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							:		str										=						logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = 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,						__UpperCamelCase				):
  							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        __SCREAMING_SNAKE_CASE			(					self				):
  							# check the len of the state_dict keys to see if we have learnable params
  							return list(filter(lambda __UpperCamelCase				: len(list(x.state_dict().keys()				)				) > 0,						self.traced				)				)
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  					lowercase__					=				42
  					lowercase__					=				0
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  					lowercase__					=				field(default_factory=UpperCAmelCase_							)
  
  
  
  
  
  
  					def __call__(					self,						__UpperCamelCase				):
  							_snake_case			 = Tracker(self.dest				)(__UpperCamelCase				).parametrized
  							_snake_case			 = Tracker(self.src				)(__UpperCamelCase				).parametrized
  
  							_snake_case			 = list(filter(lambda __UpperCamelCase				: type(__UpperCamelCase				) not in self.src_skip,						__UpperCamelCase				)				)
  							_snake_case			 = 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 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = from_model.state_dict()
  		_snake_case			 = our_model.state_dict()
  		_snake_case			 = our_model.config
  		_snake_case			 = []
  		for stage_idx in range(len(config.hidden_sizes						)						):
  				for block_id in range(config.depths[stage_idx]						):
  						_snake_case			 = f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
  						_snake_case			 = f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''
  
  						all_keys.append((from_key, to_key)						)
  						_snake_case			 = f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
  						_snake_case			 = 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			 = from_state_dict.pop(lowercase_						)
  
  		our_model.load_state_dict(lowercase_						)
  		return our_model
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_		,							lowercase_ = True		,							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		print(f'''Downloading weights for {name}...'''						)
  		_snake_case			 = cached_download(lowercase_						)
  		print(f'''Converting {name}...'''						)
  		_snake_case			 = torch.load(lowercase_						)["state_dict"]
  		from_model.load_state_dict(lowercase_						)
  		from_model.eval()
  		with torch.no_grad():
  				_snake_case			 = VanForImageClassification(lowercase_						).eval()
  				_snake_case			 = ModuleTransfer(src=lowercase_		,							dest=lowercase_						)
  				_snake_case			 = torch.randn((1, 3, 224, 224)						)
  				module_transfer(lowercase_						)
  				_snake_case			 = copy_parameters(lowercase_		,							lowercase_						)
  
  		if not torch.allclose(from_model(lowercase_						)		,							our_model(lowercase_						).logits						):
  				raise ValueError("The model logits don't match the original one."						)
  
  		_snake_case			 = name
  		print(lowercase_						)
  
  		if push_to_hub:
  				our_model.push_to_hub(
  				    repo_path_or_name=save_directory / checkpoint_name		,							commit_message="Add model"		,							use_temp_dir=lowercase_		,							)
  
  				# we can use the convnext one
  				_snake_case			 = 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=lowercase_		,							)
  
  				print(f'''Pushed {checkpoint_name}'''						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_ = None		,							lowercase_ = True						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = "imagenet-1k-id2label.json"
  		_snake_case			 = 1000
  
  		_snake_case			 = "huggingface/label-files"
  		_snake_case			 = num_labels
  		_snake_case			 = json.load(open(hf_hub_download(lowercase_		,							lowercase_		,							repo_type="dataset"						)		,							"r"						)						)
  		_snake_case			 = {int(lowercase_						): v for k, v in idalabel.items()}
  
  		_snake_case			 = idalabel
  		_snake_case			 = {v: k for k, v in idalabel.items()}
  
  		_snake_case			 = partial(lowercase_		,							num_labels=lowercase_		,							idalabel=lowercase_		,							labelaid=lowercase_						)
  
  		_snake_case			 = {
  		    "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]		,							),
  		}
  
  		_snake_case			 = {
  		    "van-tiny": van_tiny,
  		    "van-small": van_small,
  		    "van-base": van_base,
  		    "van-large": van_large,
  		}
  
  		_snake_case			 = {
  		    "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(
  				    lowercase_		,							names_to_config[model_name]		,							checkpoint=names_to_original_checkpoints[model_name]		,							from_model=names_to_original_models[model_name]()		,							save_directory=lowercase_		,							push_to_hub=lowercase_		,							)
  		else:
  				for model_name, config in names_to_config.items():
  						convert_weight_and_push(
  						    lowercase_		,							lowercase_		,							checkpoint=names_to_original_checkpoints[model_name]		,							from_model=names_to_original_models[model_name]()		,							save_directory=lowercase_		,							push_to_hub=lowercase_		,							)
  
  
  if __name__ == "__main__":
  						_lowerCamelCase							:		List[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							:		str										=						parser.parse_args()
  						_lowerCamelCase							:		Path										=						args.pytorch_dump_folder_path
  						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  						_lowerCamelCase							:		Any										=						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 unittest
  import warnings
  from dataclasses import dataclass
  
  from transformers.convert_slow_tokenizer import SpmConverter
  from transformers.testing_utils import get_tests_dir
  
  
  
  
  
  @dataclass
  class   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				42
  
  
  
  
  
  class   lowerCamelCase_							( unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece.model"				)
  							_snake_case			 = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						0				)
  
  							_snake_case			 = FakeOriginalTokenizer(vocab_file=__UpperCamelCase				)
  
  							with warnings.catch_warnings(record=__UpperCamelCase				) as w:
  									_snake_case			 = SpmConverter(__UpperCamelCase				)
  							self.assertEqual(len(__UpperCamelCase				),						1				)
  
  							self.assertIn(
  							    "The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"
  							    " which is not implemented in the fast tokenizers.",						str(w[0].message				),						)
  
  
  
  
  
  