

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 				a  :
						_lowercase    						=			42
						_lowercase    						=			field(default_factory=UpperCAmelCase				)
						_lowercase    						=			field(default_factory=UpperCAmelCase				)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


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


						def __call__(       self						,					A_	):







										'''simple docstring'''


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





						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







@dataclass
class 				a  :
						_lowercase    						=			42
						_lowercase    						=			42
						_lowercase    						=			1
						_lowercase    						=			field(default_factory=UpperCAmelCase				)
						_lowercase    						=			field(default_factory=UpperCAmelCase				)
						_lowercase    						=			True


						def __call__(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    Tracker(self.dest	)(A_	).parametrized
										_UpperCAmelCase       :		List[Any] 		=    Tracker(self.src	)(A_	).parametrized

										_UpperCAmelCase       :		Optional[int] 		=    list(filter(lambda A_	: type(A_	) not in self.src_skip						,					A_	)	)
										_UpperCAmelCase       :		Optional[int] 		=    list(filter(lambda A_	: type(A_	) not in self.dest_skip						,					A_	)	)

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

										for dest_m, src_m in zip(A_						,					A_	):
														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				):


						def __init__(       self						,					A_	):







										'''simple docstring'''


										super().__init__()

										_UpperCAmelCase       :		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}'
														_UpperCAmelCase       :		Optional[Any] 		=    len(A_	) + 1
														feature_blocks.append((f'res{block_index}', v)	)

										_UpperCAmelCase       :		Dict 		=    nn.ModuleDict(A_	)





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


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







class 				a  (	UpperCAmelCase				):


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Union[str, Any] 		=    x.split("-"	)
										return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)





						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										if x not in self:
														_UpperCAmelCase       :		Dict 		=    self.convert_name_to_timm(A_	)
														_UpperCAmelCase       :		Dict 		=    partial(lambda: (timm.create_model(A_						,					pretrained=A_	).eval(), None)	)

										else:
														_UpperCAmelCase       :		Union[str, Any] 		=    super().__getitem__(A_	)

										return val







class 				a  (	UpperCAmelCase				):


						def __getitem__(       self						,					A_	):







										'''simple docstring'''


										if "seer" in x and "in1k" not in x:
														_UpperCAmelCase       :		Tuple 		=    RegNetModel
										else:
														_UpperCAmelCase       :		int 		=    RegNetForImageClassification
										return val







def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  List[Tuple[str, str]] )   ->       str:
				for from_key, to_key in keys:
								_UpperCAmelCase       :		Union[str, Any] 		=    from_state_dict[from_key].clone()
								print(F'Copied key={from_key} to={to_key}' )
				return to_state_dict

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  Callable[[], nn.Module]  ,      lowerCAmelCase:  Callable[[], nn.Module]  ,      lowerCAmelCase:  RegNetConfig  ,      lowerCAmelCase:  Path  ,      lowerCAmelCase:  bool = True  ,      )   ->       Union[str, Any]:
				print(F'Converting {name}...' )
				with torch.no_grad():
								_UpperCAmelCase		,							_UpperCAmelCase       :		str 		=    from_model_func()
								_UpperCAmelCase       :		Union[str, Any] 		=    our_model_func(lowerCAmelCase ).eval()
								_UpperCAmelCase       :		Optional[int] 		=    ModuleTransfer(src=lowerCAmelCase  ,      dest=lowerCAmelCase  ,      raise_if_mismatch=lowerCAmelCase )
								_UpperCAmelCase       :		List[Any] 		=    torch.randn((1, 3, 224, 224) )
								module_transfer(lowerCAmelCase )

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

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

				_UpperCAmelCase       :		Tuple 		=    from_model(lowerCAmelCase )
				_UpperCAmelCase       :		int 		=    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:
								_UpperCAmelCase       :		Any 		=    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  ,      )

								_UpperCAmelCase       :		str 		=    224 if "seer" not in name else 384
								# we can use the convnext one
								_UpperCAmelCase       :		Any 		=    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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Path  ,      lowerCAmelCase:  str = None  ,      lowerCAmelCase:  bool = True )   ->       Tuple:
				_UpperCAmelCase       :		Union[str, Any] 		=    "imagenet-1k-id2label.json"
				_UpperCAmelCase       :		Any 		=    1000
				_UpperCAmelCase       :		Dict 		=    (1, num_labels)

				_UpperCAmelCase       :		Tuple 		=    "huggingface/label-files"
				_UpperCAmelCase       :		Dict 		=    num_labels
				_UpperCAmelCase       :		Optional[Any] 		=    json.load(open(cached_download(hf_hub_url(lowerCAmelCase  ,      lowerCAmelCase  ,      repo_type="dataset" ) )  ,      "r" ) )
				_UpperCAmelCase       :		Union[str, Any] 		=    {int(lowerCAmelCase ): v for k, v in idalabel.items()}

				_UpperCAmelCase       :		Optional[Any] 		=    idalabel
				_UpperCAmelCase       :		Any 		=    {v: k for k, v in idalabel.items()}

				_UpperCAmelCase       :		Union[str, Any] 		=    partial(lowerCAmelCase  ,      num_labels=lowerCAmelCase  ,      idalabel=lowerCAmelCase  ,      labelaid=lowerCAmelCase )

				_UpperCAmelCase       :		str 		=    {
				    "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 ),
				}

				_UpperCAmelCase       :		Any 		=    NameToOurModelFuncMap()
				_UpperCAmelCase       :		Optional[int] 		=    NameToFromModelFuncMap()
				# add seer weights logic

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

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

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

				_UpperCAmelCase       :		List[str] 		=    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() )  ,      )

				_UpperCAmelCase       :		Union[str, Any] 		=    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
				_UpperCAmelCase       :		Tuple 		=    partial(
				    lowerCAmelCase  ,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"  ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,      )

				_UpperCAmelCase       :		Union[str, Any] 		=    partial(
				    lowerCAmelCase  ,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"  ,      lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,      )

				_UpperCAmelCase       :		List[Any] 		=    partial(
				    lowerCAmelCase  ,      "https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"  ,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )  ,      )

				_UpperCAmelCase       :		List[Any] 		=    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__":
					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)





import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use DeformableDetrImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


