'''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()
__lowercase        =    logging.get_logger()






@dataclass
class 							a__:


			'''simple docstring'''






			UpperCAmelCase_     :	nn.Module
			UpperCAmelCase_     :	List[nn.Module]						=			field(default_factory=lowerCAmelCase__      )
			UpperCAmelCase_     :	list						=			field(default_factory=lowerCAmelCase__      )



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    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 				a_			(  self):




					"""simple docstring"""


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







@dataclass
class 							a__:


			'''simple docstring'''






			UpperCAmelCase_     :	nn.Module
			UpperCAmelCase_     :	nn.Module
			UpperCAmelCase_     :	int						=			1
			UpperCAmelCase_     :	List						=			field(default_factory=lowerCAmelCase__      )
			UpperCAmelCase_     :	List						=			field(default_factory=lowerCAmelCase__      )
			UpperCAmelCase_     :	bool						=			True



			def __call__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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

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




					"""simple docstring"""


					super().__init__()

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

					lowerCAmelCase     =    nn.ModuleDict(__lowerCAmelCase)







			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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







			def __getitem__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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

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

					return val







class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			def __getitem__(  self			,       __lowerCAmelCase):




					"""simple docstring"""


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







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

		'''simple docstring'''


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






def 							snake_case__				(     _A:   str ,  _A:   Callable[[], nn.Module] ,  _A:   Callable[[], nn.Module] ,  _A:   RegNetConfig ,  _A:   Path ,  _A:   bool = True ,  )       ->      Tuple:

		'''simple docstring'''


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

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

		lowerCAmelCase     =    our_model(_A ,  output_hidden_states=_A					)
		lowerCAmelCase     =    (
		    our_outputs.logits if isinstance(_A ,  _A					) else our_outputs.last_hidden_state
		)

		lowerCAmelCase     =    from_model(_A					)
		lowerCAmelCase     =    from_output[-1] if type(_A					) 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:
				lowerCAmelCase     =    our_outputs.hidden_states[-1]

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

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

				print(f"Pushed {name}"					)






def 							snake_case__				(     _A:   Path ,  _A:   str = None ,  _A:   bool = True					)       ->      Any:

		'''simple docstring'''


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

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

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

		lowerCAmelCase     =    partial(_A ,  num_labels=_A ,  idalabel=_A ,  labelaid=_A					)

		lowerCAmelCase     =    {
		    """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, 11110, 28280] ,  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, 11110, 28280] ,  groups_width=1010					),
		}

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

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

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

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

		lowerCAmelCase     =    partial(
		    _A ,  """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()					) ,  )

		lowerCAmelCase     =    partial(
		    _A ,  """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
		lowerCAmelCase     =    partial(
		    _A ,  """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch""" ,  lambda: FakeRegNetVisslWrapper(RegNetYaagf()					) ,  )

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

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

		lowerCAmelCase     =    partial(
		    _A ,  """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(
				    _A ,  names_to_from_model_map[model_name] ,  names_to_ours_model_map[model_name] ,  names_to_config[model_name] ,  _A ,  _A ,  )
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    _A ,  names_to_from_model_map[model_name] ,  names_to_ours_model_map[model_name] ,  _A ,  _A ,  _A ,  )
		return config, expected_shape


if __name__ == "__main__":
						__lowercase        =    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.''',
						)

						__lowercase        =    parser.parse_args()

						__lowercase        =    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 os
from distutils.util import strtobool






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

		'''simple docstring'''


		for e in env_keys:
				lowerCAmelCase     =    int(os.environ.get(_A ,  -1					)					)
				if val >= 0:
						return val
		return default






def 							snake_case__				(     _A:   str ,  _A:   List[Any]=False					)       ->      int:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return strtobool(_A					) == 1  # As its name indicates `strtobool` actually returns an int...






def 							snake_case__				(     _A:   Dict ,  _A:   List[Any]="no"					)       ->      str:

		'''simple docstring'''


		lowerCAmelCase     =    os.environ.get(_A ,  str(_A					)					)
		return value




