






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_       :Tuple       =						logging.get_logger()




@dataclass
class 				__A     :





				"""simple docstring"""







				UpperCamelCase__ :      nn.Module
				UpperCamelCase__ :      List[nn.Module]			 =field(default_factory=a )
				UpperCamelCase__ :      list			 =field(default_factory=a )


				def       __lowercase							( self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Dict       		=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       __lowercase							( 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=a )
				UpperCamelCase__ :      List			 =field(default_factory=a )
				UpperCamelCase__ :      bool			 =True


				def __call__( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									__UpperCamelCase      :					Any       		=Tracker(self.dest    )(lowerCamelCase__    ).parametrized
									__UpperCamelCase      :					Dict       		=Tracker(self.src    )(lowerCamelCase__    ).parametrized

									__UpperCamelCase      :					Union[str, Any]       		=list(filter(lambda lowerCamelCase__    : type(lowerCamelCase__    ) not in self.src_skip							,  lowerCamelCase__    )    )
									__UpperCamelCase      :					Tuple       		=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__()

									__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      :					Dict       		=len(lowerCamelCase__    ) + 1
														feature_blocks.append((f'res{block_index}', v)    )

									__UpperCamelCase      :					Any       		=nn.ModuleDict(lowerCamelCase__    )







				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

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




class 				__A     (			a ):





				"""simple docstring"""







				def       __lowercase							( self							,  lowerCamelCase__    ):




									"""simple docstring"""

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







				def __getitem__( self							,  lowerCamelCase__    ):




									"""simple docstring"""

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

									else:
														__UpperCamelCase      :					Dict       		=super().__getitem__(lowerCamelCase__    )

									return val




class 				__A     (			a ):





				"""simple docstring"""







				def __getitem__( self							,  lowerCamelCase__    ):




									"""simple docstring"""

									if "seer" in x and "in1k" not in x:
														__UpperCamelCase      :					str       		=RegNetModel
									else:
														__UpperCamelCase      :					Union[str, Any]       		=RegNetForImageClassification
									return val






def 							A     (       a_							,a_							,a_					)     ->						Any:
					for from_key, to_key in keys:
										__UpperCamelCase      :					str       		=from_state_dict[from_key].clone()
										print(F'Copied key={from_key} to={to_key}'					)
					return to_state_dict






def 							A     (       a_							,a_							,a_							,a_							,a_							,a_ = True							,)     ->						Union[str, Any]:
					print(F'Converting {name}...'					)
					with torch.no_grad():
										__UpperCamelCase		,				__UpperCamelCase      :					str       		=from_model_func()
										__UpperCamelCase      :					Dict       		=our_model_func(a_					).eval()
										__UpperCamelCase      :					List[Any]       		=ModuleTransfer(src=a_							,dest=a_							,raise_if_mismatch=a_					)
										__UpperCamelCase      :					List[str]       		=torch.randn((1, 3, 224, 224)					)
										module_transfer(a_					)

					if from_state_dict is not None:
										__UpperCamelCase      :					Dict       		=[]
										# 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      :					List[str]       		=manually_copy_vissl_head(a_							,our_model.state_dict()							,a_					)
										our_model.load_state_dict(a_					)

					__UpperCamelCase      :					Optional[Any]       		=our_model(a_							,output_hidden_states=a_					)
					__UpperCamelCase      :					Union[str, Any]       		=(
					    our_outputs.logits if isinstance(a_							,a_					) else our_outputs.last_hidden_state
					)

					__UpperCamelCase      :					int       		=from_model(a_					)
					__UpperCamelCase      :					Optional[Any]       		=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:
										__UpperCamelCase      :					Optional[Any]       		=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_							,)

										__UpperCamelCase      :					List[Any]       		=224 if 'seer' not in name else 384
										# we can use the convnext one
										__UpperCamelCase      :					List[str]       		=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 							A     (       a_							,a_ = None							,a_ = True					)     ->						Union[str, Any]:
					__UpperCamelCase      :					Optional[Any]       		='imagenet-1k-id2label.json'
					__UpperCamelCase      :					str       		=1_000
					__UpperCamelCase      :					int       		=(1, num_labels)

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

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

					__UpperCamelCase      :					Tuple       		=partial(a_							,num_labels=a_							,idalabel=a_							,labelaid=a_					)

					__UpperCamelCase      :					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, 1_008]							,groups_width=48							,layer_type='x'					),
					    'regnet-x-040': ImageNetPreTrainedConfig(
					        depths=[2, 5, 14, 2]							,hidden_sizes=[80, 240, 560, 1_360]							,groups_width=40							,layer_type='x'					),
					    'regnet-x-064': ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]							,hidden_sizes=[168, 392, 784, 1_624]							,groups_width=56							,layer_type='x'					),
					    'regnet-x-080': ImageNetPreTrainedConfig(
					        depths=[2, 5, 15, 1]							,hidden_sizes=[80, 240, 720, 1_920]							,groups_width=120							,layer_type='x'					),
					    'regnet-x-120': ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]							,hidden_sizes=[224, 448, 896, 2_240]							,groups_width=112							,layer_type='x'					),
					    'regnet-x-160': ImageNetPreTrainedConfig(
					        depths=[2, 6, 13, 1]							,hidden_sizes=[256, 512, 896, 2_048]							,groups_width=128							,layer_type='x'					),
					    'regnet-x-320': ImageNetPreTrainedConfig(
					        depths=[2, 7, 13, 1]							,hidden_sizes=[336, 672, 1_344, 2_520]							,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, 1_512]							,groups_width=24					),
					    'regnet-y-040': ImageNetPreTrainedConfig(
					        depths=[2, 6, 12, 2]							,hidden_sizes=[128, 192, 512, 1_088]							,groups_width=64					),
					    'regnet-y-064': ImageNetPreTrainedConfig(
					        depths=[2, 7, 14, 2]							,hidden_sizes=[144, 288, 576, 1_296]							,groups_width=72					),
					    'regnet-y-080': ImageNetPreTrainedConfig(
					        depths=[2, 4, 10, 1]							,hidden_sizes=[168, 448, 896, 2_016]							,groups_width=56					),
					    'regnet-y-120': ImageNetPreTrainedConfig(
					        depths=[2, 5, 11, 1]							,hidden_sizes=[224, 448, 896, 2_240]							,groups_width=112					),
					    'regnet-y-160': ImageNetPreTrainedConfig(
					        depths=[2, 4, 11, 1]							,hidden_sizes=[224, 448, 1_232, 3_024]							,groups_width=112					),
					    'regnet-y-320': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]							,hidden_sizes=[232, 696, 1_392, 3_712]							,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, 1_392, 3_712]							,groups_width=232					),
					    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]							,hidden_sizes=[328, 984, 1_968, 4_920]							,groups_width=328					),
					    'regnet-y-1280-seer': RegNetConfig(
					        depths=[2, 7, 17, 1]							,hidden_sizes=[528, 1_056, 2_904, 7_392]							,groups_width=264					),
					    'regnet-y-2560-seer': RegNetConfig(
					        depths=[3, 7, 16, 1]							,hidden_sizes=[640, 1_696, 2_544, 5_088]							,groups_width=640					),
					    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]							,hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,groups_width=1_010					),
					    # finetuned on imagenet
					    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]							,hidden_sizes=[232, 696, 1_392, 3_712]							,groups_width=232					),
					    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[2, 5, 12, 1]							,hidden_sizes=[328, 984, 1_968, 4_920]							,groups_width=328					),
					    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]							,hidden_sizes=[528, 1_056, 2_904, 7_392]							,groups_width=264					),
					    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[3, 7, 16, 1]							,hidden_sizes=[640, 1_696, 2_544, 5_088]							,groups_width=640					),
					    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]							,hidden_sizes=[2_020, 4_040, 11_110, 28_280]							,groups_width=1_010					),
					}

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

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

					# pretrained
					__UpperCamelCase      :					Union[str, Any]       		=partial(
					    a_							,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)							,)

					__UpperCamelCase      :					Optional[int]       		=partial(
					    a_							,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)							,)

					__UpperCamelCase      :					str       		=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()					)							,)

					__UpperCamelCase      :					Optional[int]       		=partial(
					    a_							,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'							,lambda: FakeRegNetVisslWrapper(
					        RegNet(RegNetParams(depth=27							,group_width=1_010							,w_a=1_744							,w_a=620.83							,w_m=2.52					)					)					)							,)

					# IN1K finetuned
					__UpperCamelCase      :					Union[str, Any]       		=partial(
					    a_							,'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'							,lambda: FakeRegNetVisslWrapper(RegNetYaagf()					)							,)

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

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

					__UpperCamelCase      :					str       		=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=1_010							,w_a=1_744							,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__":
							A_       :Any       =						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_       :List[Any]       =						parser.parse_args()

							A_       :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"""



import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]    ):





  '''simple docstring'''





  lowercase							    =       []
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight',
          f'stage{idx}.patch_embed.proj.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias',
          f'stage{idx}.patch_embed.proj.bias',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight',
          f'stage{idx}.patch_embed.norm.weight',
      )    )
  embed.append(
      (
          f'cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias',
          f'stage{idx}.patch_embed.norm.bias',
      )    )
  return embed
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  Union[str, Any]		,					__snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked',
          f'stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_q.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_k.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj_v.bias',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight',
          f'stage{idx}.blocks.{cnt}.attn.proj.weight',
      )    )
  attention_weights.append(
      (
          f'cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias',
          f'stage{idx}.blocks.{cnt}.attn.proj.bias',
      )    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight', f'stage{idx}.blocks.{cnt}.mlp.fc2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias', f'stage{idx}.blocks.{cnt}.mlp.fc2.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight', f'stage{idx}.blocks.{cnt}.norm1.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias', f'stage{idx}.blocks.{cnt}.norm1.bias')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight', f'stage{idx}.blocks.{cnt}.norm2.weight')    )
  attention_weights.append(
      (f'cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias', f'stage{idx}.blocks.{cnt}.norm2.bias')    )
  return attention_weights
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[str]    ):





  '''simple docstring'''





  lowercase							    =       []
  token.append((f'cvt.encoder.stages.{idx}.cls_token', 'stage2.cls_token')    )
  return token
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       []
  head.append(('layernorm.weight', 'norm.weight')    )
  head.append(('layernorm.bias', 'norm.bias')    )
  head.append(('classifier.weight', 'head.weight')    )
  head.append(('classifier.bias', 'head.bias')    )
  return head
def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  List[Any]		,					__snake_case			:  Dict		,					__snake_case			:  List[str]		,					__snake_case			:  Union[str, Any]    ):





  '''simple docstring'''





  lowercase							    =       'imagenet-1k-id2label.json'
  lowercase							    =       10_00

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

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

  lowercase							    =       lowercase							    =       CvtConfig(num_labels=__snake_case		,					idalabel=__snake_case		,					labelaid=__snake_case    )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'		,					1    )[-1][4:6] == "13":
    lowercase							    =       [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'		,					1    )[-1][4:6] == "21":
    lowercase							    =       [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    lowercase							    =       [2, 2, 20]
    lowercase							    =       [3, 12, 16]
    lowercase							    =       [1_92, 7_68, 10_24]

  lowercase							    =       CvtForImageClassification(__snake_case    )
  lowercase							    =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'    )
  lowercase							    =       image_size
  lowercase							    =       torch.load(__snake_case		,					map_location=torch.device('cpu'    )    )

  lowercase							    =       OrderedDict()
  lowercase							    =       []

  for idx in range(len(config.depth    )    ):
    if config.cls_token[idx]:
      lowercase							    =       list_of_state_dict + cls_token(__snake_case    )
    lowercase							    =       list_of_state_dict + embeddings(__snake_case    )
    for cnt in range(config.depth[idx]    ):
      lowercase							    =       list_of_state_dict + attention(__snake_case		,					__snake_case    )

  lowercase							    =       list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(__snake_case    )
  for i in range(len(__snake_case    )    ):
    lowercase							    =       original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(__snake_case    )
  model.save_pretrained(__snake_case    )
  image_processor.save_pretrained(__snake_case    )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
    _UpperCamelCase		:  Optional[Any]						=		argparse.ArgumentParser()
    parser.add_argument(
        '--cvt_model',
        default='cvt-w24',
        type=str,
        help='Name of the cvt model you\'d like to convert.',
    )
    parser.add_argument(
        '--image_size',
        default=3_8_4,
        type=int,
        help='Input Image Size',
    )
    parser.add_argument(
        '--cvt_file_name',
        default=R'cvtmodels\CvT-w24-384x384-IN-22k.pth',
        type=str,
        help='Input Image Size',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    _UpperCamelCase		:  Tuple						=		parser.parse_args()
    convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)


