




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        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					nn.Module
     _SCREAMING_SNAKE_CASE      :					List[nn.Module]     					=    field(default_factory=UpperCAmelCase__				)
     _SCREAMING_SNAKE_CASE      :					list     					=    field(default_factory=UpperCAmelCase__				)
     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase	):
            a			   =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        lowerCAmelCase__  (				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        UpperCAmelCase__				:



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					nn.Module
     _SCREAMING_SNAKE_CASE      :					nn.Module
     _SCREAMING_SNAKE_CASE      :					int     					=    0
     _SCREAMING_SNAKE_CASE      :					List     					=    field(default_factory=UpperCAmelCase__				)
     _SCREAMING_SNAKE_CASE      :					List     					=    field(default_factory=UpperCAmelCase__				)
     def __call__(				self ,    _lowerCAmelCase	):
            a			   =Tracker(self.dest	)(_lowerCAmelCase	).parametrized
            a			   =Tracker(self.src	)(_lowerCAmelCase	).parametrized

            a			   =list(filter(lambda _lowerCAmelCase	: type(_lowerCAmelCase	) not in self.src_skip ,    _lowerCAmelCase	)	)
            a			   =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      )->			nn.Module:

       """simple docstring"""




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

                     all_keys.append((from_key, to_key)      )
                     a			   =F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
                     a			   =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			   =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   ,       )->			Any:

       """simple docstring"""




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

       """simple docstring"""




       a			   ="""imagenet-1k-id2label.json"""
       a			   =1000

       a			   ="""huggingface/label-files"""
       a			   =num_labels
       a			   =json.load(open(hf_hub_download(UpperCAmelCase_   ,       UpperCAmelCase_   ,       repo_type="""dataset"""      )   ,       """r"""      )      )
       a			   ={int(UpperCAmelCase_      ): v for k, v in idalabel.items()}

       a			   =idalabel
       a			   ={v: k for k, v in idalabel.items()}

       a			   =partial(UpperCAmelCase_   ,       num_labels=UpperCAmelCase_   ,       idalabel=UpperCAmelCase_   ,       labelaid=UpperCAmelCase_      )

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

       a			   ={
           """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__":
    _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)






from __future__ import annotations





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			str:

       """simple docstring"""




       a   ,      a   ,      a			   ="""""", """""", """"""
       a			   =[]

       for character in message_part:
              tmp.append(character_to_number[character]      )

       for each in tmp:
              one += each[0]
              two += each[1]
              three += each[2]

       return one + two + three





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      dict[str, str]      )->			tuple[str, str, str]:

       """simple docstring"""




       a   ,      a			   ="""""", """"""
       a			   =[]

       for character in message_part:
              this_part += character_to_number[character]

       for digit in this_part:
              tmp += digit
              if len(UpperCAmelCase_      ) == len(UpperCAmelCase_      ):
                     result.append(UpperCAmelCase_      )
                     a			   =""""""

       return result[0], result[1], result[2]





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str      )->			tuple[str, str, dict[str, str], dict[str, str]]:

       """simple docstring"""




       a			   =alphabet.replace(""" """   ,       """"""      ).upper()
       a			   =message.replace(""" """   ,       """"""      ).upper()

       # Check length and characters
       if len(UpperCAmelCase_      ) != 27:
              raise KeyError("""Length of alphabet has to be 27."""      )
       for each in message:
              if each not in alphabet:
                     raise ValueError("""Each message character has to be included in alphabet!"""      )

    # Generate dictionares
       a			   =(
           """111""",
           """112""",
           """113""",
           """121""",
           """122""",
           """123""",
           """131""",
           """132""",
           """133""",
           """211""",
           """212""",
           """213""",
           """221""",
           """222""",
           """223""",
           """231""",
           """232""",
           """233""",
           """311""",
           """312""",
           """313""",
           """321""",
           """322""",
           """323""",
           """331""",
           """332""",
           """333""",
       )
       a			   ={}
       a			   ={}
       for letter, number in zip(UpperCAmelCase_   ,       UpperCAmelCase_      ):
              a			   =number
              a			   =letter

       return message, alphabet, character_to_number, number_to_character





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a   ,      a			   ="""""", """"""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              encrypted_numeric += __encrypt_part(
                  message[i : i + period]   ,       UpperCAmelCase_      )

       for i in range(0   ,       len(UpperCAmelCase_      )   ,       3      ):
              encrypted += number_to_character[encrypted_numeric[i : i + 3]]

       return encrypted





def 							lowerCamelCase    (							UpperCAmelCase_	:      str   ,       UpperCAmelCase_	:      str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."   ,       UpperCAmelCase_	:      int = 5      )->			str:

       """simple docstring"""




       a   ,      a   ,      a   ,      a			   =__prepare(
           UpperCAmelCase_   ,       UpperCAmelCase_      )
       a			   =[]
       a			   =""""""

       for i in range(0   ,       len(UpperCAmelCase_      ) + 1   ,       UpperCAmelCase_      ):
              a   ,      a   ,      a			   =__decrypt_part(message[i : i + period]   ,       UpperCAmelCase_      )

              for j in range(0   ,       len(UpperCAmelCase_      )      ):
                     decrypted_numeric.append(a[j] + b[j] + c[j]      )

       for each in decrypted_numeric:
              decrypted += number_to_character[each]

       return decrypted


if __name__ == "__main__":
    _lowerCamelCase							=      '''DEFEND THE EAST WALL OF THE CASTLE.'''
    _lowerCamelCase							=      encrypt_message(msg, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    _lowerCamelCase							=      decrypt_message(encrypted, '''EPSDUCVWYM.ZLKXNBTFGORIJHAQ''')
    print(f"""Encrypted: {encrypted}\nDecrypted: {decrypted}""")
