



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

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=       logging.get_logger()






@dataclass
class       SCREAMING_SNAKE_CASE	:
							__lowerCamelCase		:		nn.Module
							__lowerCamelCase		:		List[nn.Module]			=field(default_factory=lowercase__    )
							__lowerCamelCase		:		list			=field(default_factory=lowercase__    )





							def   UpperCamelCase_    (      self						:						Tuple					, __lowercase						:						List[str]					, __lowercase						:						Tensor					, __lowercase						:						Tensor		):

											'''simple docstring'''
											__a        =						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						:						Dict					, __lowercase						:						Tensor		):

											'''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   UpperCamelCase_    (      self						:						str		):

											'''simple docstring'''
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda __lowercase		: len(list(x.state_dict().keys()		)		) > 0					, self.traced		)		)






@dataclass
class       SCREAMING_SNAKE_CASE	:
							__lowerCamelCase		:		nn.Module
							__lowerCamelCase		:		nn.Module
							__lowerCamelCase		:		int			=1
							__lowerCamelCase		:		List			=field(default_factory=lowercase__    )
							__lowerCamelCase		:		List			=field(default_factory=lowercase__    )
							__lowerCamelCase		:		bool			=True





							def __call__(      self						:						Any					, __lowercase						:						Tensor		):

											'''simple docstring'''
											__a        =						Tracker(self.dest		)(__lowercase		).parametrized
											__a        =						Tracker(self.src		)(__lowercase		).parametrized

											__a        =						list(filter(lambda __lowercase		: type(__lowercase		) not in self.src_skip					, __lowercase		)		)
											__a        =						list(filter(lambda __lowercase		: type(__lowercase		) not in self.dest_skip					, __lowercase		)		)

											if len(__lowercase		) != len(__lowercase		) and self.raise_if_mismatch:
															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}"		)






class       SCREAMING_SNAKE_CASE	(							nn.Module    ):





							def __init__(      self						:						Any					, __lowercase						:						nn.Module		):

											'''simple docstring'''
											super().__init__()

											__a        =						[]
											# - get the stem
											feature_blocks.append(("""conv1""", model.stem)		)
											# - get all the feature blocks
											for k, v in model.trunk_output.named_children():
															assert k.startswith("""block"""		), F"Unexpected layer name {k}"
															__a        =						len(__lowercase		) + 1
															feature_blocks.append((F"res{block_index}", v)		)

											__a        =						nn.ModuleDict(__lowercase		)







							def   UpperCamelCase_    (      self						:						Tuple					, __lowercase						:						Tensor		):

											'''simple docstring'''
											return get_trunk_forward_outputs(
											    __lowercase					, out_feat_keys=__lowercase					, feature_blocks=self._feature_blocks					, )






class       SCREAMING_SNAKE_CASE	(							lowercase__    ):





							def   UpperCamelCase_    (      self						:						Optional[int]					, __lowercase						:						str		):

											'''simple docstring'''
											__a        =						x.split("""-"""		)
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)







							def __getitem__(      self						:						Dict					, __lowercase						:						str		):

											'''simple docstring'''
											# default to timm!
											if x not in self:
															__a        =						self.convert_name_to_timm(__lowercase		)
															__a        =						partial(lambda: (timm.create_model(__lowercase					, pretrained=__lowercase		).eval(), None)		)

											else:
															__a        =						super().__getitem__(__lowercase		)

											return val






class       SCREAMING_SNAKE_CASE	(							lowercase__    ):





							def __getitem__(      self						:						str					, __lowercase						:						str		):

											'''simple docstring'''
											if "seer" in x and "in1k" not in x:
															__a        =						RegNetModel
											else:
															__a        =						RegNetForImageClassification
											return val



def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Optional[Any] ,   _SCREAMING_SNAKE_CASE				:	Optional[Any] ,   _SCREAMING_SNAKE_CASE				:	List[Tuple[str, str]]			):
				"""simple docstring"""







				for from_key, to_key in keys:
								__a        =						from_state_dict[from_key].clone()
								print(f"Copied key={from_key} to={to_key}"			)
				return to_state_dict

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	str ,   _SCREAMING_SNAKE_CASE				:	Callable[[], nn.Module] ,   _SCREAMING_SNAKE_CASE				:	Callable[[], nn.Module] ,   _SCREAMING_SNAKE_CASE				:	RegNetConfig ,   _SCREAMING_SNAKE_CASE				:	Path ,   _SCREAMING_SNAKE_CASE				:	bool = True ,   ):
				"""simple docstring"""







				print(f"Converting {name}..."			)
				with torch.no_grad():
								__a        =						from_model_func()
								__a        =						our_model_func(__lowerCamelCase			).eval()
								__a        =						ModuleTransfer(src=__lowerCamelCase ,   dest=__lowerCamelCase ,   raise_if_mismatch=__lowerCamelCase			)
								__a        =						torch.randn((1, 3, 224, 224)			)
								module_transfer(__lowerCamelCase			)

				if from_state_dict is not None:
								__a        =						[]
								# for seer - in1k finetuned we have to manually copy the head
								if "seer" in name and "in1k" in name:
												__a        =						[("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
								__a        =						manually_copy_vissl_head(__lowerCamelCase ,   our_model.state_dict() ,   __lowerCamelCase			)
								our_model.load_state_dict(__lowerCamelCase			)

				__a        =						our_model(__lowerCamelCase ,   output_hidden_states=__lowerCamelCase			)
				__a        =						(
				    our_outputs.logits if isinstance(__lowerCamelCase ,   __lowerCamelCase			) else our_outputs.last_hidden_state
				)

				__a        =						from_model(__lowerCamelCase			)
				__a        =						from_output[-1] if type(__lowerCamelCase			) is list else from_output

				# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
				if "seer" in name and "in1k" in name:
								__a        =						our_outputs.hidden_states[-1]

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

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

								__a        =						224 if """seer""" not in name else 384
								# we can use the convnext one
								__a        =						AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" ,   size=__lowerCamelCase			)
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / name ,   commit_message="""Add image processor""" ,   use_temp_dir=__lowerCamelCase ,   )

								print(f"Pushed {name}"			)

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	Path ,   _SCREAMING_SNAKE_CASE				:	str = None ,   _SCREAMING_SNAKE_CASE				:	bool = True			):
				"""simple docstring"""







				__a        =						"""imagenet-1k-id2label.json"""
				__a        =						1000
				__a        =						(1, num_labels)

				__a        =						"""huggingface/label-files"""
				__a        =						num_labels
				__a        =						json.load(open(cached_download(hf_hub_url(__lowerCamelCase ,   __lowerCamelCase ,   repo_type="""dataset"""			)			) ,   """r"""			)			)
				__a        =						{int(__lowerCamelCase			): v for k, v in idalabel.items()}

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

				__a        =						partial(__lowerCamelCase ,   num_labels=__lowerCamelCase ,   idalabel=__lowerCamelCase ,   labelaid=__lowerCamelCase			)

				__a        =						{
				    """regnet-x-002""": ImageNetPreTrainedConfig(
				        depths=[1, 1, 4, 7] ,   hidden_sizes=[24, 56, 152, 368] ,   groups_width=8 ,   layer_type="""x"""			),
				    """regnet-x-004""": ImageNetPreTrainedConfig(
				        depths=[1, 2, 7, 12] ,   hidden_sizes=[32, 64, 160, 384] ,   groups_width=16 ,   layer_type="""x"""			),
				    """regnet-x-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 5, 7] ,   hidden_sizes=[48, 96, 240, 528] ,   groups_width=24 ,   layer_type="""x"""			),
				    """regnet-x-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 5] ,   hidden_sizes=[64, 128, 288, 672] ,   groups_width=16 ,   layer_type="""x"""			),
				    """regnet-x-016""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 2] ,   hidden_sizes=[72, 168, 408, 912] ,   groups_width=24 ,   layer_type="""x"""			),
				    """regnet-x-032""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 15, 2] ,   hidden_sizes=[96, 192, 432, 1008] ,   groups_width=48 ,   layer_type="""x"""			),
				    """regnet-x-040""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 14, 2] ,   hidden_sizes=[80, 240, 560, 1360] ,   groups_width=40 ,   layer_type="""x"""			),
				    """regnet-x-064""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1] ,   hidden_sizes=[168, 392, 784, 1624] ,   groups_width=56 ,   layer_type="""x"""			),
				    """regnet-x-080""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 15, 1] ,   hidden_sizes=[80, 240, 720, 1920] ,   groups_width=120 ,   layer_type="""x"""			),
				    """regnet-x-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1] ,   hidden_sizes=[224, 448, 896, 2240] ,   groups_width=112 ,   layer_type="""x"""			),
				    """regnet-x-160""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 13, 1] ,   hidden_sizes=[256, 512, 896, 2048] ,   groups_width=128 ,   layer_type="""x"""			),
				    """regnet-x-320""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 13, 1] ,   hidden_sizes=[336, 672, 1344, 2520] ,   groups_width=168 ,   layer_type="""x"""			),
				    # y variant
				    """regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] ,   hidden_sizes=[24, 56, 152, 368] ,   groups_width=8			),
				    """regnet-y-004""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 6, 6] ,   hidden_sizes=[48, 104, 208, 440] ,   groups_width=8			),
				    """regnet-y-006""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 7, 4] ,   hidden_sizes=[48, 112, 256, 608] ,   groups_width=16			),
				    """regnet-y-008""": ImageNetPreTrainedConfig(
				        depths=[1, 3, 8, 2] ,   hidden_sizes=[64, 128, 320, 768] ,   groups_width=16			),
				    """regnet-y-016""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 17, 2] ,   hidden_sizes=[48, 120, 336, 888] ,   groups_width=24			),
				    """regnet-y-032""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 13, 1] ,   hidden_sizes=[72, 216, 576, 1512] ,   groups_width=24			),
				    """regnet-y-040""": ImageNetPreTrainedConfig(
				        depths=[2, 6, 12, 2] ,   hidden_sizes=[128, 192, 512, 1088] ,   groups_width=64			),
				    """regnet-y-064""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 14, 2] ,   hidden_sizes=[144, 288, 576, 1296] ,   groups_width=72			),
				    """regnet-y-080""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 10, 1] ,   hidden_sizes=[168, 448, 896, 2016] ,   groups_width=56			),
				    """regnet-y-120""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 11, 1] ,   hidden_sizes=[224, 448, 896, 2240] ,   groups_width=112			),
				    """regnet-y-160""": ImageNetPreTrainedConfig(
				        depths=[2, 4, 11, 1] ,   hidden_sizes=[224, 448, 1232, 3024] ,   groups_width=112			),
				    """regnet-y-320""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1] ,   hidden_sizes=[232, 696, 1392, 3712] ,   groups_width=232			),
				    # models created by SEER -> https://arxiv.org/abs/2202.08360
				    """regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,   hidden_sizes=[232, 696, 1392, 3712] ,   groups_width=232			),
				    """regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1] ,   hidden_sizes=[328, 984, 1968, 4920] ,   groups_width=328			),
				    """regnet-y-1280-seer""": RegNetConfig(
				        depths=[2, 7, 17, 1] ,   hidden_sizes=[528, 1056, 2904, 7392] ,   groups_width=264			),
				    """regnet-y-2560-seer""": RegNetConfig(
				        depths=[3, 7, 16, 1] ,   hidden_sizes=[640, 1696, 2544, 5088] ,   groups_width=640			),
				    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1] ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,   groups_width=1010			),
				    # finetuned on imagenet
				    """regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1] ,   hidden_sizes=[232, 696, 1392, 3712] ,   groups_width=232			),
				    """regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 5, 12, 1] ,   hidden_sizes=[328, 984, 1968, 4920] ,   groups_width=328			),
				    """regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1] ,   hidden_sizes=[528, 1056, 2904, 7392] ,   groups_width=264			),
				    """regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[3, 7, 16, 1] ,   hidden_sizes=[640, 1696, 2544, 5088] ,   groups_width=640			),
				    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1] ,   hidden_sizes=[2020, 4040, 1_1110, 2_8280] ,   groups_width=1010			),
				}

				__a        =						NameToOurModelFuncMap()
				__a        =						NameToFromModelFuncMap()
				# add seer weights logic

				def load_using_classy_vision(_SCREAMING_SNAKE_CASE				:	str ,   _SCREAMING_SNAKE_CASE				:	Callable[[], nn.Module]			) -> Tuple[nn.Module, Dict]:
								__a        =						torch.hub.load_state_dict_from_url(__lowerCamelCase ,   model_dir=str(__lowerCamelCase			) ,   map_location="""cpu"""			)
								__a        =						model_func()
								# check if we have a head, if yes add it
								__a        =						files["""classy_state_dict"""]["""base_model"""]["""model"""]
								__a        =						model_state_dict["""trunk"""]
								model.load_state_dict(__lowerCamelCase			)
								return model.eval(), model_state_dict["heads"]

				# pretrained
				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch""" ,   lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27 ,   group_width=1010 ,   w_a=1744 ,   w_a=620.83 ,   w_m=2.52			)			)			) ,   )

				# IN1K finetuned
				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,   lambda: FakeRegNetVisslWrapper(RegNetYaaagf()			) ,   )

				__a        =						partial(
				    __lowerCamelCase ,   """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch""" ,   lambda: FakeRegNetVisslWrapper(
				        RegNet(RegNetParams(depth=27 ,   group_width=1010 ,   w_a=1744 ,   w_a=620.83 ,   w_m=2.52			)			)			) ,   )

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


if __name__ == "__main__":
						lowerCamelCase__	=       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 regnet* architecture,"""
						        """ currently: regnetx-*, regnety-*. 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__	=       parser.parse_args()

						lowerCamelCase__	=       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)








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


