"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__      						=  logging.get_logger()



@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    nn.Module
				_lowerCAmelCase     :    List[nn.Module]			  =						field(default_factory=lowerCAmelCase					)
				_lowerCAmelCase     :    list			  =						field(default_factory=lowerCAmelCase					)

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def __call__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

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





@dataclass
class   lowerCAmelCase_    :



				"""simple docstring"""
				_lowerCAmelCase     :    nn.Module
				_lowerCAmelCase     :    nn.Module
				_lowerCAmelCase     :    int			  =						1
				_lowerCAmelCase     :    List			  =						field(default_factory=lowerCAmelCase					)
				_lowerCAmelCase     :    List			  =						field(default_factory=lowerCAmelCase					)
				_lowerCAmelCase     :    bool			  =						True

				def __call__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			Tracker(self.dest    )(lowerCAmelCase    ).parametrized
										snake_case								=			Tracker(self.src    )(lowerCAmelCase    ).parametrized

										snake_case								=			list(filter(lambda lowerCAmelCase    : type(lowerCAmelCase    ) not in self.src_skip		,    lowerCAmelCase    )    )
										snake_case								=			list(filter(lambda lowerCAmelCase    : type(lowerCAmelCase    ) not in self.dest_skip		,    lowerCAmelCase    )    )

										if len(lowerCAmelCase    ) != len(lowerCAmelCase    ) and self.raise_if_mismatch:
																raise Exception(
																    F"""Numbers of operations are different. Source module has {len(lowerCAmelCase    )} operations while"""
																    F""" destination module has {len(lowerCAmelCase    )}."""    )

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





class   lowerCAmelCase_    (			nn.Module					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										super().__init__()

										snake_case								=			[]
										# - 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}"""
																snake_case								=			len(lowerCAmelCase    ) + 1
																feature_blocks.append((F"""res{block_index}""", v)    )

										snake_case								=			nn.ModuleDict(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return get_trunk_forward_outputs(
										    lowerCAmelCase		,    out_feat_keys=lowerCAmelCase		,    feature_blocks=self._feature_blocks		,    )





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

				def __getitem__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if x not in self:
																snake_case								=			self.convert_name_to_timm(lowerCAmelCase    )
																snake_case								=			partial(lambda: (timm.create_model(lowerCAmelCase		,    pretrained=lowerCAmelCase    ).eval(), None)    )

										else:
																snake_case								=			super().__getitem__(lowerCAmelCase    )

										return val





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __getitem__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										if "seer" in x and "in1k" not in x:
																snake_case								=			RegNetModel
										else:
																snake_case								=			RegNetForImageClassification
										return val





def    lowerCAmelCase__    (			_UpperCamelCase       :				Any ,			_UpperCamelCase       :				Optional[int] ,			_UpperCamelCase       :				List[Tuple[str, str]]					)					->				Optional[Any]:





						"""simple docstring"""






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





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				Callable[[], nn.Module] ,			_UpperCamelCase       :				Callable[[], nn.Module] ,			_UpperCamelCase       :				RegNetConfig ,			_UpperCamelCase       :				Path ,			_UpperCamelCase       :				bool = True ,			)					->				List[Any]:





						"""simple docstring"""






						print(f"""Converting {name}..."""					)
						with torch.no_grad():
												snake_case					,snake_case								=			from_model_func()
												snake_case								=			our_model_func(_UpperCamelCase					).eval()
												snake_case								=			ModuleTransfer(src=_UpperCamelCase ,			dest=_UpperCamelCase ,			raise_if_mismatch=_UpperCamelCase					)
												snake_case								=			torch.randn((1, 3, 2_2_4, 2_2_4)					)
												module_transfer(_UpperCamelCase					)

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

						snake_case								=			our_model(_UpperCamelCase ,			output_hidden_states=_UpperCamelCase					)
						snake_case								=			(
						    our_outputs.logits if isinstance(_UpperCamelCase ,			_UpperCamelCase					) else our_outputs.last_hidden_state
						)

						snake_case								=			from_model(_UpperCamelCase					)
						snake_case								=			from_output[-1] if type(_UpperCamelCase					) 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:
												snake_case								=			our_outputs.hidden_states[-1]

						assert torch.allclose(_UpperCamelCase ,			_UpperCamelCase					), "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=_UpperCamelCase ,			)

												snake_case								=			2_2_4 if 'seer' not in name else 3_8_4
												# we can use the convnext one
												snake_case								=			AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' ,			size=_UpperCamelCase					)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / name ,			commit_message='Add image processor' ,			use_temp_dir=_UpperCamelCase ,			)

												print(f"""Pushed {name}"""					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				Path ,			_UpperCamelCase       :				str = None ,			_UpperCamelCase       :				bool = True					)					->				Dict:





						"""simple docstring"""






						snake_case								=			'imagenet-1k-id2label.json'
						snake_case								=			1_0_0_0
						snake_case								=			(1, num_labels)

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

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

						snake_case								=			partial(_UpperCamelCase ,			num_labels=_UpperCamelCase ,			idalabel=_UpperCamelCase ,			labelaid=_UpperCamelCase					)

						snake_case								=			{
						    'regnet-x-002': ImageNetPreTrainedConfig(
						        depths=[1, 1, 4, 7] ,			hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8] ,			groups_width=8 ,			layer_type='x'					),
						    'regnet-x-004': ImageNetPreTrainedConfig(
						        depths=[1, 2, 7, 1_2] ,			hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4] ,			groups_width=1_6 ,			layer_type='x'					),
						    'regnet-x-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 5, 7] ,			hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8] ,			groups_width=2_4 ,			layer_type='x'					),
						    'regnet-x-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 5] ,			hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2] ,			groups_width=1_6 ,			layer_type='x'					),
						    'regnet-x-016': ImageNetPreTrainedConfig(
						        depths=[2, 4, 1_0, 2] ,			hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2] ,			groups_width=2_4 ,			layer_type='x'					),
						    'regnet-x-032': ImageNetPreTrainedConfig(
						        depths=[2, 6, 1_5, 2] ,			hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8] ,			groups_width=4_8 ,			layer_type='x'					),
						    'regnet-x-040': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_4, 2] ,			hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0] ,			groups_width=4_0 ,			layer_type='x'					),
						    'regnet-x-064': ImageNetPreTrainedConfig(
						        depths=[2, 4, 1_0, 1] ,			hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4] ,			groups_width=5_6 ,			layer_type='x'					),
						    'regnet-x-080': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_5, 1] ,			hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0] ,			groups_width=1_2_0 ,			layer_type='x'					),
						    'regnet-x-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_1, 1] ,			hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0] ,			groups_width=1_1_2 ,			layer_type='x'					),
						    'regnet-x-160': ImageNetPreTrainedConfig(
						        depths=[2, 6, 1_3, 1] ,			hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8] ,			groups_width=1_2_8 ,			layer_type='x'					),
						    'regnet-x-320': ImageNetPreTrainedConfig(
						        depths=[2, 7, 1_3, 1] ,			hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0] ,			groups_width=1_6_8 ,			layer_type='x'					),
						    # y variant
						    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] ,			hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8] ,			groups_width=8					),
						    'regnet-y-004': ImageNetPreTrainedConfig(
						        depths=[1, 3, 6, 6] ,			hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0] ,			groups_width=8					),
						    'regnet-y-006': ImageNetPreTrainedConfig(
						        depths=[1, 3, 7, 4] ,			hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8] ,			groups_width=1_6					),
						    'regnet-y-008': ImageNetPreTrainedConfig(
						        depths=[1, 3, 8, 2] ,			hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8] ,			groups_width=1_6					),
						    'regnet-y-016': ImageNetPreTrainedConfig(
						        depths=[2, 6, 1_7, 2] ,			hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8] ,			groups_width=2_4					),
						    'regnet-y-032': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_3, 1] ,			hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2] ,			groups_width=2_4					),
						    'regnet-y-040': ImageNetPreTrainedConfig(
						        depths=[2, 6, 1_2, 2] ,			hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8] ,			groups_width=6_4					),
						    'regnet-y-064': ImageNetPreTrainedConfig(
						        depths=[2, 7, 1_4, 2] ,			hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6] ,			groups_width=7_2					),
						    'regnet-y-080': ImageNetPreTrainedConfig(
						        depths=[2, 4, 1_0, 1] ,			hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6] ,			groups_width=5_6					),
						    'regnet-y-120': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_1, 1] ,			hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0] ,			groups_width=1_1_2					),
						    'regnet-y-160': ImageNetPreTrainedConfig(
						        depths=[2, 4, 1_1, 1] ,			hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4] ,			groups_width=1_1_2					),
						    'regnet-y-320': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_2, 1] ,			hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] ,			groups_width=2_3_2					),
						    # models created by SEER -> https://arxiv.org/abs/2202.08360
						    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 1_2, 1] ,			hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] ,			groups_width=2_3_2					),
						    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 1_2, 1] ,			hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0] ,			groups_width=3_2_8					),
						    'regnet-y-1280-seer': RegNetConfig(
						        depths=[2, 7, 1_7, 1] ,			hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2] ,			groups_width=2_6_4					),
						    'regnet-y-2560-seer': RegNetConfig(
						        depths=[3, 7, 1_6, 1] ,			hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8] ,			groups_width=6_4_0					),
						    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
						        depths=[2, 7, 1_7, 1] ,			hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,			groups_width=1_0_1_0					),
						    # finetuned on imagenet
						    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_2, 1] ,			hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2] ,			groups_width=2_3_2					),
						    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 5, 1_2, 1] ,			hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0] ,			groups_width=3_2_8					),
						    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 1_7, 1] ,			hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2] ,			groups_width=2_6_4					),
						    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[3, 7, 1_6, 1] ,			hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8] ,			groups_width=6_4_0					),
						    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
						        depths=[2, 7, 1_7, 1] ,			hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0] ,			groups_width=1_0_1_0					),
						}

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

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

						# pretrained
						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'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()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' ,			lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=2_7 ,			group_width=1_0_1_0 ,			w_a=1_7_4_4 ,			w_a=6_20.83 ,			w_m=2.52					)					)					) ,			)

						# IN1K finetuned
						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' ,			lambda: FakeRegNetVisslWrapper(RegNetYaagf()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' ,			lambda: FakeRegNetVisslWrapper(RegNetYaaagf()					) ,			)

						snake_case								=			partial(
						    _UpperCamelCase ,			'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' ,			lambda: FakeRegNetVisslWrapper(
						        RegNet(RegNetParams(depth=2_7 ,			group_width=1_0_1_0 ,			w_a=1_7_4_4 ,			w_a=6_20.83 ,			w_m=2.52					)					)					) ,			)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      						=  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.",
					)

					SCREAMING_SNAKE_CASE__      						=  parser.parse_args()

					SCREAMING_SNAKE_CASE__      						=  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"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						return math.sqrt(_UpperCamelCase					) * math.sqrt(_UpperCamelCase					) == num





def    lowerCAmelCase__    (			_UpperCamelCase       :				int					)					->				bool:





						"""simple docstring"""






						snake_case								=			0
						snake_case								=			n
						while left <= right:
												snake_case								=			(left + right) // 2
												if mid**2 == n:
																		return True
												elif mid**2 > n:
																		snake_case								=			mid - 1
												else:
																		snake_case								=			mid + 1
						return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


