

'''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()
lowercase__            =     logging.get_logger(__name__)




@dataclass
class 			A_					:






     '''simple docstring'''



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


     def   UpperCAmelCase_			(						self					:	Tuple	,     lowercase_					:	int	,     lowercase_					:	Tensor	,     lowercase_					:	Tensor			)		->      List[str]:
      UpperCAmelCase			:      Dict		   =					len(list(m.modules()			)			) == 1 or isinstance(lowercase_	,     nn.Convad			) or isinstance(lowercase_	,     nn.BatchNormad			)
      if has_not_submodules:
       if not isinstance(lowercase_	,     lowercase_			):
        self.traced.append(lowercase_			)


     def __call__(						self					:	Optional[Any]	,     lowercase_					:	Tensor			)		->      Optional[Any]:
      for m in self.module.modules():
       self.handles.append(m.register_forward_hook(self._forward_hook			)			)
      self.module(lowercase_			)
      [x.remove() for x in self.handles]
      return self




     @property
     def   UpperCAmelCase_			(						self					:	List[str]			)		->      int:
      # check the len of the state_dict keys to see if we have learnable params
      return list(filter(lambda lowercase_			: len(list(x.state_dict().keys()			)			) > 0	,     self.traced			)			)




@dataclass
class 			A_					:






     '''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					:	List[str]	,     lowercase_					:	Tensor			)		->      Optional[Any]:
      UpperCAmelCase			:      str		   =					Tracker(self.dest			)(lowercase_			).parametrized
      UpperCAmelCase			:      Any		   =					Tracker(self.src			)(lowercase_			).parametrized

      UpperCAmelCase			:      Optional[int]		   =					list(filter(lambda lowercase_			: type(lowercase_			) not in self.src_skip	,     lowercase_			)			)
      UpperCAmelCase			:      Any		   =					list(filter(lambda lowercase_			: type(lowercase_			) not in self.dest_skip	,     lowercase_			)			)

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

      for dest_m, src_m in zip(lowercase_	,     lowercase_			):
       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_    ,			UpperCAmelCase_      ):
 # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
 UpperCAmelCase			:      Optional[Any]		   =					from_model.state_dict()
 UpperCAmelCase			:      Any		   =					our_model.state_dict()
 UpperCAmelCase			:      int		   =					our_model.config
 UpperCAmelCase			:      List[str]		   =					[]
 for stage_idx in range(len(config.hidden_sizes      )      ):
  for block_id in range(config.depths[stage_idx]      ):
   UpperCAmelCase			:      List[Any]		   =					F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
   UpperCAmelCase			:      Any		   =					F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

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

 our_model.load_state_dict(UpperCAmelCase_      )
 return our_model




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_ = True    ,			):
 print(F"""Downloading weights for {name}..."""      )
 UpperCAmelCase			:      str		   =					cached_download(UpperCAmelCase_      )
 print(F"""Converting {name}..."""      )
 UpperCAmelCase			:      int		   =					torch.load(UpperCAmelCase_      )['state_dict']
 from_model.load_state_dict(UpperCAmelCase_      )
 from_model.eval()
 with torch.no_grad():
  UpperCAmelCase			:      Dict		   =					VanForImageClassification(UpperCAmelCase_      ).eval()
  UpperCAmelCase			:      Union[str, Any]		   =					ModuleTransfer(src=UpperCAmelCase_    ,			dest=UpperCAmelCase_      )
  UpperCAmelCase			:      str		   =					torch.randn((1, 3, 2_24, 2_24)      )
  module_transfer(UpperCAmelCase_      )
  UpperCAmelCase			:      Dict		   =					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			:      Dict		   =					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			:      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  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_ = None    ,			UpperCAmelCase_ = True      ):
 UpperCAmelCase			:      str		   =					'imagenet-1k-id2label.json'
 UpperCAmelCase			:      str		   =					10_00

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

 UpperCAmelCase			:      Optional[Any]		   =					idalabel
 UpperCAmelCase			:      int		   =					{v: k for k, v in idalabel.items()}

 UpperCAmelCase			:      List[str]		   =					partial(UpperCAmelCase_    ,			num_labels=UpperCAmelCase_    ,			idalabel=UpperCAmelCase_    ,			labelaid=UpperCAmelCase_      )

 UpperCAmelCase			:      Any		   =					{
     'van-tiny': ImageNetPreTrainedConfig(
         hidden_sizes=[32, 64, 1_60, 2_56]    ,			depths=[3, 3, 5, 2]    ,			mlp_ratios=[8, 8, 4, 4]    ,			),
     'van-small': ImageNetPreTrainedConfig(
         hidden_sizes=[64, 1_28, 3_20, 5_12]    ,			depths=[2, 2, 4, 2]    ,			mlp_ratios=[8, 8, 4, 4]    ,			),
     'van-base': ImageNetPreTrainedConfig(
         hidden_sizes=[64, 1_28, 3_20, 5_12]    ,			depths=[3, 3, 12, 3]    ,			mlp_ratios=[8, 8, 4, 4]    ,			),
     'van-large': ImageNetPreTrainedConfig(
         hidden_sizes=[64, 1_28, 3_20, 5_12]    ,			depths=[3, 5, 27, 3]    ,			mlp_ratios=[8, 8, 4, 4]    ,			),
 }

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

 UpperCAmelCase			:      List[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__":
 lowercase__            =     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.",
 )

 lowercase__            =     parser.parse_args()
 lowercase__            =     args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowercase__            =     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 __future__ import annotations




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert n > 1 and a > 0 and greatest_common_divisor(UpperCAmelCase_    ,			UpperCAmelCase_      ) == 1
 ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Dict		   =					extended_gcd(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 UpperCAmelCase			:      int		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      int		   =					extended_euclid(UpperCAmelCase_    ,			UpperCAmelCase_      )  # Implemented below
 if b < 0:
  UpperCAmelCase			:      Optional[int]		   =					(b % n + n) % n
 return b




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_    ,			UpperCAmelCase_      ):
 UpperCAmelCase			:      Tuple		   =					invert_modulo(UpperCAmelCase_    ,			UpperCAmelCase_      )
 UpperCAmelCase			:      List[Any]		   =					(b * s) % n
 return x




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase   ,					UpperCAmelCase   ,					UpperCAmelCase			:      Dict		   =					a, 1, 0
 else:
  ((UpperCAmelCase)   ,					(UpperCAmelCase)   ,					(UpperCAmelCase))			:      Optional[int]		   =					extended_gcd(UpperCAmelCase_    ,			a % b      )
  UpperCAmelCase			:      Dict		   =					q
  UpperCAmelCase			:      int		   =					p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if b == 0:
  return (1, 0)
 ((UpperCAmelCase)   ,					(UpperCAmelCase))			:      Tuple		   =					extended_euclid(UpperCAmelCase_    ,			a % b      )
 UpperCAmelCase			:      Tuple		   =					a // b
 return (y, x - k * y)




def  UpperCamelCase(	UpperCAmelCase_    ,			UpperCAmelCase_      ):
 if a < b:
  UpperCAmelCase   ,					UpperCAmelCase			:      Union[str, Any]		   =					b, a

 while a % b != 0:
  UpperCAmelCase   ,					UpperCAmelCase			:      int		   =					b, a % b

 return b


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

 testmod(name="modular_division", verbose=True)
 testmod(name="modular_division2", verbose=True)
 testmod(name="invert_modulo", verbose=True)
 testmod(name="extended_gcd", verbose=True)
 testmod(name="extended_euclid", verbose=True)
 testmod(name="greatest_common_divisor", verbose=True)




