






'''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()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger()







@dataclass
class    A_	:
							"""simple docstring"""




							a__   		=	42
							a__   		=	field(default_factory=a_							)
							a__   		=	field(default_factory=a_							)

							def 							_A	(		self			:Optional[int]       ,							lowerCAmelCase__			:Dict       ,							lowerCAmelCase__			:Tensor       ,							lowerCAmelCase__			:Tensor				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       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			:Tuple       ,							lowerCAmelCase__			:Tensor				)				->  Any:


										'''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 							_A	(		self			:List[Any]				)				->  Tuple:


										'''simple docstring'''




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







@dataclass
class    A_	:
							"""simple docstring"""




							a__   		=	42
							a__   		=	42
							a__   		=	1
							a__   		=	field(default_factory=a_							)
							a__   		=	field(default_factory=a_							)
							a__   		=	True

							def __call__(		self			:List[str]       ,							lowerCAmelCase__			:Tensor				)				->  List[Any]:


										'''simple docstring'''




										snake_case_				:					Tuple   =       Tracker(self.dest				)(lowerCAmelCase__				).parametrized
										snake_case_				:					Tuple   =       Tracker(self.src				)(lowerCAmelCase__				).parametrized

										snake_case_				:					Optional[int]   =       list(filter(lambda lowerCAmelCase__				: type(lowerCAmelCase__				) not in self.src_skip       ,							lowerCAmelCase__				)				)
										snake_case_				:					Optional[int]   =       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    A_	(nn.Module							):
							"""simple docstring"""




							def __init__(		self			:str       ,							lowerCAmelCase__			:nn.Module				)				->  str:


										'''simple docstring'''




										super().__init__()

										snake_case_				:					List[Tuple[str, nn.Module]]   =       []
										# - 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_				:					str   =       len(lowerCAmelCase__				) + 1
													feature_blocks.append((F'''res{block_index}''', v)				)

										snake_case_				:					str   =       nn.ModuleDict(lowerCAmelCase__				)






							def 							_A	(		self			:Union[str, Any]       ,							lowerCAmelCase__			:Tensor				)				->  Optional[Any]:


										'''simple docstring'''




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







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:str       ,							lowerCAmelCase__			:str				)				->  str:


										'''simple docstring'''




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






							def __getitem__(		self			:Any       ,							lowerCAmelCase__			:str				)				->  Callable[[], Tuple[nn.Module, Dict]]:


										'''simple docstring'''




										if x not in self:
													snake_case_				:					Optional[Any]   =       self.convert_name_to_timm(lowerCAmelCase__				)
													snake_case_				:					Any   =       partial(lambda: (timm.create_model(lowerCAmelCase__       ,							pretrained=lowerCAmelCase__				).eval(), None)				)

										else:
													snake_case_				:					Any   =       super().__getitem__(lowerCAmelCase__				)

										return val







class    A_	(a_							):
							"""simple docstring"""




							def __getitem__(		self			:Union[str, Any]       ,							lowerCAmelCase__			:str				)				->  Callable[[], nn.Module]:


										'''simple docstring'''




										if "seer" in x and "in1k" not in x:
													snake_case_				:					List[Any]   =       RegNetModel
										else:
													snake_case_				:					Any   =       RegNetForImageClassification
										return val

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[int]:



			"""simple docstring"""



			for from_key, to_key in keys:
						snake_case_				:					List[Any]   =       from_state_dict[from_key].clone()
						print(F'''Copied key={from_key} to={to_key}'''						)
			return to_state_dict

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__ = True     ,)->    Union[str, Any]:



			"""simple docstring"""



			print(F'''Converting {name}...'''						)
			with torch.no_grad():
						snake_case_,	snake_case_				:					Tuple   =       from_model_func()
						snake_case_				:					str   =       our_model_func(__magic_name__						).eval()
						snake_case_				:					List[Any]   =       ModuleTransfer(src=__magic_name__     ,dest=__magic_name__     ,raise_if_mismatch=__magic_name__						)
						snake_case_				:					List[Any]   =       torch.randn((1, 3, 224, 224)						)
						module_transfer(__magic_name__						)

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

			snake_case_				:					str   =       our_model(__magic_name__     ,output_hidden_states=__magic_name__						)
			snake_case_				:					Optional[Any]   =       (
			    our_outputs.logits if isinstance(__magic_name__     ,__magic_name__						) else our_outputs.last_hidden_state
			)

			snake_case_				:					Tuple   =       from_model(__magic_name__						)
			snake_case_				:					str   =       from_output[-1] if type(__magic_name__						) 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_				:					Tuple   =       our_outputs.hidden_states[-1]

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

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

						print(F'''Pushed {name}'''						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = None     ,__magic_name__ = True						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					int   =       "imagenet-1k-id2label.json"
			snake_case_				:					Dict   =       1000
			snake_case_				:					Union[str, Any]   =       (1, num_labels)

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

			snake_case_				:					Any   =       idalabel
			snake_case_				:					Dict   =       {v: k for k, v in idalabel.items()}

			snake_case_				:					str   =       partial(__magic_name__     ,num_labels=__magic_name__     ,idalabel=__magic_name__     ,labelaid=__magic_name__						)

			snake_case_				:					Dict   =       {
			    "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						),
			}

			snake_case_				:					Any   =       NameToOurModelFuncMap()
			snake_case_				:					Tuple   =       NameToFromModelFuncMap()
			# add seer weights logic

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

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

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

			snake_case_				:					Optional[int]   =       partial(
			    __magic_name__     ,"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_				:					Union[str, Any]   =       partial(
			    __magic_name__     ,"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
			snake_case_				:					str   =       partial(
			    __magic_name__     ,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"     ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)     ,)

			snake_case_				:					Optional[int]   =       partial(
			    __magic_name__     ,"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"     ,lambda: FakeRegNetVisslWrapper(RegNetYaagf()						)     ,)

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

			snake_case_				:					Union[str, Any]   =       partial(
			    __magic_name__     ,"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(
						    __magic_name__     ,names_to_from_model_map[model_name]     ,names_to_ours_model_map[model_name]     ,names_to_config[model_name]     ,__magic_name__     ,__magic_name__     ,)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    __magic_name__     ,names_to_from_model_map[model_name]     ,names_to_ours_model_map[model_name]     ,__magic_name__     ,__magic_name__     ,__magic_name__     ,)
			return config, expected_shape


if __name__ == "__main__":
		__lowerCamelCase   :					Dict   							=					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   :					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 unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters

def 	__UpperCAmelCase       ( )->    int:



			"""simple docstring"""



			snake_case_				:					Any   =       {
			    "repo_name": ["test_repo1", "test_repo2", "test_repo3"],
			    "path": ["test_1.py", "test_2.py", "unit_test.py"],
			    "content": ["a " * 20, "a " * 30, "b " * 7],
			}
			snake_case_				:					int   =       Dataset.from_dict(__magic_name__						)
			return dataset







class    A_	(a_							):
							"""simple docstring"""




							def 							_A	(		self			:List[str]				)				->  str:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       get_dataset()
										snake_case_				:					Optional[int]   =       make_duplicate_clusters(lowerCAmelCase__       ,							0.8_5				)
										self.assertEqual(len(duplicate_clusters[0]				)       ,							2				)






							def 							_A	(		self			:Union[str, Any]				)				->  List[str]:


										'''simple docstring'''




										snake_case_				:					Optional[int]   =       get_dataset()
										snake_case_,	snake_case_				:					List[Any]   =       deduplicate_dataset(lowerCAmelCase__				)
										self.assertEqual(len(lowerCAmelCase__				)       ,							2				)
										print(lowerCAmelCase__				)
										self.assertEqual(duplicate_clusters[0][0]["copies"]       ,							2				)
										self.assertEqual(duplicate_clusters[0][0]["is_extreme"]       ,							lowerCAmelCase__				)




