									__lowercase 					=   '''last_stage.''' + trimmed_name

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

			if "fc" in new_name:
						__lowercase 					=   new_name.replace('''fc'''       ,			'''convolution'''  )
			elif ("norm1" in new_name) and ("layernorm1" not in new_name):
						__lowercase 					=   new_name.replace('''norm1'''       ,			'''batchnorm_before'''  )
			elif ("norm2" in new_name) and ("layernorm2" not in new_name):
						__lowercase 					=   new_name.replace('''norm2'''       ,			'''batchnorm_after'''  )
			if "proj" in new_name:
						__lowercase 					=   new_name.replace('''proj'''       ,			'''projection'''  )
			if "dist_head" in new_name:
						__lowercase 					=   new_name.replace('''dist_head'''       ,			'''distillation_classifier'''  )
			elif "head" in new_name:
						__lowercase 					=   new_name.replace('''head'''       ,			'''classifier'''  )
			elif "patch_embed" in new_name:
						__lowercase 					=   '''efficientformer.''' + new_name
			elif new_name == "norm.weight" or new_name == "norm.bias":
						__lowercase 					=   new_name.replace('''norm'''       ,			'''layernorm'''  )
						__lowercase 					=   '''efficientformer.''' + new_name
			else:
						__lowercase 					=   '''efficientformer.encoder.''' + new_name

			return new_name


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			for key in checkpoint.copy().keys():
						__lowercase 					=   checkpoint.pop(a__  )
						__lowercase 					=   val

			return checkpoint


def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase 					=   Image.open(requests.get(a__       ,			stream=a__  ).raw  )

			return image


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   torch.load(a__       ,			map_location='''cpu'''  )['''model''']
			__lowercase 					=   EfficientFormerConfig.from_json_file(a__  )
			__lowercase 					=   EfficientFormerForImageClassificationWithTeacher(a__  )
			__lowercase 					=   '''_'''.join(checkpoint_path.split('''/'''  )[-1].split('''.'''  )[0].split('''_'''  )[:-1]  )

			__lowercase 					=   config.depths[-1] - config.num_metaad_blocks + 1
			__lowercase 					=   convert_torch_checkpoint(a__       ,			a__  )

			model.load_state_dict(a__  )
			model.eval()

			__lowercase 					=   {
			    '''bilinear''': PILImageResampling.BILINEAR,
			    '''bicubic''': PILImageResampling.BICUBIC,
			    '''nearest''': PILImageResampling.NEAREST,
			}

			# prepare image
			__lowercase 					=   prepare_img()
			__lowercase 					=   2_56
			__lowercase 					=   2_24
			__lowercase 					=   EfficientFormerImageProcessor(
			    size={'''shortest_edge''': image_size}       ,			crop_size={'''height''': crop_size, '''width''': crop_size}       ,			resample=pillow_resamplings['''bicubic''']       ,			)
			__lowercase 					=   processor(images=a__       ,			return_tensors='''pt'''  ).pixel_values

			# original processing pipeline
			__lowercase 					=   Compose(
			    [
			        Resize(a__       ,			interpolation=pillow_resamplings['''bicubic''']  ),
			        CenterCrop(a__  ),
			        ToTensor(),
			        Normalize(a__       ,			a__  ),
			    ]  )
			__lowercase 					=   image_transforms(a__  ).unsqueeze(0  )

			assert torch.allclose(a__       ,			a__  )

			__lowercase 					=   model(a__  )
			__lowercase 					=   outputs.logits

			__lowercase 					=   (1, 10_00)

			if "l1" in model_name:
						__lowercase 					=   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]       ,			a__       ,			atol=1E-3  )
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						__lowercase 					=   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]       ,			a__       ,			atol=1E-3  )
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						__lowercase 					=   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(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__":
	a : List[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)

	a : Dict		=				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,
	)






def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return price * (1 + tax_rate)


if __name__ == "__main__":
	print(f'''{price_plus_tax(100, 0.25) = }''')
	print(f'''{price_plus_tax(125.50, 0.05) = }''')


