'''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()
snake_case__	:     int      =    logging.get_logger(__name__)






@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






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


  def _UpperCamelCase	(					self ,						snake_case_ ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[Any]  = len(list(m.modules()		)		) == 1 or isinstance(snake_case_ ,						nn.Convad		) or isinstance(snake_case_ ,						nn.BatchNormad		)
       if has_not_submodules:
            if not isinstance(snake_case_ ,						snake_case_		):
                 self.traced.append(snake_case_		)


  def __call__(					self ,						snake_case_		):




       '''simple docstring'''

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






  @property
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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







@dataclass
class 	__SCREAMING_SNAKE_CASE		:







  '''simple docstring'''






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


  def __call__(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = Tracker(self.dest		)(snake_case_		).parametrized
       UpperCAmelCase_				:  Optional[int]  = Tracker(self.src		)(snake_case_		).parametrized

       UpperCAmelCase_				:  Optional[Any]  = list(filter(lambda snake_case_		: type(snake_case_		) not in self.src_skip ,						snake_case_		)		)
       UpperCAmelCase_				:  Union[str, Any]  = list(filter(lambda snake_case_		: type(snake_case_		) not in self.dest_skip ,						snake_case_		)		)

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

       for dest_m, src_m in zip(snake_case_ ,						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_   :				nn.Module      ,	lowerCamelCase_   :				nn.Module  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = from_model.state_dict()
     UpperCAmelCase_				:  Optional[Any]  = our_model.state_dict()
     UpperCAmelCase_				:  Tuple  = our_model.config
     UpperCAmelCase_				:  Dict  = []
     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_				:  Union[str, Any]  = F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

               all_keys.append((from_key, to_key)  )
               UpperCAmelCase_				:  str  = F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
               UpperCAmelCase_				:  Any  = 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_				:  Optional[int]  = from_state_dict.pop(lowerCamelCase_  )

     our_model.load_state_dict(lowerCamelCase_  )
     return our_model


def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				VanConfig      ,	lowerCamelCase_   :				str      ,	lowerCamelCase_   :				nn.Module      ,	lowerCamelCase_   :				Path      ,	lowerCamelCase_   :				bool = True      ,	):



     """simple docstring"""





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

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

     UpperCAmelCase_				:  int  = name
     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
          UpperCAmelCase_				:  Dict  = 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_   :				Path      ,	lowerCamelCase_   :				str = None      ,	lowerCamelCase_   :				bool = True  ):



     """simple docstring"""





     UpperCAmelCase_				:  Optional[Any]  = 'imagenet-1k-id2label.json'
     UpperCAmelCase_				:  List[str]  = 1000

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

     UpperCAmelCase_				:  Tuple  = idalabel
     UpperCAmelCase_				:  List[Any]  = {v: k for k, v in idalabel.items()}

     UpperCAmelCase_				:  Optional[Any]  = partial(lowerCamelCase_      ,	num_labels=lowerCamelCase_      ,	idalabel=lowerCamelCase_      ,	labelaid=lowerCamelCase_  )

     UpperCAmelCase_				:  List[str]  = {
         '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_				:  Union[str, Any]  = {
         'van-tiny': van_tiny,
         'van-small': van_small,
         'van-base': van_base,
         'van-large': van_large,
     }

     UpperCAmelCase_				:  Dict  = {
         '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(
              lowerCamelCase_      ,	names_to_config[model_name]      ,	checkpoint=names_to_original_checkpoints[model_name]      ,	from_model=names_to_original_models[model_name]()      ,	save_directory=lowerCamelCase_      ,	push_to_hub=lowerCamelCase_      ,	)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   lowerCamelCase_      ,	lowerCamelCase_      ,	checkpoint=names_to_original_checkpoints[model_name]      ,	from_model=names_to_original_models[model_name]()      ,	save_directory=lowerCamelCase_      ,	push_to_hub=lowerCamelCase_      ,	)


if __name__ == "__main__":
 snake_case__	:     str      =    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.''',
 )

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













import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							UNetaDModel(
										    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("DownBlock2D", "AttnDownBlock2D")						,	up_block_types=("AttnUpBlock2D", "UpBlock2D")						,	)
										return model





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]						)

										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1E-2






					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							1E-2 if torch_device != "mps" else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance

@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

										lowerCamelCase__      =							image[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

