


"""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"""
							SCREAMING_SNAKE_CASE              =   42
							SCREAMING_SNAKE_CASE              =   field(default_factory=__SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE              =   field(default_factory=__SCREAMING_SNAKE_CASE			)






							def        lowerCAmelCase  (    self			:  Union[str, Any]		,			lowerCamelCase_			:  List[str]		,			lowerCamelCase_			:  Tensor		,			lowerCamelCase_			:  Tensor					):
													UpperCAmelCase__												=     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			:  Tuple		,			lowerCamelCase_			:  Tensor					):
													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			:  str					):
													# 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 				lowerCamelCase							:


							"""simple docstring"""
							SCREAMING_SNAKE_CASE              =   42
							SCREAMING_SNAKE_CASE              =   42
							SCREAMING_SNAKE_CASE              =   0
							SCREAMING_SNAKE_CASE              =   field(default_factory=__SCREAMING_SNAKE_CASE			)
							SCREAMING_SNAKE_CASE              =   field(default_factory=__SCREAMING_SNAKE_CASE			)






							def __call__(    self			:  Optional[Any]		,			lowerCamelCase_			:  Tensor					):
													UpperCAmelCase__												=     Tracker(self.dest					)(lowerCamelCase_					).parametrized
													UpperCAmelCase__												=     Tracker(self.src					)(lowerCamelCase_					).parametrized

													UpperCAmelCase__												=     list(filter(lambda lowerCamelCase_					: type(lowerCamelCase_					) not in self.src_skip		,			lowerCamelCase_					)					)
													UpperCAmelCase__												=     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__			(							_UpperCamelCase						,      _UpperCamelCase			):





						"""simple docstring"""

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

																		all_keys.append((from_key, to_key)			)
																		UpperCAmelCase__												=     F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		UpperCAmelCase__												=     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:
												UpperCAmelCase__												=     from_state_dict.pop(_UpperCamelCase			)

						our_model.load_state_dict(_UpperCamelCase			)
						return our_model






def        a__			(							_UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase						,      _UpperCamelCase = True						,      ):





						"""simple docstring"""

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

						UpperCAmelCase__												=     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
												UpperCAmelCase__												=     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        a__			(							_UpperCamelCase						,      _UpperCamelCase = None						,      _UpperCamelCase = True			):





						"""simple docstring"""

						UpperCAmelCase__												=     '''imagenet-1k-id2label.json'''
						UpperCAmelCase__												=     1000

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

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

						UpperCAmelCase__												=     partial(_UpperCamelCase						,      num_labels=_UpperCamelCase						,      idalabel=_UpperCamelCase						,      labelaid=_UpperCamelCase			)

						UpperCAmelCase__												=     {
						    '''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]						,      ),
						}

						UpperCAmelCase__												=     {
						    '''van-tiny''': van_tiny,
						    '''van-small''': van_small,
						    '''van-base''': van_base,
						    '''van-large''': van_large,
						}

						UpperCAmelCase__												=     {
						    '''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__":
						__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"""

						UpperCAmelCase__												=     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=_UpperCamelCase						,      default=1						,      help='''Number of TPU cores to use (1 or 8).'''			)

						# positional
						parser.add_argument(
						    '''training_script'''						,      type=_UpperCamelCase						,      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=_UpperCamelCase			)

						return parser.parse_args()






def        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [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()