"""simple docstring"""


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_:					str     =					logging.get_logger()






@dataclass
class    a__    :
			snake_case_							=      42
			snake_case_							=      field(default_factory=UpperCAmelCase_	)
			snake_case_							=      field(default_factory=UpperCAmelCase_	)


			def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
								'''simple docstring'''



								lowercase__				 =							len(list(m.modules()  )  ) == 1 or isinstance(_lowercase,				nn.Convad  ) or isinstance(_lowercase,				nn.BatchNormad  )
								if has_not_submodules:
													self.traced.append(_lowercase  )


			def __call__(     self,				_UpperCAmelCase  ):
								'''simple docstring'''



								for m in self.module.modules():
													self.handles.append(m.register_forward_hook(self._forward_hook  )  )
								self.module(_lowercase  )
								[x.remove() for x in self.handles]
								return self


			@property
			def 	snake_case__	(     self  ):
								'''simple docstring'''



								return list(filter(lambda _UpperCAmelCase  : len(list(x.state_dict().keys()  )  ) > 0,				self.traced  )  )






@dataclass
class    a__    :
			snake_case_							=      42
			snake_case_							=      42
			snake_case_							=      0
			snake_case_							=      field(default_factory=UpperCAmelCase_	)
			snake_case_							=      field(default_factory=UpperCAmelCase_	)


			def __call__(     self,				_UpperCAmelCase  ):
								'''simple docstring'''



								lowercase__				 =							Tracker(self.dest  )(_lowercase  ).parametrized
								lowercase__				 =							Tracker(self.src  )(_lowercase  ).parametrized

								lowercase__				 =							list(filter(lambda _UpperCAmelCase  : type(_lowercase  ) not in self.src_skip,				_lowercase  )  )
								lowercase__				 =							list(filter(lambda _UpperCAmelCase  : type(_lowercase  ) not in self.dest_skip,				_lowercase  )  )

								if len(_lowercase  ) != len(_lowercase  ):
													raise Exception(
													    F'''Numbers of operations are different. Source module has {len(_lowercase  )} operations while'''
													    F''' destination module has {len(_lowercase  )}.'''  )

								for dest_m, src_m in zip(_lowercase,				_lowercase  ):
													dest_m.load_state_dict(src_m.state_dict()  )
													if self.verbose == 1:
																		print(F'''Transfered from={src_m} to={dest_m}'''  )







def 					__a   (		A    ,				A    ,				A    ,				A = True      ):






					'''simple docstring'''



					print(f'''Converting {name}...'''      )
					with torch.no_grad():
										lowercase__				 =							timm.create_model(A    ,				pretrained=A      ).eval()
										lowercase__				 =							ResNetForImageClassification(A      ).eval()
										lowercase__				 =							ModuleTransfer(src=A    ,				dest=A      )
										lowercase__				 =							torch.randn((1, 3, 2_24, 2_24)      )
										module_transfer(A      )

					assert torch.allclose(from_model(A      )    ,				our_model(A      ).logits      ), "The model logits don't match the original one."

					lowercase__				 =							f'''resnet{'-'.join(name.split('resnet'      )      )}'''
					print(A      )

					if push_to_hub:
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name    ,				commit_message="Add model"    ,				use_temp_dir=A    ,				)

										# we can use the convnext one
										lowercase__				 =							AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"      )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / checkpoint_name    ,				commit_message="Add image processor"    ,				use_temp_dir=A    ,				)

										print(f'''Pushed {checkpoint_name}'''      )







def 					__a   (		A    ,				A = None    ,				A = True      ):






					'''simple docstring'''



					lowercase__				 =							'imagenet-1k-id2label.json'
					lowercase__				 =							10_00
					lowercase__				 =							(1, num_labels)

					lowercase__				 =							'huggingface/label-files'
					lowercase__				 =							num_labels
					lowercase__				 =							json.load(open(hf_hub_download(A    ,				A    ,				repo_type="dataset"      )    ,				"r"      )      )
					lowercase__				 =							{int(A      ): v for k, v in idalabel.items()}

					lowercase__				 =							idalabel
					lowercase__				 =							{v: k for k, v in idalabel.items()}

					lowercase__				 =							partial(A    ,				num_labels=A    ,				idalabel=A    ,				labelaid=A      )

					lowercase__				 =							{
					    'resnet18': ImageNetPreTrainedConfig(
					        depths=[2, 2, 2, 2]    ,				hidden_sizes=[64, 1_28, 2_56, 5_12]    ,				layer_type="basic"      ),
					    'resnet26': ImageNetPreTrainedConfig(
					        depths=[2, 2, 2, 2]    ,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,				layer_type="bottleneck"      ),
					    'resnet34': ImageNetPreTrainedConfig(
					        depths=[3, 4, 6, 3]    ,				hidden_sizes=[64, 1_28, 2_56, 5_12]    ,				layer_type="basic"      ),
					    'resnet50': ImageNetPreTrainedConfig(
					        depths=[3, 4, 6, 3]    ,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,				layer_type="bottleneck"      ),
					    'resnet101': ImageNetPreTrainedConfig(
					        depths=[3, 4, 23, 3]    ,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,				layer_type="bottleneck"      ),
					    'resnet152': ImageNetPreTrainedConfig(
					        depths=[3, 8, 36, 3]    ,				hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,				layer_type="bottleneck"      ),
					}

					if model_name:
										convert_weight_and_push(A    ,				names_to_config[model_name]    ,				A    ,				A      )
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(A    ,				A    ,				A    ,				A      )
					return config, expected_shape


if __name__ == "__main__":
	lowerCAmelCase_:					Tuple     =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default=None,
	    type=str,
	    help=(
	        "The name of the model you wish to convert, it must be one of the supported resnet* architecture,"
	        " currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."
	    ),
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path",
	    default=None,
	    type=Path,
	    required=True,
	    help="Path to the output PyTorch model directory.",
	)
	parser.add_argument(
	    "--push_to_hub",
	    default=True,
	    type=bool,
	    required=False,
	    help="If True, push model and image processor to the hub.",
	)

	lowerCAmelCase_:					Union[str, Any]     =					parser.parse_args()
	lowerCAmelCase_:					Path     =					args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""


from typing import Any

import numpy as np







def 					__a   (		A      ):






     '''simple docstring'''



     return np.array_equal(A    ,				matrix.conjugate().T      )







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							v.conjugate().T
     lowercase__				 =							v_star.dot(A      )
     assert isinstance(A    ,				np.ndarray      )
     return (v_star_dot.dot(A      )) / (v_star.dot(A      ))







def 					__a   (		):






     '''simple docstring'''



     lowercase__				 =							np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]      )
     lowercase__				 =							np.array([[1], [2], [3]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     print(rayleigh_quotient(A    ,				A      )      )

     lowercase__				 =							np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]      )
     assert is_hermitian(A      ), f'''{a} is not hermitian.'''
     assert rayleigh_quotient(A    ,				A      ) == float(3      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 tests()




