
'''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							     = logging.get_logger(__name__)



@dataclass
class 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			42
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Tensor						,			lowerCAmelCase_	:			Tensor     ) ->							int:
        snake_case_    							=				len(list(m.modules()     )     ) == 1 or isinstance(lowerCAmelCase_						,			nn.Convad     ) or isinstance(lowerCAmelCase_						,			nn.BatchNormad     )
        if has_not_submodules:
              if not isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
                    self.traced.append(lowerCAmelCase_     )

  def __call__(self	:			Tuple						,			lowerCAmelCase_	:			Tensor     ) ->							Optional[Any]:
        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 		_lowerCamelCase						(self	:			Dict     ) ->							Union[str, Any]:
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda lowerCAmelCase_     : len(list(x.state_dict().keys()     )     ) > 0						,			self.traced     )     )



@dataclass
class 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			42
  _UpperCamelCase							=			42
  _UpperCamelCase							=			0
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)
  _UpperCamelCase							=			field(default_factory=UpperCamelCase							)

  def __call__(self	:			List[Any]						,			lowerCAmelCase_	:			Tensor     ) ->							Tuple:
        snake_case_    							=				Tracker(self.dest     )(lowerCAmelCase_     ).parametrized
        snake_case_    							=				Tracker(self.src     )(lowerCAmelCase_     ).parametrized

        snake_case_    							=				list(filter(lambda lowerCAmelCase_     : type(lowerCAmelCase_     ) not in self.src_skip						,			lowerCAmelCase_     )     )
        snake_case_    							=				list(filter(lambda lowerCAmelCase_     : type(lowerCAmelCase_     ) not in self.dest_skip						,			lowerCAmelCase_     )     )

        if len(lowerCAmelCase_     ) != len(lowerCAmelCase_     ):
              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}'''     )

def 	snake_case_							(				_a  :				nn.Module			,       _a  :				nn.Module      ):
      # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
      snake_case_    							=				from_model.state_dict()
      snake_case_    							=				our_model.state_dict()
      snake_case_    							=				our_model.config
      snake_case_    							=				[]
      for stage_idx in range(len(config.hidden_sizes      )      ):
            for block_id in range(config.depths[stage_idx]      ):
                  snake_case_    							=				F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                  snake_case_    							=				F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                  all_keys.append((from_key, to_key)      )
                  snake_case_    							=				F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                  snake_case_    							=				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:
            snake_case_    							=				from_state_dict.pop(_a      )

      our_model.load_state_dict(_a      )
      return our_model
def 	snake_case_							(				_a  :				str			,       _a  :				VanConfig			,       _a  :				str			,       _a  :				nn.Module			,       _a  :				Path			,       _a  :				bool = True			,       ):
      print(F'''Downloading weights for {name}...'''      )
      snake_case_    							=				cached_download(_a      )
      print(F'''Converting {name}...'''      )
      snake_case_    							=				torch.load(_a      )["state_dict"]
      from_model.load_state_dict(_a      )
      from_model.eval()
      with torch.no_grad():
            snake_case_    							=				VanForImageClassification(_a      ).eval()
            snake_case_    							=				ModuleTransfer(src=_a			,       dest=_a      )
            snake_case_    							=				torch.randn((1, 3, 224, 224)      )
            module_transfer(_a      )
            snake_case_    							=				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."      )

      snake_case_    							=				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
            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=_a			,       )

            print(F'''Pushed {checkpoint_name}'''      )
def 	snake_case_							(				_a  :				Path			,       _a  :				str = None			,       _a  :				bool = True      ):
      snake_case_    							=				"imagenet-1k-id2label.json"
      snake_case_    							=				1_000

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

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

      snake_case_    							=				partial(_a			,       num_labels=_a			,       idalabel=_a			,       labelaid=_a      )

      snake_case_    							=				{
          "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]			,       ),
      }

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

      snake_case_    							=				{
          "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__":
     A							     = 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							     = parser.parse_args()
     A							     = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     A							     = 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'''





from .pipeline_kandinskya_a import KandinskyVaaPipeline
from .pipeline_kandinsky2_2_controlnet import KandinskyVaaControlnetPipeline
from .pipeline_kandinsky2_2_controlnet_img2img import KandinskyVaaControlnetImgaImgPipeline
from .pipeline_kandinsky2_2_img2img import KandinskyVaaImgaImgPipeline
from .pipeline_kandinsky2_2_inpainting import KandinskyVaaInpaintPipeline
from .pipeline_kandinsky2_2_prior import KandinskyVaaPriorPipeline
from .pipeline_kandinsky2_2_prior_emb2emb import KandinskyVaaPriorEmbaEmbPipeline

