
"""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 (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Dict          =   old_name

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

				if layer == "0":
						lowerCamelCase					:      int          =   old_name.replace('0',						'convolution1'      )
				elif layer == "1":
						lowerCamelCase					:      int          =   old_name.replace('1',						'batchnorm_before'      )
				elif layer == "3":
						lowerCamelCase					:      str          =   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',						a_      ):
				lowerCamelCase					:      Dict          =   r'\b\d{2}\b'
				if bool(re.search(a_,						a_      )      ):
						lowerCamelCase					:      str          =   re.search(r'\d\.\d\d.',						a_      ).group()
				else:
						lowerCamelCase					:      str          =   re.search(r'\d\.\d.',						a_      ).group()
				if int(match[0]      ) < 6:
						lowerCamelCase					:      Any          =   old_name.replace(a_,						''      )
						lowerCamelCase					:      Dict          =   trimmed_name.replace('network',						match[0] + '.meta4D_layers.blocks.' + match[2:-1]      )
						lowerCamelCase					:      List[str]          =   'intermediate_stages.' + trimmed_name
				else:
						lowerCamelCase					:      Union[str, Any]          =   old_name.replace(a_,						''      )
						if int(match[2]      ) < num_meta4D_last_stage:
								lowerCamelCase					:      Tuple          =   trimmed_name.replace('network',						'meta4D_layers.blocks.' + match[2]      )
						else:
								lowerCamelCase					:      List[Any]          =   str(int(match[2]      ) - num_meta4D_last_stage      )
								lowerCamelCase					:      Tuple          =   trimmed_name.replace('network',						'meta3D_layers.blocks.' + layer_index      )
								if "norm1" in old_name:
										lowerCamelCase					:      str          =   trimmed_name.replace('norm1',						'layernorm1'      )
								elif "norm2" in old_name:
										lowerCamelCase					:      Dict          =   trimmed_name.replace('norm2',						'layernorm2'      )
								elif "fc1" in old_name:
										lowerCamelCase					:      Tuple          =   trimmed_name.replace('fc1',						'linear_in'      )
								elif "fc2" in old_name:
										lowerCamelCase					:      int          =   trimmed_name.replace('fc2',						'linear_out'      )

						lowerCamelCase					:      List[Any]          =   'last_stage.' + trimmed_name

		elif "network" in old_name and re.search(r'.\d.',						a_      ):
				lowerCamelCase					:      Optional[int]          =   old_name.replace('network',						'intermediate_stages'      )

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

		return new_name
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for key in checkpoint.copy().keys():
				lowerCamelCase					:      Dict          =   checkpoint.pop(a_      )
				lowerCamelCase					:      str          =   val

		return checkpoint
def 		UpperCAmelCase (		):





		'''simple docstring'''


		lowerCamelCase					:      str          =   'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowerCamelCase					:      List[Any]          =   Image.open(requests.get(a_,						stream=a_      ).raw      )

		return image
def 		UpperCAmelCase (		a_,						a_,						a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Any          =   torch.load(a_,						map_location='cpu'      )['model']
		lowerCamelCase					:      int          =   EfficientFormerConfig.from_json_file(a_      )
		lowerCamelCase					:      int          =   EfficientFormerForImageClassificationWithTeacher(a_      )
		lowerCamelCase					:      List[Any]          =   '_'.join(checkpoint_path.split('/'      )[-1].split('.'      )[0].split('_'      )[:-1]      )

		lowerCamelCase					:      List[str]          =   config.depths[-1] - config.num_metaad_blocks + 1
		lowerCamelCase					:      Optional[int]          =   convert_torch_checkpoint(a_,						a_      )

		model.load_state_dict(a_      )
		model.eval()

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

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

		# original processing pipeline
		lowerCamelCase					:      Optional[int]          =   Compose(
		    [
		        Resize(a_,						interpolation=pillow_resamplings['bicubic']      ),
		        CenterCrop(a_      ),
		        ToTensor(),
		        Normalize(a_,						a_      ),
		    ]      )
		lowerCamelCase					:      Tuple          =   image_transforms(a_      ).unsqueeze(0      )

		assert torch.allclose(a_,						a_      )

		lowerCamelCase					:      Tuple          =   model(a_      )
		lowerCamelCase					:      int          =   outputs.logits

		lowerCamelCase					:      int          =   (1, 1000)

		if "l1" in model_name:
				lowerCamelCase					:      Any          =   torch.Tensor(
				    [-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8]      )
				assert torch.allclose(logits[0, :10],						a_,						atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				lowerCamelCase					:      Dict          =   torch.Tensor(
				    [-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7]      )
				assert torch.allclose(logits[0, :10],						a_,						atol=1E-3      )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				lowerCamelCase					:      Optional[int]          =   torch.Tensor(
				    [-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8]      )
				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						=			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						=			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"""

def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		while b:
				lowerCamelCase     ,   lowerCamelCase					:      Tuple          =   b, a % b
		return a
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		return a if b == 0 else euclidean_gcd_recursive(a_,						a % b      )
def 		UpperCAmelCase (		):





		'''simple docstring'''


		print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3,						5      )}"""      )
		print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5,						3      )}"""      )
		print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1,						3      )}"""      )
		print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3,						6      )}"""      )
		print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6,						3      )}"""      )

		print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3,						5      )}"""      )
		print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5,						3      )}"""      )
		print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1,						3      )}"""      )
		print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3,						6      )}"""      )
		print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6,						3      )}"""      )


if __name__ == "__main__":
					main()






