code: 
  
  
  '''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()
  lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
  @dataclass
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  nn.Module
        lowerCAmelCase__      :  List[nn.Module]						      =							field(default_factory=lowercase_  )
        lowerCAmelCase__      :  list						      =							field(default_factory=lowercase_  )
  
  
  
        def    UpperCamelCase__	(self					:					Optional[int]     ,		UpperCamelCase					:					Optional[Any]     ,		UpperCamelCase					:					Tensor     ,		UpperCamelCase					:					Tensor				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				len(list(m.modules()				)				) == 1 or isinstance(UpperCamelCase     ,		nn.Convad				) or isinstance(UpperCamelCase     ,		nn.BatchNormad				)
               if has_not_submodules:
                      if not isinstance(UpperCamelCase     ,		UpperCamelCase				):
                             self.traced.append(UpperCamelCase				)
  
  
  
        def __call__(self					:					Optional[int]     ,		UpperCamelCase					:					Tensor				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               for m in self.module.modules():
                      self.handles.append(m.register_forward_hook(self._forward_hook				)				)
               self.module(UpperCamelCase				)
               [x.remove() for x in self.handles]
               return self
  
  
  
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Dict				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return list(filter(lambda UpperCamelCase				: len(list(x.state_dict().keys()				)				) > 0     ,		self.traced				)				)
  
  
  
  
  
  
  @dataclass
  class 						__lowerCAmelCase		:
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  nn.Module
        lowerCAmelCase__      :  nn.Module
        lowerCAmelCase__      :  int						      =							0
        lowerCAmelCase__      :  List						      =							field(default_factory=lowercase_  )
        lowerCAmelCase__      :  List						      =							field(default_factory=lowercase_  )
  
  
  
        def __call__(self					:					int     ,		UpperCamelCase					:					Tensor				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				Tracker(self.dest				)(UpperCamelCase				).parametrized
               lowercase__       =				Tracker(self.src				)(UpperCamelCase				).parametrized
  
               lowercase__       =				list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.src_skip     ,		UpperCamelCase				)				)
               lowercase__       =				list(filter(lambda UpperCamelCase				: type(UpperCamelCase				) not in self.dest_skip     ,		UpperCamelCase				)				)
  
               if len(UpperCamelCase				) != len(UpperCamelCase				):
                      raise Exception(
                          f"Numbers of operations are different. Source module has {len(UpperCamelCase				)} operations while"
                          f" destination module has {len(UpperCamelCase				)}."				)
  
               for dest_m, src_m in zip(UpperCamelCase     ,		UpperCamelCase				):
                      dest_m.load_state_dict(src_m.state_dict()				)
                      if self.verbose == 1:
                             print(f"Transfered from={src_m} to={dest_m}"				)
  
  
  
  
  
  
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> nn.Module:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				from_model.state_dict()
         lowercase__       =				our_model.state_dict()
         lowercase__       =				our_model.config
         lowercase__       =				[]
         for stage_idx in range(len(config.hidden_sizes ) ):
                for block_id in range(config.depths[stage_idx] ):
                       lowercase__       =				f"block{stage_idx + 1}.{block_id}.layer_scale_1"
                       lowercase__       =				f"van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"
  
                       all_keys.append((from_key, to_key) )
                       lowercase__       =				f"block{stage_idx + 1}.{block_id}.layer_scale_2"
                       lowercase__       =				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:
                lowercase__       =				from_state_dict.pop(A )
  
         our_model.load_state_dict(A )
         return our_model
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A						,				A						,				A						,				A = True						,				) -> Union[str, Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         print(f"Downloading weights for {name}..." )
         lowercase__       =				cached_download(A )
         print(f"Converting {name}..." )
         lowercase__       =				torch.load(A )['''state_dict''']
         from_model.load_state_dict(A )
         from_model.eval()
         with torch.no_grad():
                lowercase__       =				VanForImageClassification(A ).eval()
                lowercase__       =				ModuleTransfer(src=A						,				dest=A )
                lowercase__       =				torch.randn((1, 3, 224, 224) )
                module_transfer(A )
                lowercase__       =				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.''' )
  
         lowercase__       =				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
                lowercase__       =				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 _SCREAMING_SNAKE_CASE   (A						,				A = None						,				A = True ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				'''imagenet-1k-id2label.json'''
         lowercase__       =				1_000
  
         lowercase__       =				'''huggingface/label-files'''
         lowercase__       =				num_labels
         lowercase__       =				json.load(open(hf_hub_download(A						,				A						,				repo_type='''dataset''' )						,				'''r''' ) )
         lowercase__       =				{int(A ): v for k, v in idalabel.items()}
  
         lowercase__       =				idalabel
         lowercase__       =				{v: k for k, v in idalabel.items()}
  
         lowercase__       =				partial(A						,				num_labels=A						,				idalabel=A						,				labelaid=A )
  
         lowercase__       =				{
             '''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]						,				),
         }
  
         lowercase__       =				{
             '''van-tiny''': van_tiny,
             '''van-small''': van_small,
             '''van-base''': van_base,
             '''van-large''': van_large,
         }
  
         lowercase__       =				{
             '''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__":
   lowerCamelCase							:     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.',
   )
  
   lowerCamelCase							:     Union[str, Any]								=			parser.parse_args()
   lowerCamelCase							:     Path								=			args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   lowerCamelCase							:     List[str]								=			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)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import os
  from shutil import copyfile
  from typing import TYPE_CHECKING, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  from ...utils.versions import require_version
  
  
  if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation
  
  require_version('tokenizers>=0.13.3')
  
  if is_sentencepiece_available():
   from .tokenization_llama import LlamaTokenizer
  else:
   lowerCamelCase							:     Optional[int]								=			None
  
  lowerCamelCase							:     List[Any]								=			logging.get_logger(__name__)
  lowerCamelCase							:     Dict								=			{'vocab_file': 'tokenizer.model', 'tokenizer_file': 'tokenizer.json'}
  
  lowerCamelCase		,						lowerCamelCase							:     str								=			'[INST]', '[/INST]'
  lowerCamelCase		,						lowerCamelCase							:     Optional[Any]								=			'<<SYS>>\n', '\n<</SYS>>\n\n'
  
  # fmt: off
  lowerCamelCase							:     Optional[int]								=			'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  List[str]						      =							LlamaTokenizer
        lowerCAmelCase__      :  Tuple						      =							"""left"""
        lowerCAmelCase__      :  int						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					Optional[Any]     ,		UpperCamelCase					:					Tuple=None     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					List[str]=False     ,		UpperCamelCase					:					Dict="<unk>"     ,		UpperCamelCase					:					int="<s>"     ,		UpperCamelCase					:					Dict="</s>"     ,		UpperCamelCase					:					Any=True     ,		UpperCamelCase					:					Tuple=False     ,		**UpperCamelCase					:					List[str]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               super().__init__(
                   vocab_file=UpperCamelCase     ,		tokenizer_file=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
               lowercase__       =				add_eos_token
               self.update_post_processor()
  
               lowercase__       =				vocab_file
               lowercase__       =				False if not self.vocab_file else True
  
  
  
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				self.bos_token
               lowercase__       =				self.bos_token_id
  
               lowercase__       =				self.eos_token
               lowercase__       =				self.eos_token_id
  
               lowercase__       =				f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
               lowercase__       =				f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"
  
               lowercase__       =				[]
               if self.add_bos_token:
                      special_tokens.append((bos, bos_token_id)				)
               if self.add_eos_token:
                      special_tokens.append((eos, eos_token_id)				)
               lowercase__       =				processors.TemplateProcessing(
                   single=UpperCamelCase     ,		pair=UpperCamelCase     ,		special_tokens=UpperCamelCase				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_eos_token
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Tuple				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self._add_bos_token
  
  
  
        @add_eos_token.setter
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					int				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        @add_bos_token.setter
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				value
               self.update_post_processor()
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if not self.can_save_slow_tokenizer:
                      raise ValueError(
                          '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                          '''tokenizer.'''				)
  
               if not os.path.isdir(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
  
               if os.path.abspath(self.vocab_file				) != os.path.abspath(UpperCamelCase				):
                      copyfile(self.vocab_file     ,		UpperCamelCase				)
  
               return (out_vocab_file,)
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					"Conversation"				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				list(conversation.iter_texts()				)
               if not all([is_user for is_user, msg in dialogue[::2]]				) or not all(
                   [not is_user for is_user, msg in dialogue[1::2]]				):
                      raise ValueError(
                          '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''				)
  
               lowercase__       =				[]
               if len(conversation.past_user_inputs				) > 0:
                      if not conversation.past_user_inputs[0].startswith(UpperCamelCase				) or E_SYS not in conversation.past_user_inputs[0]:
                             lowercase__       =				(
                                 B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                             )
               elif not dialogue[0][1].startswith(UpperCamelCase				) or E_SYS not in dialogue[0][1]:
                      lowercase__       =				(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])
  
               dialog_tokens += sum(
                   [
                       [self.bos_token_id]
                       + self.encode(
                           f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "     ,		add_special_tokens=UpperCamelCase				)
                       + [self.eos_token_id]
                       for prompt, answer in zip(dialogue[::2]     ,		dialogue[1::2]				)
                   ]     ,		[]     ,		)
               if not (dialogue[-1][0]):
                      raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"				)
               dialog_tokens += [self.bos_token_id] + self.encode(
                   f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"     ,		add_special_tokens=UpperCamelCase				)
               return dialog_tokens
  
  
  
  
  