



"""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 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     List[nn.Module]		=	field(default_factory=_lowerCAmelCase							)
					_A						:     list		=	field(default_factory=_lowerCAmelCase							)






					def   lowerCAmelCase_							(			self:				Optional[int]		,	snake_case:				Union[str, Any]		,	snake_case:				Tensor		,	snake_case:				Tensor		)  ->      List[str]:
											snake_case_					:Optional[int]		=     len(list(m.modules()		)		) == 1 or isinstance(snake_case		,	nn.Convad		) or isinstance(snake_case		,	nn.BatchNormad		)
											if has_not_submodules:
																	if not isinstance(snake_case		,	snake_case		):
																							self.traced.append(snake_case		)






					def __call__(			self:				Optional[Any]		,	snake_case:				Tensor		)  ->      Optional[Any]:
											for m in self.module.modules():
																	self.handles.append(m.register_forward_hook(self._forward_hook		)		)
											self.module(snake_case		)
											[x.remove() for x in self.handles]
											return self






					@property
					def   lowerCAmelCase_							(			self:				str		)  ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case		: len(list(x.state_dict().keys()		)		) > 0		,	self.traced		)		)

@dataclass
class 							lowerCamelCase    :






					'''simple docstring'''





					_A						:     nn.Module
					_A						:     nn.Module
					_A						:     int		=	0
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)
					_A						:     List		=	field(default_factory=_lowerCAmelCase							)






					def __call__(			self:				List[Any]		,	snake_case:				Tensor		)  ->      List[Any]:
											snake_case_					:Optional[int]		=     Tracker(self.dest		)(snake_case		).parametrized
											snake_case_					:int		=     Tracker(self.src		)(snake_case		).parametrized

											snake_case_					:Optional[Any]		=     list(filter(lambda snake_case		: type(snake_case		) not in self.src_skip		,	snake_case		)		)
											snake_case_					:Any		=     list(filter(lambda snake_case		: type(snake_case		) not in self.dest_skip		,	snake_case		)		)

											if len(snake_case		) != len(snake_case		):
																	raise Exception(
																	    f"""Numbers of operations are different. Source module has {len(snake_case		)} operations while"""
																	    f""" destination module has {len(snake_case		)}."""		)

											for dest_m, src_m in zip(snake_case		,	snake_case		):
																	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      ):







						'''simple docstring'''


						snake_case_					:Any		=     from_model.state_dict()
						snake_case_					:Optional[Any]		=     our_model.state_dict()
						snake_case_					:Dict		=     our_model.config
						snake_case_					:List[str]		=     []
						for stage_idx in range(len(config.hidden_sizes      )      ):
												for block_id in range(config.depths[stage_idx]      ):
																		snake_case_					:List[Any]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		snake_case_					:Dict		=     f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)      )
																		snake_case_					:Optional[int]		=     f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		snake_case_					:Tuple		=     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_					:List[str]		=     from_state_dict.pop(_lowercase      )

						our_model.load_state_dict(_lowercase      )
						return our_model
def   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase,   _lowercase = True,   ):







						'''simple docstring'''


						print(f"""Downloading weights for {name}..."""      )
						snake_case_					:Union[str, Any]		=     cached_download(_lowercase      )
						print(f"""Converting {name}..."""      )
						snake_case_					:Union[str, Any]		=     torch.load(_lowercase      )["""state_dict"""]
						from_model.load_state_dict(_lowercase      )
						from_model.eval()
						with torch.no_grad():
												snake_case_					:List[str]		=     VanForImageClassification(_lowercase      ).eval()
												snake_case_					:Tuple		=     ModuleTransfer(src=_lowercase,   dest=_lowercase      )
												snake_case_					:int		=     torch.randn((1, 3, 224, 224)      )
												module_transfer(_lowercase      )
												snake_case_					:int		=     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_					:List[str]		=     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_					:str		=     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      ):







						'''simple docstring'''


						snake_case_					:str		=     """imagenet-1k-id2label.json"""
						snake_case_					:int		=     1000

						snake_case_					:List[Any]		=     """huggingface/label-files"""
						snake_case_					:List[Any]		=     num_labels
						snake_case_					:Optional[Any]		=     json.load(open(hf_hub_download(_lowercase,   _lowercase,   repo_type="""dataset"""      ),   """r"""      )      )
						snake_case_					:int		=     {int(_lowercase      ): v for k, v in idalabel.items()}

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

						snake_case_					:str		=     partial(_lowercase,   num_labels=_lowercase,   idalabel=_lowercase,   labelaid=_lowercase      )

						snake_case_					:Dict		=     {
						    """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_					:Optional[int]		=     {
						    """van-tiny""": van_tiny,
						    """van-small""": van_small,
						    """van-base""": van_base,
						    """van-large""": van_large,
						}

						snake_case_					:str		=     {
						    """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)





"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:str		=     ArgumentParser(
						    description=(
						        """PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"""
						    )      )

						# Optional arguments for the launch helper
						parser.add_argument("""--num_cores""",   type=_lowercase,   default=1,   help="""Number of TPU cores to use (1 or 8)."""      )

						# positional
						parser.add_argument(
						    """training_script""",   type=_lowercase,   help=(
						        """The full path to the single TPU training """
						        """program/script to be launched in parallel, """
						        """followed by all the arguments for the """
						        """training script"""
						    ),   )

						# rest from the training program
						parser.add_argument("""training_script_args""",   nargs=_lowercase      )

						return parser.parse_args()
def   A_    (     ):







						'''simple docstring'''


						snake_case_					:Any		=     parse_args()

						# Import training_script as a module.
						snake_case_					:str		=     Path(args.training_script      )
						sys.path.append(str(script_fpath.parent.resolve()      )      )
						snake_case_					:Dict		=     script_fpath.stem
						snake_case_					:int		=     importlib.import_module(_lowercase      )

						# Patch sys.argv
						snake_case_					:Optional[Any]		=     [args.training_script] + args.training_script_args + ["""--tpu_num_cores""", str(args.num_cores      )]

						xmp.spawn(mod._mp_fn,   args=(),   nprocs=args.num_cores      )


if __name__ == "__main__":
						main()
