



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




@dataclass
class  snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     42
     _SCREAMING_SNAKE_CASE            =     field(default_factory=lowerCAmelCase_					)
     _SCREAMING_SNAKE_CASE            =     field(default_factory=lowerCAmelCase_					)






     def        lowercase_       (	self  :  Dict,			_snake_case  :  List[Any],			_snake_case  :  Tensor,			_snake_case  :  Tensor )       ->str:
            snake_case__							:     int            =     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  :  Optional[Any],			_snake_case  :  Tensor )       ->Optional[Any]:
            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        lowercase_       (	self  :  Dict )       ->Any:
            # check the len of the state_dict keys to see if we have learnable params
            return list(filter(lambda _snake_case : len(list(x.state_dict().keys() ) ) > 0,			self.traced ) )




@dataclass
class  snake_case__						:






     """simple docstring"""


     _SCREAMING_SNAKE_CASE            =     42
     _SCREAMING_SNAKE_CASE            =     42
     _SCREAMING_SNAKE_CASE            =     0
     _SCREAMING_SNAKE_CASE            =     field(default_factory=lowerCAmelCase_					)
     _SCREAMING_SNAKE_CASE            =     field(default_factory=lowerCAmelCase_					)






     def __call__(	self  :  Any,			_snake_case  :  Tensor )       ->str:
            snake_case__							:     Optional[Any]            =     Tracker(self.dest )(_snake_case ).parametrized
            snake_case__							:     str            =     Tracker(self.src )(_snake_case ).parametrized

            snake_case__							:     Dict            =     list(filter(lambda _snake_case : type(_snake_case ) not in self.src_skip,			_snake_case ) )
            snake_case__							:     List[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 	lowercase_							(A			:			nn.Module		, A			:			nn.Module				):
       # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
       snake_case__							:     Optional[Any]            =     from_model.state_dict()
       snake_case__							:     int            =     our_model.state_dict()
       snake_case__							:     Union[str, Any]            =     our_model.config
       snake_case__							:     Dict            =     []
       for stage_idx in range(len(config.hidden_sizes				)				):
              for block_id in range(config.depths[stage_idx]				):
                     snake_case__							:     Dict            =     F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
                     snake_case__							:     Optional[Any]            =     F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

                     all_keys.append((from_key, to_key)				)
                     snake_case__							:     Dict            =     F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                     snake_case__							:     int            =     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__							:     Union[str, Any]            =     from_state_dict.pop(A				)

       our_model.load_state_dict(A				)
       return our_model
def 	lowercase_							(A			:			str		, A			:			VanConfig		, A			:			str		, A			:			nn.Module		, A			:			Path		, A			:			bool = True		, ):
       print(F'''Downloading weights for {name}...'''				)
       snake_case__							:     Dict            =     cached_download(A				)
       print(F'''Converting {name}...'''				)
       snake_case__							:     Optional[Any]            =     torch.load(A				)['state_dict']
       from_model.load_state_dict(A				)
       from_model.eval()
       with torch.no_grad():
              snake_case__							:     Union[str, Any]            =     VanForImageClassification(A				).eval()
              snake_case__							:     Tuple            =     ModuleTransfer(src=A		, dest=A				)
              snake_case__							:     Optional[Any]            =     torch.randn((1, 3, 2_2_4, 2_2_4)				)
              module_transfer(A				)
              snake_case__							:     int            =     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__							:     int            =     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__							:     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=A		, )

              print(F'''Pushed {checkpoint_name}'''				)
def 	lowercase_							(A			:			Path		, A			:			str = None		, A			:			bool = True				):
       snake_case__							:     Dict            =     'imagenet-1k-id2label.json'
       snake_case__							:     List[str]            =     1_0_0_0

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

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

       snake_case__							:     Union[str, Any]            =     partial(A		, num_labels=A		, idalabel=A		, labelaid=A				)

       snake_case__							:     Union[str, Any]            =     {
           'van-tiny': ImageNetPreTrainedConfig(
               hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]		, depths=[3, 3, 5, 2]		, mlp_ratios=[8, 8, 4, 4]		, ),
           'van-small': ImageNetPreTrainedConfig(
               hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		, depths=[2, 2, 4, 2]		, mlp_ratios=[8, 8, 4, 4]		, ),
           'van-base': ImageNetPreTrainedConfig(
               hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		, depths=[3, 3, 1_2, 3]		, mlp_ratios=[8, 8, 4, 4]		, ),
           'van-large': ImageNetPreTrainedConfig(
               hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]		, depths=[3, 5, 2_7, 3]		, mlp_ratios=[8, 8, 4, 4]		, ),
       }

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

       snake_case__							:     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(
                  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_       :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_       :Dict				  =				parser.parse_args()
 a_       :Path				  =				args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 a_       :Optional[int]				  =				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)











from manim import *




class  snake_case__						(	lowerCAmelCase_					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[Any] )       ->Dict:
            snake_case__							:     str            =     Rectangle(height=0.5,			width=0.5 )
            snake_case__							:     str            =     Rectangle(height=0.4_6,			width=0.4_6 ).set_stroke(width=0 )
            snake_case__							:     List[Any]            =     Rectangle(height=0.2_5,			width=0.2_5 )

            snake_case__							:     Any            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Optional[Any]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[int]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Any            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Optional[Any]            =     Text('CPU',			font_size=2_4 )
            snake_case__							:     str            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            cpu.move_to([-2.5, -0.5, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(4 )]
            snake_case__							:     int            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     str            =     Text('GPU',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            gpu.move_to([-1, -1, 0] )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     [mem.copy() for i in range(6 )]
            snake_case__							:     Union[str, Any]            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )

            snake_case__							:     int            =     Text('Model',			font_size=2_4 )
            snake_case__							:     List[Any]            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            model.move_to([3, -1.0, 0] )
            self.add(_snake_case )

            snake_case__							:     Any            =     []
            snake_case__							:     Optional[int]            =     []

            for i, rect in enumerate(_snake_case ):
                   rect.set_stroke(_snake_case )

                   snake_case__							:     Union[str, Any]            =     Rectangle(height=0.4_6 / 4,			width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(_snake_case,			opacity=0.7 )

                   if i == 0:
                          cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ),			buff=0.0_2,			direction=_snake_case )
                          cpu_target.set_x(cpu_target.get_x() + 0.1 )
                   elif i == 3:
                          cpu_target.next_to(model_cpu_arr[0],			direction=_snake_case,			buff=0.0 )
                   else:
                          cpu_target.next_to(model_cpu_arr[i - 1],			direction=_snake_case,			buff=0.0 )
                   self.add(_snake_case )
                   model_cpu_arr.append(_snake_case )

            self.add(*_snake_case,			*_snake_case )

            snake_case__							:     Optional[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     List[Any]            =     [meta_mem.copy() for i in range(6 )]
            snake_case__							:     Dict            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Tuple            =     VGroup(*_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     int            =     VGroup(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0 )
            snake_case__							:     Union[str, Any]            =     Text('Disk',			font_size=2_4 )
            snake_case__							:     int            =     Group(_snake_case,			_snake_case ).arrange(_snake_case,			buff=0.5,			aligned_edge=_snake_case )
            disk.move_to([-4.0, -1.2_5, 0] )
            self.add(_snake_case,			_snake_case )

            snake_case__							:     str            =     []

            for i in range(6 ):
                   snake_case__							:     str            =     fill.copy().set_fill(_snake_case,			opacity=0.8 )
                   target.move_to(disk_left_col_base[i] ).scale(0.5 )
                   cpu_disk_arr.append(_snake_case )

            self.add(*_snake_case )

            snake_case__							:     int            =     Square(side_length=2.2 )
            key.move_to([-5, 2, 0] )

            snake_case__							:     int            =     MarkupText(
                F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',			font_size=1_8,			)

            key_text.move_to([-5, 2.4, 0] )

            self.add(_snake_case,			_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(
                F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''',			font_size=1_8,			)

            blue_text.next_to(_snake_case,			DOWN * 2.4,			aligned_edge=key_text.get_left() )
            self.add(_snake_case )

            snake_case__							:     Optional[int]            =     MarkupText(F'''The offloaded weights are all sent to the CPU.''',			font_size=2_4 )
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            for i in range(6 ):
                   snake_case__							:     int            =     cpu_disk_arr[i]
                   snake_case__							:     Optional[Any]            =     rect.copy().set_fill(_snake_case,			opacity=0.8 ).scale(2.0 )
                   cpa.generate_target()
                   cpa.target.move_to(model_base[i] )

                   if i == 0:
                          rect.set_fill(_snake_case,			opacity=0.8 )
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[0] ).scale(2.0 )

                          self.remove(
                              *_snake_case,			*_snake_case,			)

                   else:
                          rect.generate_target()
                          rect.target.move_to(cpu_left_col_base[i] ).scale(2.0 )
                   self.play(MoveToTarget(_snake_case ),			MoveToTarget(_snake_case ),			model_base[i].animate.set_stroke(_snake_case ) )
            self.play(FadeOut(_snake_case ) )

            snake_case__							:     Optional[Any]            =     MarkupText(
                F'''Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.''',			font_size=2_4,			)
            step_a.move_to([2, 2, 0] )

            self.play(Write(_snake_case,			run_time=3 ) )

            snake_case__							:     str            =     []
            snake_case__							:     Optional[Any]            =     []
            for i in range(6 ):
                   snake_case__							:     List[Any]            =     Arrow(start=_snake_case,			end=_snake_case,			color=_snake_case,			buff=0.5 )
                   a.next_to(model_base[i].get_left(),			_snake_case,			buff=0.2 )
                   arrows.append(_snake_case )
                   animations.append(Write(_snake_case ) )
            self.play(*_snake_case )
            self.wait()






