





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

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__ :			Tuple       	=			logging.get_logger(__name__)







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  nn.Module
					lowerCamelCase  :  List[nn.Module]           =							field(default_factory=SCREAMING_SNAKE_CASE_	)
					lowerCamelCase  :  list           =							field(default_factory=SCREAMING_SNAKE_CASE_	)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Union[str, Any]			,       __lowercase     :	Union[str, Any]			,       __lowercase     :	str			,       __lowercase     :	int		):






						'''simple docstring'''







						UpperCAmelCase_		    =  len(list(m.modules()		)		) == 1 or isinstance(__UpperCAmelCase			,       nn.Convad		) or isinstance(__UpperCAmelCase			,       nn.BatchNormad		)
						if has_not_submodules:
							if not isinstance(__UpperCAmelCase			,       __UpperCAmelCase		):
								self.traced.append(__UpperCAmelCase		)





					def __call__(    self     :	List[str]			,       __lowercase     :	int		):






						'''simple docstring'''







						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 							SCREAMING_SNAKE_CASE				(    self     :	Optional[int]		):






						'''simple docstring'''







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







@dataclass
class 	_UpperCamelCase	:







					'''simple docstring'''

					lowerCamelCase  :  nn.Module
					lowerCamelCase  :  nn.Module
					lowerCamelCase  :  int           =							0
					lowerCamelCase  :  List           =							field(default_factory=SCREAMING_SNAKE_CASE_	)
					lowerCamelCase  :  List           =							field(default_factory=SCREAMING_SNAKE_CASE_	)





					def __call__(    self     :	Dict			,       __lowercase     :	Optional[Any]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  Tracker(self.dest		)(__UpperCAmelCase		).parametrized
						UpperCAmelCase_		    =  Tracker(self.src		)(__UpperCAmelCase		).parametrized

						UpperCAmelCase_		    =  list(filter(lambda __lowercase		: type(__UpperCAmelCase		) not in self.src_skip			,       __UpperCAmelCase		)		)
						UpperCAmelCase_		    =  list(filter(lambda __lowercase		: type(__UpperCAmelCase		) not in self.dest_skip			,       __UpperCAmelCase		)		)

						if len(__UpperCAmelCase		) != len(__UpperCAmelCase		):
							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}"""		)




def  A_(      A					,   A ):
	UpperCAmelCase_		    =  from_model.state_dict()
	UpperCAmelCase_		    =  our_model.state_dict()
	UpperCAmelCase_		    =  our_model.config
	UpperCAmelCase_		    =  []
	for stage_idx in range(len(config.hidden_sizes ) ):
		for block_id in range(config.depths[stage_idx] ):
			UpperCAmelCase_		    =  f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
			UpperCAmelCase_		    =  f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

			all_keys.append((from_key, to_key) )
			UpperCAmelCase_		    =  f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
			UpperCAmelCase_		    =  f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

			all_keys.append((from_key, to_key) )

	for from_key, to_key in all_keys:
		UpperCAmelCase_		    =  from_state_dict.pop(A )

	our_model.load_state_dict(A )
	return our_model




def  A_(      A					,   A					,   A					,   A					,   A					,   A = True					,   ):
	print(f"""Downloading weights for {name}...""" )
	UpperCAmelCase_		    =  cached_download(A )
	print(f"""Converting {name}...""" )
	UpperCAmelCase_		    =  torch.load(A )["""state_dict"""]
	from_model.load_state_dict(A )
	from_model.eval()
	with torch.no_grad():
		UpperCAmelCase_		    =  VanForImageClassification(A ).eval()
		UpperCAmelCase_		    =  ModuleTransfer(src=A					,   dest=A )
		UpperCAmelCase_		    =  torch.randn((1, 3, 224, 224) )
		module_transfer(A )
		UpperCAmelCase_		    =  copy_parameters(A					,   A )

	if not torch.allclose(from_model(A )					,   our_model(A ).logits ):
		raise ValueError("""The model logits don't match the original one.""" )

	UpperCAmelCase_		    =  name
	print(A )

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

		# we can use the convnext one
		UpperCAmelCase_		    =  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=A					,   )

		print(f"""Pushed {checkpoint_name}""" )




def  A_(      A					,   A = None					,   A = True ):
	UpperCAmelCase_		    =  """imagenet-1k-id2label.json"""
	UpperCAmelCase_		    =  1000

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

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

	UpperCAmelCase_		    =  partial(A					,   num_labels=A					,   idalabel=A					,   labelaid=A )

	UpperCAmelCase_		    =  {
	    """van-tiny""": ImageNetPreTrainedConfig(
	        hidden_sizes=[32, 64, 160, 256]					,   depths=[3, 3, 5, 2]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
	    """van-small""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]					,   depths=[2, 2, 4, 2]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
	    """van-base""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]					,   depths=[3, 3, 12, 3]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
	    """van-large""": ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 128, 320, 512]					,   depths=[3, 5, 27, 3]					,   mlp_ratios=[8, 8, 4, 4]					,   ),
	}

	UpperCAmelCase_		    =  {
	    """van-tiny""": van_tiny,
	    """van-small""": van_small,
	    """van-base""": van_base,
	    """van-large""": van_large,
	}

	UpperCAmelCase_		    =  {
	    """van-tiny""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
	    ),
	    """van-small""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
	    ),
	    """van-base""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
	    ),
	    """van-large""": (
	        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
	    ),
	}

	if model_name:
		convert_weight_and_push(
		    A					,   names_to_config[model_name]					,   checkpoint=names_to_original_checkpoints[model_name]					,   from_model=names_to_original_models[model_name]()					,   save_directory=A					,   push_to_hub=A					,   )
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    A					,   A					,   checkpoint=names_to_original_checkpoints[model_name]					,   from_model=names_to_original_models[model_name]()					,   save_directory=A					,   push_to_hub=A					,   )


if __name__ == "__main__":
				UpperCamelCase__ :			Union[str, 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: van-tiny/small/base/large. 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(
				    """--van_dir""",
				    required=True,
				    type=Path,
				    help=(
				        """A path to VAN\'s original implementation directory. You can download from here:"""
				        """ https://github.com/Visual-Attention-Network/VAN-Classification"""
				    ),
				)
				parser.add_argument(
				    """--push_to_hub""",
				    default=True,
				    type=bool,
				    required=False,
				    help="""If True, push model and image processor to the hub.""",
				)

				UpperCamelCase__ :			List[Any]       	=			parser.parse_args()
				UpperCamelCase__ :			Optional[Any]       	=			args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				UpperCamelCase__ :			int       	=			args.van_dir
				# append the path to the parents to maskformer dir
				sys.path.append(str(van_dir.parent))
				from van.models.van import van_base, van_large, van_small, van_tiny

				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




"""simple docstring"""






import copy

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


UpperCamelCase_						   =   logging.get_logger(__name__)





class 							snake_case       (     SCREAMING_SNAKE_CASE_		):
   a_					:		Tuple     					=     """hybrid-clip"""
   a_					:		Optional[Any]     					=     True







   def __init__( self       ,							__UpperCAmelCase=5_12       ,							**__UpperCAmelCase)   ->Dict:
      super().__init__(**__UpperCAmelCase)

      if "text_config" not in kwargs:
         raise ValueError("`text_config` can not be `None`.")

      if "vision_config" not in kwargs:
         raise ValueError("`vision_config` can not be `None`.")

      a_						   =     kwargs.pop("text_config")
      a_						   =     kwargs.pop("vision_config")

      a_						   =     text_config.pop("model_type")
      a_						   =     vision_config.pop("model_type")

      from transformers import AutoConfig

      a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      if vision_model_type == "clip":
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase).vision_config
      elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         a_						   =     CLIPVisionConfig(**__UpperCAmelCase)
      else:
         a_						   =     AutoConfig.for_model(__UpperCAmelCase       ,							**__UpperCAmelCase)

      a_						   =     projection_dim
      a_						   =     1.0







   @classmethod
   def    UpperCAmelCase__	( cls       ,							__UpperCAmelCase       ,							__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Optional[int]:
      return cls(text_config=text_config.to_dict()       ,							vision_config=vision_config.to_dict()       ,							**__UpperCAmelCase)







   def    UpperCAmelCase__	( self)   ->str:
      a_						   =     copy.deepcopy(self.__dict__)
      a_						   =     self.text_config.to_dict()
      a_						   =     self.vision_config.to_dict()
      a_						   =     self.__class__.model_type
      return output