


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()
lowerCamelCase						 =	logging.get_logger(__name__)




@dataclass
class A  :
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  List[nn.Module]				=field(default_factory=UpperCamelCase_					)
      UpperCamelCase__							:  list				=field(default_factory=UpperCamelCase_					)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Tensor      ,       lowercase_						:					Tensor			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =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						:					Any      ,       lowercase_						:					Tensor			) ->						Tuple:
             """simple docstring"""




             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 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




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




@dataclass
class A  :
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  nn.Module
      UpperCamelCase__							:  int				=0
      UpperCamelCase__							:  List				=field(default_factory=UpperCamelCase_					)
      UpperCamelCase__							:  List				=field(default_factory=UpperCamelCase_					)






      def __call__( self						:					Optional[int]      ,       lowercase_						:					Tensor			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Any          =Tracker(self.dest			)(lowercase_			).parametrized
             _lowerCamelCase	:		int          =Tracker(self.src			)(lowercase_			).parametrized

             _lowerCamelCase	:		Union[str, Any]          =list(filter(lambda lowercase_			: type(lowercase_			) not in self.src_skip      ,       lowercase_			)			)
             _lowerCamelCase	:		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      a_				(   SCREAMING_SNAKE_CASE__ :				nn.Module		,      SCREAMING_SNAKE_CASE__ :				nn.Module					):
       '''simple docstring'''





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

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

       our_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
       return our_model




def      a_				(   SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				VanConfig		,      SCREAMING_SNAKE_CASE__ :				str		,      SCREAMING_SNAKE_CASE__ :				nn.Module		,      SCREAMING_SNAKE_CASE__ :				Path		,      SCREAMING_SNAKE_CASE__ :				bool = True		,      ):
       '''simple docstring'''





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

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

       _lowerCamelCase	:		int          =name
       print(SCREAMING_SNAKE_CASE__					)

       if push_to_hub:
              our_model.push_to_hub(
                  repo_path_or_name=save_directory / checkpoint_name		,      commit_message='Add model'		,      use_temp_dir=SCREAMING_SNAKE_CASE__		,      )

              # we can use the convnext one
              _lowerCamelCase	:		int          =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=SCREAMING_SNAKE_CASE__		,      )

              print(F'''Pushed {checkpoint_name}'''					)




def      a_				(   SCREAMING_SNAKE_CASE__ :				Path		,      SCREAMING_SNAKE_CASE__ :				str = None		,      SCREAMING_SNAKE_CASE__ :				bool = True					):
       '''simple docstring'''





       _lowerCamelCase	:		List[str]          ='imagenet-1k-id2label.json'
       _lowerCamelCase	:		str          =1_000

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

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

       _lowerCamelCase	:		Optional[int]          =partial(SCREAMING_SNAKE_CASE__		,      num_labels=SCREAMING_SNAKE_CASE__		,      idalabel=SCREAMING_SNAKE_CASE__		,      labelaid=SCREAMING_SNAKE_CASE__					)

       _lowerCamelCase	:		Union[str, Any]          ={
           '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]		,      ),
       }

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

       _lowerCamelCase	:		Union[str, 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(
                  SCREAMING_SNAKE_CASE__		,      names_to_config[model_name]		,      checkpoint=names_to_original_checkpoints[model_name]		,      from_model=names_to_original_models[model_name]()		,      save_directory=SCREAMING_SNAKE_CASE__		,      push_to_hub=SCREAMING_SNAKE_CASE__		,      )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(
                         SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      checkpoint=names_to_original_checkpoints[model_name]		,      from_model=names_to_original_models[model_name]()		,      save_directory=SCREAMING_SNAKE_CASE__		,      push_to_hub=SCREAMING_SNAKE_CASE__		,      )


if __name__ == "__main__":
 lowerCamelCase						 =	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.',
 )

 lowerCamelCase						 =	parser.parse_args()
 lowerCamelCase						 =	args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 lowerCamelCase						 =	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 bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def      a_				(   SCREAMING_SNAKE_CASE__ :				Tuple		,      SCREAMING_SNAKE_CASE__ :				Optional[int]					):
       '''simple docstring'''





       _lowerCamelCase	:		Union[str, Any]          =copy.deepcopy(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		Union[str, Any]          =sorted(SCREAMING_SNAKE_CASE__					)
       _lowerCamelCase	:		List[Any]          =[bisect.bisect_right(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					) for y in x]
       return quantized




def      a_				(   SCREAMING_SNAKE_CASE__ :				Any		,      SCREAMING_SNAKE_CASE__ :				Optional[int]=0					):
       '''simple docstring'''





       _lowerCamelCase	:		List[Any]          =np.arange(start=3		,      stop=SCREAMING_SNAKE_CASE__		,      step=4					).tolist() if k > 0 else [10]
       _lowerCamelCase	:		List[Any]          =_quantize(SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__					)
       # count number of elements per group
       _lowerCamelCase	:		Union[str, Any]          =np.unique(SCREAMING_SNAKE_CASE__		,      return_counts=SCREAMING_SNAKE_CASE__					)[1]
       _lowerCamelCase	:		int          =[0] + bins + [np.inf]
       logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE__					)					)
       logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE__					)					)
       return groups




class A  (       UpperCamelCase_					):






      def __init__( self						:					Optional[Any]      ,       lowercase_						:					int      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					int			) ->						Any:
             """simple docstring"""




             if not isinstance(lowercase_      ,       lowercase_			):
                    raise ValueError(
                        'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(lowercase_			)			)
             _lowerCamelCase	:		Any          =sampler
             _lowerCamelCase	:		Dict          =group_ids
             _lowerCamelCase	:		Dict          =batch_size






      def __iter__( self						:					Optional[int]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)
             _lowerCamelCase	:		Union[str, Any]          =defaultdict(lowercase_			)

             _lowerCamelCase	:		Any          =0
             for idx in self.sampler:
                    _lowerCamelCase	:		Any          =self.group_ids[idx]
                    buffer_per_group[group_id].append(lowercase_			)
                    samples_per_group[group_id].append(lowercase_			)
                    if len(buffer_per_group[group_id]			) == self.batch_size:
                           yield buffer_per_group[group_id]  # TODO
                           num_batches += 1
                           del buffer_per_group[group_id]
                    assert len(buffer_per_group[group_id]			) < self.batch_size

             # now we have run out of elements that satisfy
             # the group criteria, let's return the remaining
             # elements so that the size of the sampler is
             # deterministic
             _lowerCamelCase	:		Union[str, Any]          =len(self			)
             _lowerCamelCase	:		Dict          =expected_num_batches - num_batches
             if num_remaining > 0:
                    # for the remaining batches, group the batches by similar lengths
                    _lowerCamelCase	:		Union[str, Any]          =[]
                    for group_id, idxs in sorted(buffer_per_group.items()      ,       key=lambda lowercase_			: x[0]			):
                           batch_idx.extend(lowercase_			)
                           if len(lowercase_			) >= self.batch_size:
                                  yield batch_idx[: self.batch_size]
                                  _lowerCamelCase	:		str          =batch_idx[self.batch_size :]
                                  num_remaining -= 1
                    if len(lowercase_			) > 0:
                           yield batch_idx
                           num_remaining -= 1
             assert num_remaining == 0






      def __len__( self						:					Optional[int]			) ->						Union[str, Any]:
             """simple docstring"""




             return (len(self.sampler			) + self.batch_size - 1) // self.batch_size


