




"""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     a__ (				snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					old_name

			if "patch_embed" in old_name:
						lowerCamelCase			      =					old_name.split("""."""				)

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

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

									lowerCamelCase			      =					'''last_stage.''' + trimmed_name

			elif "network" in old_name and re.search(R""".\d."""       , __snake_case				):
						lowerCamelCase			      =					old_name.replace("""network"""       , """intermediate_stages"""				)

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

			return new_name



def     a__ (				snake_case__       , snake_case__				)						->					List[str]:
			for key in checkpoint.copy().keys():
						lowerCamelCase			      =					checkpoint.pop(__snake_case				)
						lowerCamelCase			      =					val

			return checkpoint



def     a__ (				)						->					Optional[int]:
			lowerCamelCase			      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
			lowerCamelCase			      =					Image.open(requests.get(__snake_case       , stream=__snake_case				).raw				)

			return image



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					torch.load(__snake_case       , map_location="""cpu"""				)['''model''']
			lowerCamelCase			      =					EfficientFormerConfig.from_json_file(__snake_case				)
			lowerCamelCase			      =					EfficientFormerForImageClassificationWithTeacher(__snake_case				)
			lowerCamelCase			      =					'''_'''.join(checkpoint_path.split("""/"""				)[-1].split("""."""				)[0].split("""_"""				)[:-1]				)

			lowerCamelCase			      =					config.depths[-1] - config.num_metaad_blocks + 1
			lowerCamelCase			      =					convert_torch_checkpoint(__snake_case       , __snake_case				)

			model.load_state_dict(__snake_case				)
			model.eval()

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

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

			# original processing pipeline
			lowerCamelCase			      =					Compose(
			    [
			        Resize(__snake_case       , interpolation=pillow_resamplings["""bicubic"""]				),
			        CenterCrop(__snake_case				),
			        ToTensor(),
			        Normalize(__snake_case       , __snake_case				),
			    ]				)
			lowerCamelCase			      =					image_transforms(__snake_case				).unsqueeze(0				)

			assert torch.allclose(__snake_case       , __snake_case				)

			lowerCamelCase			      =					model(__snake_case				)
			lowerCamelCase			      =					outputs.logits

			lowerCamelCase			      =					(1, 10_00)

			if "l1" in model_name:
						lowerCamelCase			      =					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]       , __snake_case       , atol=1E-3				)
						assert logits.shape == expected_shape
			elif "l3" in model_name:
						lowerCamelCase			      =					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]       , __snake_case       , atol=1E-3				)
						assert logits.shape == expected_shape
			elif "l7" in model_name:
						lowerCamelCase			      =					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(__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							:			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)

					lowerCAmelCase							:			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 typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



