




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     (				lowercase      :			List[Any]	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      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     (				lowercase      :			Optional[int]      ,				lowercase      :			str	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      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     (				lowercase      :			Dict	):


      '''simple docstring'''



      lowerCamelCase_							=       []
      token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')	)
      return token

def      _SCREAMING_SNAKE_CASE     (				):


      '''simple docstring'''



      lowerCamelCase_							=       []
      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     (				lowercase      :			List[str]      ,				lowercase      :			str      ,				lowercase      :			Union[str, Any]      ,				lowercase      :			str	):


      '''simple docstring'''



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

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

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

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

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

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

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

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

      lowerCamelCase_							=       OrderedDict()
      lowerCamelCase_							=       []

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

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

      model.load_state_dict(lowercase	)
      model.save_pretrained(lowercase	)
      image_processor.save_pretrained(lowercase	)


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

if __name__ == "__main__":
 lowerCamelCase							:						str					       =  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=384,
     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."
 )

 lowerCamelCase							:						Optional[int]					       =  parser.parse_args()
 convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)







import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A				:       int      =  logging.get_logger()






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					List[nn.Module]    					=						field(default_factory=_a )
					snake_case_		:					list    					=						field(default_factory=_a )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		Tensor  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Tuple:




											'''simple docstring'''

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


					def __call__(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						List[str]:




											'''simple docstring'''

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






					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

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






@dataclass
class      UpperCamelCase:
					snake_case_		:					nn.Module
					snake_case_		:					nn.Module
					snake_case_		:					int    					=						0
					snake_case_		:					List    					=						field(default_factory=_a )
					snake_case_		:					List    					=						field(default_factory=_a )


					def __call__(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		Tensor    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		Tracker(self.dest    )(SCREAMING_SNAKE_CASE    ).parametrized
											__snake_case				=		Tracker(self.src    )(SCREAMING_SNAKE_CASE    ).parametrized

											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.src_skip  ,     SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		list(filter(lambda SCREAMING_SNAKE_CASE    : type(SCREAMING_SNAKE_CASE    ) not in self.dest_skip  ,     SCREAMING_SNAKE_CASE    )    )

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

											for dest_m, src_m in zip(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    ):
																	dest_m.load_state_dict(src_m.state_dict()    )
																	if self.verbose == 1:
																							print(f'''Transfered from={src_m} to={dest_m}'''    )





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






						print(F'''Converting {name}...'''			)
						with torch.no_grad():
												__snake_case				=		timm.create_model(_lowerCAmelCase   ,						pretrained=_lowerCAmelCase			).eval()
												__snake_case				=		ResNetForImageClassification(_lowerCAmelCase			).eval()
												__snake_case				=		ModuleTransfer(src=_lowerCAmelCase   ,						dest=_lowerCAmelCase			)
												__snake_case				=		torch.randn((1, 3, 224, 224)			)
												module_transfer(_lowerCAmelCase			)

						assert torch.allclose(from_model(_lowerCAmelCase			)   ,						our_model(_lowerCAmelCase			).logits			), "The model logits don't match the original one."

						__snake_case				=		F'''resnet{'-'.join(name.split('resnet'			)			)}'''
						print(_lowerCAmelCase			)

						if push_to_hub:
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add model"   ,						use_temp_dir=_lowerCAmelCase   ,						)

												# we can use the convnext one
												__snake_case				=		AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / checkpoint_name   ,						commit_message="Add image processor"   ,						use_temp_dir=_lowerCAmelCase   ,						)

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





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase = None   ,						_lowerCAmelCase = True			)    ->						Dict:


						'''simple docstring'''






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

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

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

						__snake_case				=		partial(_lowerCAmelCase   ,						num_labels=_lowerCAmelCase   ,						idalabel=_lowerCAmelCase   ,						labelaid=_lowerCAmelCase			)

						__snake_case				=		{
						    "resnet18": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet26": ImageNetPreTrainedConfig(
						        depths=[2, 2, 2, 2]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet34": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[64, 128, 256, 512]   ,						layer_type="basic"			),
						    "resnet50": ImageNetPreTrainedConfig(
						        depths=[3, 4, 6, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet101": ImageNetPreTrainedConfig(
						        depths=[3, 4, 23, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						    "resnet152": ImageNetPreTrainedConfig(
						        depths=[3, 8, 36, 3]   ,						hidden_sizes=[256, 512, 1024, 2048]   ,						layer_type="bottleneck"			),
						}

						if model_name:
												convert_weight_and_push(_lowerCAmelCase   ,						names_to_config[model_name]   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase			)
						return config, expected_shape


if __name__ == "__main__":
	A				:       Optional[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 resnet* architecture,'
	        ' currently: resnet18,26,34,50,101,152. 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				:       Union[str, 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)





