



'''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        lowerCAmelCase_     (			_lowerCamelCase:     List[Any] ,   _lowerCamelCase:     str      ):
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	old_name

		if "patch_embed" in old_name:
				__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE ,		__SCREAMING_SNAKE_CASE :	Any					 =	old_name.split("""."""      )

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

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

						__SCREAMING_SNAKE_CASE :	Tuple					 =	"""last_stage.""" + trimmed_name

		elif "network" in old_name and re.search(r""".\d.""" ,   _lowerCamelCase      ):
				__SCREAMING_SNAKE_CASE :	Dict					 =	old_name.replace("""network""" ,   """intermediate_stages"""      )

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

		return new_name


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     str      ):
		for key in checkpoint.copy().keys():
				__SCREAMING_SNAKE_CASE :	str					 =	checkpoint.pop(_lowerCamelCase      )
				__SCREAMING_SNAKE_CASE :	List[str]					 =	val

		return checkpoint


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
		__SCREAMING_SNAKE_CASE :	str					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      )

		return image


def        lowerCAmelCase_     (			_lowerCamelCase:     Path ,   _lowerCamelCase:     Path ,   _lowerCamelCase:     Path ,   _lowerCamelCase:     bool      ):
		__SCREAMING_SNAKE_CASE :	List[str]					 =	torch.load(_lowerCamelCase ,   map_location="""cpu"""      )["""model"""]
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	EfficientFormerConfig.from_json_file(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	EfficientFormerForImageClassificationWithTeacher(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	"""_""".join(checkpoint_path.split("""/"""      )[-1].split("""."""      )[0].split("""_"""      )[:-1]      )

		__SCREAMING_SNAKE_CASE :	Any					 =	config.depths[-1] - config.num_metaad_blocks + 1
		__SCREAMING_SNAKE_CASE :	int					 =	convert_torch_checkpoint(_lowerCamelCase ,   _lowerCamelCase      )

		model.load_state_dict(_lowerCamelCase      )
		model.eval()

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	{
		    """bilinear""": PILImageResampling.BILINEAR,
		    """bicubic""": PILImageResampling.BICUBIC,
		    """nearest""": PILImageResampling.NEAREST,
		}

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

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

		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	str					 =	model(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	outputs.logits

		__SCREAMING_SNAKE_CASE :	List[str]					 =	(1, 10_00)

		if "l1" in model_name:
				__SCREAMING_SNAKE_CASE :	str					 =	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] ,   _lowerCamelCase ,   atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				__SCREAMING_SNAKE_CASE :	Any					 =	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] ,   _lowerCamelCase ,   atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				__SCREAMING_SNAKE_CASE :	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(_lowerCamelCase      ).mkdir(exist_ok=_lowerCamelCase      )
		model.save_pretrained(_lowerCamelCase      )
		print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )
		processor.save_pretrained(_lowerCamelCase      )
		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=_lowerCamelCase ,   )
				processor.push_to_hub(
				    repo_id=F"Bearnardd/{pytorch_dump_path}" ,   commit_message="""Add image processor""" ,   use_temp_dir=_lowerCamelCase ,   )


if __name__ == "__main__":
						UpperCamelCase__ :			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__ :			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,
						)




'''simple docstring'''




UpperCamelCase__ :			List[str]												=				'''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
						from .utils import rich