'''simple docstring'''




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()
__SCREAMING_SNAKE_CASE :  Optional[Any]  =						logging.get_logger(__name__)

@dataclass
class 			lowerCamelCase_			:
      '''simple docstring'''

      __UpperCamelCase:						nn.Module
      __UpperCamelCase:						List[nn.Module]      			=		field(default_factory=snake_case__					)
      __UpperCamelCase:						list      			=		field(default_factory=snake_case__					)




      def   _A			( self		:       Tuple		,      A		:       Union[str, Any]		,      A		:       Tensor		,      A		:       Tensor ):
             _UpperCAmelCase  :						Dict							   =					len(list(m.modules() ) ) == 1 or isinstance(A		,      nn.Convad ) or isinstance(A		,      nn.BatchNormad )
             if has_not_submodules:
                    if not isinstance(A		,      A ):
                           self.traced.append(A )




      def __call__( self		:       Union[str, Any]		,      A		:       Tensor ):
             for m in self.module.modules():
                    self.handles.append(m.register_forward_hook(self._forward_hook ) )
             self.module(A )
             [x.remove() for x in self.handles]
             return self





      @property
      def   _A			( self		:       Optional[Any] ):
             # check the len of the state_dict keys to see if we have learnable params
             return list(filter(lambda A : len(list(x.state_dict().keys() ) ) > 0		,      self.traced ) )

@dataclass
class 			lowerCamelCase_			:
      '''simple docstring'''

      __UpperCamelCase:						nn.Module
      __UpperCamelCase:						nn.Module
      __UpperCamelCase:						int      			=		0
      __UpperCamelCase:						List      			=		field(default_factory=snake_case__					)
      __UpperCamelCase:						List      			=		field(default_factory=snake_case__					)




      def __call__( self		:       Dict		,      A		:       Tensor ):
             _UpperCAmelCase  :						Union[str, Any]							   =					Tracker(self.dest )(A ).parametrized
             _UpperCAmelCase  :						List[Any]							   =					Tracker(self.src )(A ).parametrized

             _UpperCAmelCase  :						List[str]							   =					list(filter(lambda A : type(A ) not in self.src_skip		,      A ) )
             _UpperCAmelCase  :						Optional[Any]							   =					list(filter(lambda A : type(A ) not in self.dest_skip		,      A ) )

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

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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   nn.Module    ,			_UpperCAmelCase						:   nn.Module					)					->					nn.Module:


       """simple docstring"""







       _UpperCAmelCase  :						str							   =					from_model.state_dict()
       _UpperCAmelCase  :						Dict							   =					our_model.state_dict()
       _UpperCAmelCase  :						int							   =					our_model.config
       _UpperCAmelCase  :						Union[str, Any]							   =					[]
       for stage_idx in range(len(config.hidden_sizes					)					):
              for block_id in range(config.depths[stage_idx]					):
                     _UpperCAmelCase  :						Tuple							   =					F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
                     _UpperCAmelCase  :						Tuple							   =					F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

                     all_keys.append((from_key, to_key)					)
                     _UpperCAmelCase  :						Optional[int]							   =					F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
                     _UpperCAmelCase  :						Dict							   =					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  :						Union[str, Any]							   =					from_state_dict.pop(_UpperCAmelCase					)

       our_model.load_state_dict(_UpperCAmelCase					)
       return our_model

def 			UpperCamelCase_							(      _UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   VanConfig    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   nn.Module    ,			_UpperCAmelCase						:   Path    ,			_UpperCAmelCase						:   bool = True    ,			)					->					Optional[int]:


       """simple docstring"""







       print(F"""Downloading weights for {name}..."""					)
       _UpperCAmelCase  :						Optional[int]							   =					cached_download(_UpperCAmelCase					)
       print(F"""Converting {name}..."""					)
       _UpperCAmelCase  :						Optional[int]							   =					torch.load(_UpperCAmelCase					)["state_dict"]
       from_model.load_state_dict(_UpperCAmelCase					)
       from_model.eval()
       with torch.no_grad():
              _UpperCAmelCase  :						str							   =					VanForImageClassification(_UpperCAmelCase					).eval()
              _UpperCAmelCase  :						Optional[int]							   =					ModuleTransfer(src=_UpperCAmelCase    ,			dest=_UpperCAmelCase					)
              _UpperCAmelCase  :						Optional[int]							   =					torch.randn((1, 3, 224, 224)					)
              module_transfer(_UpperCAmelCase					)
              _UpperCAmelCase  :						Optional[int]							   =					copy_parameters(_UpperCAmelCase    ,			_UpperCAmelCase					)

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

       _UpperCAmelCase  :						Optional[Any]							   =					name
       print(_UpperCAmelCase					)

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

              # we can use the convnext one
              _UpperCAmelCase  :						List[str]							   =					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=_UpperCAmelCase    ,			)

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

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Path    ,			_UpperCAmelCase						:   str = None    ,			_UpperCAmelCase						:   bool = True					)					->					Any:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					"imagenet-1k-id2label.json"
       _UpperCAmelCase  :						Optional[Any]							   =					1_000

       _UpperCAmelCase  :						Optional[int]							   =					"huggingface/label-files"
       _UpperCAmelCase  :						List[Any]							   =					num_labels
       _UpperCAmelCase  :						Any							   =					json.load(open(hf_hub_download(_UpperCAmelCase    ,			_UpperCAmelCase    ,			repo_type="dataset"					)    ,			"r"					)					)
       _UpperCAmelCase  :						str							   =					{int(_UpperCAmelCase					): v for k, v in idalabel.items()}

       _UpperCAmelCase  :						List[str]							   =					idalabel
       _UpperCAmelCase  :						Optional[Any]							   =					{v: k for k, v in idalabel.items()}

       _UpperCAmelCase  :						str							   =					partial(_UpperCAmelCase    ,			num_labels=_UpperCAmelCase    ,			idalabel=_UpperCAmelCase    ,			labelaid=_UpperCAmelCase					)

       _UpperCAmelCase  :						Any							   =					{
           "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  :						Optional[Any]							   =					{
           "van-tiny": van_tiny,
           "van-small": van_small,
           "van-base": van_base,
           "van-large": van_large,
       }

       _UpperCAmelCase  :						Optional[Any]							   =					{
           "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(
                  _UpperCAmelCase    ,			names_to_config[model_name]    ,			checkpoint=names_to_original_checkpoints[model_name]    ,			from_model=names_to_original_models[model_name]()    ,			save_directory=_UpperCAmelCase    ,			push_to_hub=_UpperCAmelCase    ,			)
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         _UpperCAmelCase    ,			_UpperCAmelCase    ,			checkpoint=names_to_original_checkpoints[model_name]    ,			from_model=names_to_original_models[model_name]()    ,			save_directory=_UpperCAmelCase    ,			push_to_hub=_UpperCAmelCase    ,			)


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE :  List[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.""",
  )

  __SCREAMING_SNAKE_CASE :  Dict  =						parser.parse_args()
  __SCREAMING_SNAKE_CASE :  Path  =						args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  __SCREAMING_SNAKE_CASE :  List[str]  =						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 abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


__SCREAMING_SNAKE_CASE :  Any  =						TypeVar("""T""", bound="""TaskTemplate""")

@dataclass(frozen=snake_case__					)
class 			lowerCamelCase_			(abc.ABC					):
      '''simple docstring'''

      __UpperCamelCase:						str
      __UpperCamelCase:						ClassVar[Features]
      __UpperCamelCase:						ClassVar[Features]




      def   _A			( self		:       T		,      A		:       Features ):
             return copy.deepcopy(self )




      @property
      def   _A			( self		:       List[str] ):
             return Features(**self.input_schema		,      **self.label_schema )




      @property
      @abc.abstractmethod
      def   _A			( self		:       Optional[int] ):
             raise NotImplementedError





      @classmethod
      def   _A			( cls		:       Type[T]		,      A		:       dict ):
             _UpperCAmelCase  :						Optional[Any]							   =					{f.name for f in dataclasses.fields(cls )}
             return cls(**{k: v for k, v in template_dict.items() if k in field_names} )

