


"""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     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Tuple  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					old_name

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

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

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

																		SCREAMING_SNAKE_CASE													=					'last_stage.' + trimmed_name

						elif "network" in old_name and re.search(R'.\d.' ,			SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					old_name.replace('network' ,			'intermediate_stages'  )

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

						return new_name


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Dict ,			SCREAMING_SNAKE_CASE_		:				Union[str, Any]  )   ->						Union[str, Any]:




						for key in checkpoint.copy().keys():
												SCREAMING_SNAKE_CASE													=					checkpoint.pop(SCREAMING_SNAKE_CASE_  )
												SCREAMING_SNAKE_CASE													=					val

						return checkpoint


def     lowercase			()   ->						str:
						SCREAMING_SNAKE_CASE													=					'http://images.cocodataset.org/val2017/000000039769.jpg'
						SCREAMING_SNAKE_CASE													=					Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,			stream=SCREAMING_SNAKE_CASE_  ).raw  )

						return image


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Path ,			SCREAMING_SNAKE_CASE_		:				Path ,			SCREAMING_SNAKE_CASE_		:				Path ,			SCREAMING_SNAKE_CASE_		:				bool  )   ->						Optional[int]:
						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_ ,			map_location='cpu'  )['model']
						SCREAMING_SNAKE_CASE													=					EfficientFormerConfig.from_json_file(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					EfficientFormerForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					'_'.join(checkpoint_path.split('/'  )[-1].split('.'  )[0].split('_'  )[:-1]  )

						SCREAMING_SNAKE_CASE													=					config.depths[-1] - config.num_metaad_blocks + 1
						SCREAMING_SNAKE_CASE													=					convert_torch_checkpoint(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						model.load_state_dict(SCREAMING_SNAKE_CASE_  )
						model.eval()

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

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

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

						assert torch.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					outputs.logits

						SCREAMING_SNAKE_CASE													=					(1, 10_00)

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





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






