
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 :
				_lowercase      		=42
				_lowercase      		=field(default_factory=__a	)
				_lowercase      		=field(default_factory=__a	)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				List[Any]:
							lowerCAmelCase_      =     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					)    ->				List[Any]:






							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 					__a						( self					)    ->				Union[str, Any]:
							return list(filter(lambda _UpperCamelCase					: len(list(x.state_dict().keys()					)					) > 0    ,    self.traced					)					)





@dataclass
class      _lowerCAmelCase :
				_lowercase      		=42
				_lowercase      		=42
				_lowercase      		=0
				_lowercase      		=field(default_factory=__a	)
				_lowercase      		=field(default_factory=__a	)




				def __call__( self    ,    _UpperCamelCase					)    ->				List[Any]:
							lowerCAmelCase_      =     Tracker(self.dest					)(_UpperCamelCase					).parametrized
							lowerCAmelCase_      =     Tracker(self.src					)(_UpperCamelCase					).parametrized

							lowerCAmelCase_      =     list(filter(lambda _UpperCamelCase					: type(_UpperCamelCase					) not in self.src_skip    ,    _UpperCamelCase					)					)
							lowerCAmelCase_      =     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        lowerCamelCase__    (    __lowerCAmelCase  :  nn.Module   ,							__lowerCAmelCase  :  nn.Module    ):




			"""simple docstring"""


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

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

			our_model.load_state_dict(SCREAMING_SNAKE_CASE_    )
			return our_model






def        lowerCamelCase__    (    __lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  VanConfig   ,							__lowerCAmelCase  :  str   ,							__lowerCAmelCase  :  nn.Module   ,							__lowerCAmelCase  :  Path   ,							__lowerCAmelCase  :  bool = True   ,							):




			"""simple docstring"""


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

			if not torch.allclose(from_model(SCREAMING_SNAKE_CASE_    )   ,							our_model(SCREAMING_SNAKE_CASE_    ).logits    ):
						raise ValueError("The model logits don't match the original one."    )

			lowerCAmelCase_      =     name
			print(SCREAMING_SNAKE_CASE_    )

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name   ,							commit_message="Add model"   ,							use_temp_dir=SCREAMING_SNAKE_CASE_   ,							)

						# we can use the convnext one
						lowerCAmelCase_      =     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=SCREAMING_SNAKE_CASE_   ,							)

						print(F"""Pushed {checkpoint_name}"""    )






def        lowerCamelCase__    (    __lowerCAmelCase  :  Path   ,							__lowerCAmelCase  :  str = None   ,							__lowerCAmelCase  :  bool = True    ):




			"""simple docstring"""


			lowerCAmelCase_      =     "imagenet-1k-id2label.json"
			lowerCAmelCase_      =     1000

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

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

			lowerCAmelCase_      =     partial(SCREAMING_SNAKE_CASE_   ,							num_labels=SCREAMING_SNAKE_CASE_   ,							idalabel=SCREAMING_SNAKE_CASE_   ,							labelaid=SCREAMING_SNAKE_CASE_    )

			lowerCAmelCase_      =     {
			    "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]   ,							),
			}

			lowerCAmelCase_      =     {
			    "van-tiny": van_tiny,
			    "van-small": van_small,
			    "van-base": van_base,
			    "van-large": van_large,
			}

			lowerCAmelCase_      =     {
			    "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(
						    SCREAMING_SNAKE_CASE_   ,							names_to_config[model_name]   ,							checkpoint=names_to_original_checkpoints[model_name]   ,							from_model=names_to_original_models[model_name]()   ,							save_directory=SCREAMING_SNAKE_CASE_   ,							push_to_hub=SCREAMING_SNAKE_CASE_   ,							)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(
									    SCREAMING_SNAKE_CASE_   ,							SCREAMING_SNAKE_CASE_   ,							checkpoint=names_to_original_checkpoints[model_name]   ,							from_model=names_to_original_models[model_name]()   ,							save_directory=SCREAMING_SNAKE_CASE_   ,							push_to_hub=SCREAMING_SNAKE_CASE_   ,							)


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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



