

'''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()
_a			:	Dict								=  logging.get_logger(__name__)



@dataclass
class     lowercase_		:

  '''simple docstring'''





  __lowerCAmelCase   :	nn.Module
  __lowerCAmelCase   :	List[nn.Module]      					=				field(default_factory=a	)
  __lowerCAmelCase   :	list      					=				field(default_factory=a	)
  def   snake_case_     (       self				,	a_				,	a_				,	a_       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		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				,	a_       )							->			Dict:


    """simple docstring"""
    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   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    # 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     lowercase_		:

  '''simple docstring'''





  __lowerCAmelCase   :	nn.Module
  __lowerCAmelCase   :	nn.Module
  __lowerCAmelCase   :	int      					=				0
  __lowerCAmelCase   :	List      					=				field(default_factory=a	)
  __lowerCAmelCase   :	List      					=				field(default_factory=a	)
  def __call__(       self				,	a_       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		Tracker(self.dest       )(a_       ).parametrized
    UpperCAmelCase  							=		Tracker(self.src       )(a_       ).parametrized

    UpperCAmelCase  							=		list(filter(lambda a_       : type(a_       ) not in self.src_skip				,	a_       )       )
    UpperCAmelCase  							=		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  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				nn.Module    ,				SCREAMING_SNAKE_CASE :				nn.Module  ):
  # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
  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(SCREAMING_SNAKE_CASE  )

  our_model.load_state_dict(SCREAMING_SNAKE_CASE  )
  return our_model


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				VanConfig    ,				SCREAMING_SNAKE_CASE :				str    ,				SCREAMING_SNAKE_CASE :				nn.Module    ,				SCREAMING_SNAKE_CASE :				Path    ,				SCREAMING_SNAKE_CASE :				bool = True    ,				):
  print(f'''Downloading weights for {name}...'''  )
  UpperCAmelCase  							=		cached_download(SCREAMING_SNAKE_CASE  )
  print(f'''Converting {name}...'''  )
  UpperCAmelCase  							=		torch.load(SCREAMING_SNAKE_CASE  )['state_dict']
  from_model.load_state_dict(SCREAMING_SNAKE_CASE  )
  from_model.eval()
  with torch.no_grad():
    UpperCAmelCase  							=		VanForImageClassification(SCREAMING_SNAKE_CASE  ).eval()
    UpperCAmelCase  							=		ModuleTransfer(src=SCREAMING_SNAKE_CASE    ,				dest=SCREAMING_SNAKE_CASE  )
    UpperCAmelCase  							=		torch.randn((1, 3, 224, 224)  )
    module_transfer(SCREAMING_SNAKE_CASE  )
    UpperCAmelCase  							=		copy_parameters(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  )

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

  UpperCAmelCase  							=		name
  print(SCREAMING_SNAKE_CASE  )

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

    # 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=SCREAMING_SNAKE_CASE    ,				)

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


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				Path    ,				SCREAMING_SNAKE_CASE :				str = None    ,				SCREAMING_SNAKE_CASE :				bool = True  ):
  UpperCAmelCase  							=		'imagenet-1k-id2label.json'
  UpperCAmelCase  							=		1000

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

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

  UpperCAmelCase  							=		partial(SCREAMING_SNAKE_CASE    ,				num_labels=SCREAMING_SNAKE_CASE    ,				idalabel=SCREAMING_SNAKE_CASE    ,				labelaid=SCREAMING_SNAKE_CASE  )

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


if __name__ == "__main__":
  _a			:	int								=  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.',
  )

  _a			:	Any								=  parser.parse_args()
  _a			:	Path								=  args.pytorch_dump_folder_path
  pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  _a			:	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'''




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str  ):
  UpperCAmelCase  							=		str(SCREAMING_SNAKE_CASE  ).strip()
  if not bin_string:
    raise ValueError('Empty string was passed to the function'  )
  UpperCAmelCase  							=		bin_string[0] == '-'
  if is_negative:
    UpperCAmelCase  							=		bin_string[1:]
  if not all(char in '01' for char in bin_string  ):
    raise ValueError('Non-binary value was passed to the function'  )
  UpperCAmelCase  							=		0
  for char in bin_string:
    UpperCAmelCase  							=		2 * decimal_number + int(SCREAMING_SNAKE_CASE  )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
  from doctest import testmod

  testmod()


