




"""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			:					int      ,       snake_case			:					Any    ):
					_lowerCAmelCase:Optional[int]									=  old_name

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

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

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

															_lowerCAmelCase:Tuple									=  '''last_stage.''' + trimmed_name

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

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

					return new_name





def 							UpperCAmelCase		(		snake_case			:					Optional[Any]      ,       snake_case			:					List[str]    ):
					for key in checkpoint.copy().keys():
										_lowerCAmelCase:Any									=  checkpoint.pop(snake_case    )
										_lowerCAmelCase:int									=  val

					return checkpoint





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Union[str, Any]									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Union[str, Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )

					return image





def 							UpperCAmelCase		(		snake_case			:					Path      ,       snake_case			:					Path      ,       snake_case			:					Path      ,       snake_case			:					bool    ):
					_lowerCAmelCase:List[Any]									=  torch.load(snake_case      ,       map_location='''cpu'''    )['''model''']
					_lowerCAmelCase:Dict									=  EfficientFormerConfig.from_json_file(snake_case    )
					_lowerCAmelCase:Optional[Any]									=  EfficientFormerForImageClassificationWithTeacher(snake_case    )
					_lowerCAmelCase:int									=  '''_'''.join(checkpoint_path.split('''/'''    )[-1].split('''.'''    )[0].split('''_'''    )[:-1]    )

					_lowerCAmelCase:Union[str, Any]									=  config.depths[-1] - config.num_metaad_blocks + 1
					_lowerCAmelCase:Optional[int]									=  convert_torch_checkpoint(snake_case      ,       snake_case    )

					model.load_state_dict(snake_case    )
					model.eval()

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

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

					# original processing pipeline
					_lowerCAmelCase:Dict									=  Compose(
					    [
					        Resize(snake_case      ,       interpolation=pillow_resamplings['''bicubic''']    ),
					        CenterCrop(snake_case    ),
					        ToTensor(),
					        Normalize(snake_case      ,       snake_case    ),
					    ]    )
					_lowerCAmelCase:str									=  image_transforms(snake_case    ).unsqueeze(0    )

					assert torch.allclose(snake_case      ,       snake_case    )

					_lowerCAmelCase:Optional[int]									=  model(snake_case    )
					_lowerCAmelCase:Union[str, Any]									=  outputs.logits

					_lowerCAmelCase:Optional[Any]									=  (1, 1000)

					if "l1" in model_name:
										_lowerCAmelCase:Optional[Any]									=  torch.Tensor(
										    [-0.13_12, 0.43_53, -1.04_99, -0.51_24, 0.41_83, -0.67_93, -1.37_77, -0.08_93, -0.73_58, -2.43_28]    )
										assert torch.allclose(logits[0, :10]      ,       snake_case      ,       atol=1e-3    )
										assert logits.shape == expected_shape
					elif "l3" in model_name:
										_lowerCAmelCase:str									=  torch.Tensor(
										    [-1.31_50, -1.54_56, -1.25_56, -0.84_96, -0.71_27, -0.78_97, -0.97_28, -0.30_52, 0.37_51, -0.31_27]    )
										assert torch.allclose(logits[0, :10]      ,       snake_case      ,       atol=1e-3    )
										assert logits.shape == expected_shape
					elif "l7" in model_name:
										_lowerCAmelCase:List[str]									=  torch.Tensor(
										    [-1.02_83, -1.41_31, -0.56_44, -1.31_15, -0.57_85, -1.20_49, -0.75_28, 0.19_92, -0.38_22, -0.08_78]    )
										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__":
				UpperCamelCase__						=			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__						=			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"""






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__						=			logging.get_logger(__name__)




class   a__			(       UpperCamelCase_  ):





						def __init__(							self      :	Dict     ,*a__      :	List[str]     ,**a__      :	str)						->       None:




											"""simple docstring"""







											warnings.warn(
											    '''The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
											    ''' Please use PoolFormerImageProcessor instead.'''     ,a__     ,)
											super().__init__(*a__     ,**a__)






