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()
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							nn.Module
  			lowerCamelCase							:							List[nn.Module]       		=  field(default_factory=lowercase_				)
  			lowerCamelCase							:							list       		=  field(default_factory=lowercase_				)
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       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__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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 A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return list(filter(lambda lowerCamelCase__       : len(list(x.state_dict().keys()       )       ) > 0       ,     self.traced       )       )
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							nn.Module
  			lowerCamelCase							:							nn.Module
  			lowerCamelCase							:							int       		=  0
  			lowerCamelCase							:							List       		=  field(default_factory=lowercase_				)
  			lowerCamelCase							:							List       		=  field(default_factory=lowercase_				)
  
  
  
  			def __call__(					self       ,     lowerCamelCase__       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       Tracker(self.dest       )(lowerCamelCase__       ).parametrized
  									lowercase__							 =       Tracker(self.src       )(lowerCamelCase__       ).parametrized
  
  									lowercase__							 =       list(filter(lambda lowerCamelCase__       : type(lowerCamelCase__       ) not in self.src_skip       ,     lowerCamelCase__       )       )
  									lowercase__							 =       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  _A		(				lowercase__		,		lowercase__	):
  						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(lowercase__	)
  
  						our_model.load_state_dict(lowercase__	)
  						return our_model
  
  
  
  
  
  def  _A		(				lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__ = True		,		):
  						print(f'''Downloading weights for {name}...'''	)
  						lowercase__							 =       cached_download(lowercase__	)
  						print(f'''Converting {name}...'''	)
  						lowercase__							 =       torch.load(lowercase__	)["""state_dict"""]
  						from_model.load_state_dict(lowercase__	)
  						from_model.eval()
  						with torch.no_grad():
  												lowercase__							 =       VanForImageClassification(lowercase__	).eval()
  												lowercase__							 =       ModuleTransfer(src=lowercase__		,		dest=lowercase__	)
  												lowercase__							 =       torch.randn((1, 3, 224, 224)	)
  												module_transfer(lowercase__	)
  												lowercase__							 =       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."""	)
  
  						lowercase__							 =       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
  												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=lowercase__		,		)
  
  												print(f'''Pushed {checkpoint_name}'''	)
  
  
  
  
  
  def  _A		(				lowercase__		,		lowercase__ = None		,		lowercase__ = True	):
  						lowercase__							 =       """imagenet-1k-id2label.json"""
  						lowercase__							 =       1000
  
  						lowercase__							 =       """huggingface/label-files"""
  						lowercase__							 =       num_labels
  						lowercase__							 =       json.load(open(hf_hub_download(lowercase__		,		lowercase__		,		repo_type="""dataset"""	)		,		"""r"""	)	)
  						lowercase__							 =       {int(lowercase__	): v for k, v in idalabel.items()}
  
  						lowercase__							 =       idalabel
  						lowercase__							 =       {v: k for k, v in idalabel.items()}
  
  						lowercase__							 =       partial(lowercase__		,		num_labels=lowercase__		,		idalabel=lowercase__		,		labelaid=lowercase__	)
  
  						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(
  												    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__":
  				__A          =    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          =    parser.parse_args()
  				__A          =    args.pytorch_dump_folder_path
  				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
  				__A          =    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 timeit
  from typing import Callable, Optional
  
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_pyanvml_available, is_torch_available, logging
  from .benchmark_utils import (
      Benchmark,
      Memory,
      MemorySummary,
      measure_peak_memory_cpu,
      start_memory_tracing,
      stop_memory_tracing,
  )
  
  
  if is_torch_available():
  				import torch
  
  				from .benchmark_args import PyTorchBenchmarkArguments
  
  
  if is_pyanvml_available():
  				import pyanvml.pyanvml as nvml
  
  
  __A          =    logging.get_logger(__name__)
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									if self.args.torchscript:
  															lowercase__							 =       True
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_MAPPING[config.__class__](lowerCamelCase__       )
  
  									model.eval()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									if self.args.torchscript:
  															with torch.no_grad():
  																					lowercase__							 =       torch.jit.trace(lowerCamelCase__       ,     lowerCamelCase__       )
  									else:
  															lowercase__							 =       model
  
  									def encoder_decoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       )
  															return outputs
  
  									def encoder_forward():
  															with torch.no_grad():
  																					lowercase__							 =       inference_model(lowerCamelCase__       )
  															return outputs
  
  									lowercase__							 =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.config_dict[model_name]
  
  									lowercase__							 =       (
  									    hasattr(lowerCamelCase__       ,     """architectures"""       )
  									    and isinstance(config.architectures       ,     lowerCamelCase__       )
  									    and len(config.architectures       ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  															try:
  																					lowercase__							 =       config.architectures[0]
  																					lowercase__							 =       __import__("""transformers"""       ,     fromlist=[model_class]       )
  																					lowercase__							 =       getattr(lowerCamelCase__       ,     lowerCamelCase__       )
  																					lowercase__							 =       model_cls(lowerCamelCase__       )
  															except ImportError:
  																					raise ImportError(
  																					    F'''{model_class} does not exist. If you just want to test the pretrained model, you might want to'''
  																					    """ set `--only_pretrain_model` or `args.only_pretrain_model=True`."""       )
  									else:
  															lowercase__							 =       MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase__       )
  
  									if self.args.torchscript:
  															raise NotImplementedError("""Training for torchscript is currently not implemented"""       )
  									else:
  															lowercase__							 =       model
  
  									model.train()
  									model.to(self.args.device       )
  
  									# encoder-decoder has vocab size saved differently
  									lowercase__							 =       config.vocab_size if hasattr(lowerCamelCase__       ,     """vocab_size"""       ) else config.encoder.vocab_size
  									lowercase__							 =       torch.randint(lowerCamelCase__       ,     (batch_size, sequence_length)       ,     dtype=torch.long       ,     device=self.args.device       )
  
  									if self.args.fpaa:
  															logger.info("""Running training in Mixed Precision..."""       )
  															if not self.args.is_gpu:
  																					raise ValueError("""Mixed precision is possible only for GPU."""       )
  
  															# amp seems to have memory leaks so that memory usage
  															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
  															model.half()
  
  									def compute_loss_and_backprob_encoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  															lowercase__							 =       train_model(lowerCamelCase__       ,     decoder_input_ids=lowerCamelCase__       ,     labels=lowerCamelCase__       )[0]
  															loss.backward()
  															return loss
  
  									lowercase__							 =       (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.is_tpu or self.args.torchscript:
  																					# run additional 10 times to stabilize compilation for tpu and torchscript
  																					logger.info("""Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"""       )
  																					timeit.repeat(
  																					    lowerCamelCase__       ,     repeat=1       ,     number=5       ,     )
  
  															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
  															lowercase__							 =       timeit.repeat(
  															    lowerCamelCase__       ,     repeat=self.args.repeat       ,     number=10       ,     )
  
  															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
  																					import torch_xla.debug.metrics as met
  
  																					self.print_fn(met.metrics_report()       )
  
  															return min(lowerCamelCase__       ) / 10.0
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A"
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									try:
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       start_memory_tracing("""transformers"""       )
  
  															if self.args.is_tpu:
  																					# tpu
  																					raise NotImplementedError(
  																					    """Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"""
  																					    """ `--no-memory` or `args.memory=False`"""       )
  															elif self.args.is_gpu:
  																					if not is_pyanvml_available():
  																											logger.warning(
  																											    """py3nvml not installed, we won't log GPU memory usage. """
  																											    """Install py3nvml (pip install py3nvml) to log information about GPU."""       )
  																											lowercase__							 =       """N/A"""
  																					else:
  																											logger.info(
  																											    """Measuring total GPU usage on GPU device. Make sure to not have additional processes running"""
  																											    """ on the same GPU."""       )
  																											# init nvml
  																											nvml.nvmlInit()
  																											func()
  																											lowercase__							 =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx       )
  																											lowercase__							 =       nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase__       )
  																											lowercase__							 =       meminfo.used
  																											lowercase__							 =       Memory(lowerCamelCase__       )
  																											# shutdown nvml
  																											nvml.nvmlShutdown()
  															else:
  																					# cpu
  																					lowercase__							 =       measure_peak_memory_cpu(lowerCamelCase__       )
  																					lowercase__							 =       Memory(lowerCamelCase__       ) if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ) else memory_bytes
  
  															if self.args.trace_memory_line_by_line:
  																					lowercase__							 =       stop_memory_tracing(lowerCamelCase__       )
  															else:
  																					lowercase__							 =       None
  
  															return memory, summary
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A", None
  
  
  
  
  
  
  