
"""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()
_A						=			logging.get_logger()






@dataclass
class 	_lowercase       :
	lowercase_				=     4_2
	lowercase_				=     field(default_factory=__a		)
	lowercase_				=     field(default_factory=__a		)




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_		,			UpperCAmelCase_ )		->							Dict:
			lowerCamelCase					:      Optional[Any]          =   len(list(m.modules() ) ) == 1 or isinstance(UpperCamelCase__		,			nn.Convad ) or isinstance(UpperCamelCase__		,			nn.BatchNormad )
			if has_not_submodules:
					self.traced.append(UpperCamelCase__ )




	def __call__(			self		,			UpperCAmelCase_ )		->							Any:



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




	@property
	def       _UpperCamelCase   (			self )		->							List[Any]:
			return list(filter(lambda UpperCAmelCase_ : len(list(x.state_dict().keys() ) ) > 0		,			self.traced ) )






@dataclass
class 	_lowercase       :
	lowercase_				=     4_2
	lowercase_				=     4_2
	lowercase_				=     1
	lowercase_				=     field(default_factory=__a		)
	lowercase_				=     field(default_factory=__a		)
	lowercase_				=     True




	def __call__(			self		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      List[Any]          =   Tracker(self.dest )(UpperCamelCase__ ).parametrized
			lowerCamelCase					:      Dict          =   Tracker(self.src )(UpperCamelCase__ ).parametrized

			lowerCamelCase					:      str          =   list(filter(lambda UpperCAmelCase_ : type(UpperCamelCase__ ) not in self.src_skip		,			UpperCamelCase__ ) )
			lowerCamelCase					:      Union[str, Any]          =   list(filter(lambda UpperCAmelCase_ : type(UpperCamelCase__ ) not in self.dest_skip		,			UpperCamelCase__ ) )

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

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






class 	_lowercase       (    nn.Module		):




	def __init__(			self		,			UpperCAmelCase_ )		->							Dict:
			super().__init__()

			lowerCamelCase					:      Dict          =   []
			# - 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					:      List[str]          =   len(UpperCamelCase__ ) + 1
					feature_blocks.append((F"""res{block_index}""", v) )

			lowerCamelCase					:      List[Any]          =   nn.ModuleDict(UpperCamelCase__ )




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Tuple:
			return get_trunk_forward_outputs(
			    UpperCamelCase__		,			out_feat_keys=UpperCamelCase__		,			feature_blocks=self._feature_blocks		,			)






class 	_lowercase       (    __a		):




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							str:
			lowerCamelCase					:      List[Any]          =   x.split('-' )
			return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )




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



			if x not in self:
					lowerCamelCase					:      List[str]          =   self.convert_name_to_timm(UpperCamelCase__ )
					lowerCamelCase					:      List[str]          =   partial(lambda: (timm.create_model(UpperCamelCase__		,			pretrained=UpperCamelCase__ ).eval(), None) )

			else:
					lowerCamelCase					:      List[str]          =   super().__getitem__(UpperCamelCase__ )

			return val






class 	_lowercase       (    __a		):




	def __getitem__(			self		,			UpperCAmelCase_ )		->							Callable[[], nn.Module]:



			if "seer" in x and "in1k" not in x:
					lowerCamelCase					:      Optional[Any]          =   RegNetModel
			else:
					lowerCamelCase					:      Dict          =   RegNetForImageClassification
			return val


def 		UpperCAmelCase (		a_,						a_,						a_      ):





		'''simple docstring'''


		for from_key, to_key in keys:
				lowerCamelCase					:      Any          =   from_state_dict[from_key].clone()
				print(F"""Copied key={from_key} to={to_key}"""      )
		return to_state_dict
def 		UpperCAmelCase (		a_,						a_,						a_,						a_,						a_,						a_ = True,						):





		'''simple docstring'''


		print(F"""Converting {name}..."""      )
		with torch.no_grad():
				lowerCamelCase     ,   lowerCamelCase					:      Union[str, Any]          =   from_model_func()
				lowerCamelCase					:      Optional[int]          =   our_model_func(__UpperCamelCase      ).eval()
				lowerCamelCase					:      str          =   ModuleTransfer(src=__UpperCamelCase,						dest=__UpperCamelCase,						raise_if_mismatch=__UpperCamelCase      )
				lowerCamelCase					:      Optional[Any]          =   torch.randn((1, 3, 224, 224)      )
				module_transfer(__UpperCamelCase      )

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

		lowerCamelCase					:      List[str]          =   our_model(__UpperCamelCase,						output_hidden_states=__UpperCamelCase      )
		lowerCamelCase					:      Dict          =   (
		    our_outputs.logits if isinstance(__UpperCamelCase,						__UpperCamelCase      ) else our_outputs.last_hidden_state
		)

		lowerCamelCase					:      int          =   from_model(__UpperCamelCase      )
		lowerCamelCase					:      List[str]          =   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:
				lowerCamelCase					:      Union[str, Any]          =   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,						)

				lowerCamelCase					:      List[str]          =   224 if 'seer' not in name else 384
				# we can use the convnext one
				lowerCamelCase					:      Tuple          =   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 		UpperCAmelCase (		a_,						a_ = None,						a_ = True      ):





		'''simple docstring'''


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

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

		lowerCamelCase					:      Optional[int]          =   idalabel
		lowerCamelCase					:      str          =   {v: k for k, v in idalabel.items()}

		lowerCamelCase					:      str          =   partial(__UpperCamelCase,						num_labels=__UpperCamelCase,						idalabel=__UpperCamelCase,						labelaid=__UpperCamelCase      )

		lowerCamelCase					:      Optional[Any]          =   {
		    '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      ),
		}

		lowerCamelCase					:      str          =   NameToOurModelFuncMap()
		lowerCamelCase					:      Union[str, Any]          =   NameToFromModelFuncMap()
		# add seer weights logic

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

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

		lowerCamelCase					:      Union[str, Any]          =   partial(
		    __UpperCamelCase,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch',						lambda: FakeRegNetVisslWrapper(RegNetYaagf()      ),						)

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

		lowerCamelCase					:      Union[str, Any]          =   partial(
		    __UpperCamelCase,						'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=2.5_2      )      )      ),						)

		# IN1K finetuned
		lowerCamelCase					:      Optional[int]          =   partial(
		    __UpperCamelCase,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch',						lambda: FakeRegNetVisslWrapper(RegNetYaagf()      ),						)

		lowerCamelCase					:      List[str]          =   partial(
		    __UpperCamelCase,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch',						lambda: FakeRegNetVisslWrapper(RegNetYaagf()      ),						)

		lowerCamelCase					:      List[str]          =   partial(
		    __UpperCamelCase,						'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch',						lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      ),						)

		lowerCamelCase					:      Optional[int]          =   partial(
		    __UpperCamelCase,						'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=2.5_2      )      )      ),						)

		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__":
					_A						=			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.',
					)

					_A						=			parser.parse_args()

					_A						=			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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






