


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       :					Optional[int]        =  logging.get_logger(__name__)







@dataclass
class 	_snake_case						:
 SCREAMING_SNAKE_CASE__   = 42
 SCREAMING_SNAKE_CASE__   = field(default_factory=_snake_case							)
 SCREAMING_SNAKE_CASE__   = field(default_factory=_snake_case							)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       a		:Union[str, Any]											= 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      ,						_lowerCamelCase					):
       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  SCREAMING_SNAKE_CASE__       (	self					):
       # 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 	_snake_case						:
 SCREAMING_SNAKE_CASE__   = 42
 SCREAMING_SNAKE_CASE__   = 42
 SCREAMING_SNAKE_CASE__   = 0
 SCREAMING_SNAKE_CASE__   = field(default_factory=_snake_case							)
 SCREAMING_SNAKE_CASE__   = field(default_factory=_snake_case							)




 def __call__(	self      ,						_lowerCamelCase					):
       a		:List[str]											= Tracker(self.dest					)(_lowerCamelCase					).parametrized
       a		:List[Any]											= Tracker(self.src					)(_lowerCamelCase					).parametrized

       a		:List[Any]											= list(filter(lambda _lowerCamelCase					: type(_lowerCamelCase					) not in self.src_skip      ,						_lowerCamelCase					)					)
       a		:Dict											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	nn.Module				,						UpperCAmelCase_		:	nn.Module		):




      """simple docstring"""





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

                  all_keys.append((from_key, to_key)		)
                  a		:int											= F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                  a		:List[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:
            a		:str											= from_state_dict.pop(UpperCAmelCase_		)

      our_model.load_state_dict(UpperCAmelCase_		)
      return our_model
def        __lowerCamelCase		(				UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	VanConfig				,						UpperCAmelCase_		:	str				,						UpperCAmelCase_		:	nn.Module				,						UpperCAmelCase_		:	Path				,						UpperCAmelCase_		:	bool = True				,						):




      """simple docstring"""





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

      a		:List[str]											= 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
            a		:List[Any]											= 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        __lowerCamelCase		(				UpperCAmelCase_		:	Path				,						UpperCAmelCase_		:	str = None				,						UpperCAmelCase_		:	bool = True		):




      """simple docstring"""





      a		:Any											= '''imagenet-1k-id2label.json'''
      a		:Optional[Any]											= 1000

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

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

      a		:Optional[int]											= partial(UpperCAmelCase_				,						num_labels=UpperCAmelCase_				,						idalabel=UpperCAmelCase_				,						labelaid=UpperCAmelCase_		)

      a		:Optional[int]											= {
          '''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]				,						),
      }

      a		:str											= {
          '''van-tiny''': van_tiny,
          '''van-small''': van_small,
          '''van-base''': van_base,
          '''van-large''': van_large,
      }

      a		:Optional[int]											= {
          '''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__":
     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       :					Any        =  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 tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





