





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   (				__a  :List[str]       ,	__a  :Optional[int]       )						->							Optional[Any]:






		"""simple docstring"""

		A__     =  old_name

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

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

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

						A__     =  """last_stage.""" + trimmed_name

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

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

		return new_name







def   __lowerCamelCase   (				__a  :Optional[int]       ,	__a  :str       )						->							Optional[int]:






		"""simple docstring"""

		for key in checkpoint.copy().keys():
				A__     =  checkpoint.pop(__a       )
				A__     =  val

		return checkpoint







def   __lowerCamelCase   (				)						->							Optional[int]:






		"""simple docstring"""

		A__     =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
		A__     =  Image.open(requests.get(__a       ,	stream=__a       ).raw       )

		return image







def   __lowerCamelCase   (				__a  :Path       ,	__a  :Path       ,	__a  :Path       ,	__a  :bool       )						->							List[Any]:






		"""simple docstring"""

		A__     =  torch.load(__a       ,	map_location="""cpu"""       )["""model"""]
		A__     =  EfficientFormerConfig.from_json_file(__a       )
		A__     =  EfficientFormerForImageClassificationWithTeacher(__a       )
		A__     =  """_""".join(checkpoint_path.split("""/"""       )[-1].split("""."""       )[0].split("""_"""       )[:-1]       )

		A__     =  config.depths[-1] - config.num_metaad_blocks + 1
		A__     =  convert_torch_checkpoint(__a       ,	__a       )

		model.load_state_dict(__a       )
		model.eval()

		A__     =  {
		    """bilinear""": PILImageResampling.BILINEAR,
		    """bicubic""": PILImageResampling.BICUBIC,
		    """nearest""": PILImageResampling.NEAREST,
		}

		# prepare image
		A__     =  prepare_img()
		A__     =  2_5_6
		A__     =  2_2_4
		A__     =  EfficientFormerImageProcessor(
		    size={"""shortest_edge""": image_size}       ,	crop_size={"""height""": crop_size, """width""": crop_size}       ,	resample=pillow_resamplings["""bicubic"""]       ,	)
		A__     =  processor(images=__a       ,	return_tensors="""pt"""       ).pixel_values

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

		assert torch.allclose(__a       ,	__a       )

		A__     =  model(__a       )
		A__     =  outputs.logits

		A__     =  (1, 1_0_0_0)

		if "l1" in model_name:
				A__     =  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, :1_0]       ,	__a       ,	atol=1E-3       )
				assert logits.shape == expected_shape
		elif "l3" in model_name:
				A__     =  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, :1_0]       ,	__a       ,	atol=1E-3       )
				assert logits.shape == expected_shape
		elif "l7" in model_name:
				A__     =  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(__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       :   Optional[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)

					A       :   Tuple					= 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,
					)










class     A     :
			'''simple docstring'''




			def __init__(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  {}






			def   a_			(     self  :       Any  )      ->      None:







					"""simple docstring"""






					print(self.vertex  )
					for i in self.vertex:
							print(__lowerCAmelCase				,      """ -> """				,      """ -> """.join([str(__lowerCAmelCase  ) for j in self.vertex[i]]  )  )






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int  )      ->      None:







					"""simple docstring"""






					if from_vertex in self.vertex:
							self.vertex[from_vertex].append(__lowerCAmelCase  )
					else:
							# else make a new vertex
							A__     =  [to_vertex]






			def   a_			(     self  :       List[str]  )      ->      None:







					"""simple docstring"""






					A__     =  [False] * len(self.vertex  )

					# call the recursive helper function
					for i in range(len(self.vertex  )  ):
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       list  )      ->      None:







					"""simple docstring"""






					A__     =  True

					print(__lowerCAmelCase				,      end=""" """  )

					# Recur for all the vertices that are adjacent to this node
					for i in self.vertex:
							if not visited[i]:
									self.dfs_recursive(__lowerCAmelCase				,      __lowerCAmelCase  )


if __name__ == "__main__":
					A       :   Tuple					= Graph()
					g.add_edge(0, 1)
					g.add_edge(0, 2)
					g.add_edge(1, 2)
					g.add_edge(2, 0)
					g.add_edge(2, 3)
					g.add_edge(3, 3)

					g.print_graph()
					print('''DFS:''')
					g.dfs()

					# OUTPUT:
					# 0  ->  1 -> 2
					# 1  ->  2
					# 2  ->  0 -> 3
					# 3  ->  3
					# DFS:
					#  0 1 2 3



