"""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	:	Union[str, Any]=					logging.get_logger(__name__)

@dataclass
class 						UpperCamelCase     :
				UpperCAmelCase							: nn.Module
				UpperCAmelCase							: List[nn.Module]       =						field(default_factory=lowercase		)
				UpperCAmelCase							: list       =						field(default_factory=lowercase		)



				def    _lowercase	(self							: Tuple      ,    _A							: Tuple      ,    _A							: Tensor      ,    _A							: Tensor)       ->      Optional[int]:
					__snake_case     :				List[str] 						=					len(list(m.modules())) == 1 or isinstance(_A      ,    nn.Convad) or isinstance(_A      ,    nn.BatchNormad)
					if has_not_submodules:
						if not isinstance(_A      ,    _A):
							self.traced.append(_A)



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






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







@dataclass
class 						UpperCamelCase     :
				UpperCAmelCase							: nn.Module
				UpperCAmelCase							: nn.Module
				UpperCAmelCase							: int       =						0
				UpperCAmelCase							: List       =						field(default_factory=lowercase		)
				UpperCAmelCase							: List       =						field(default_factory=lowercase		)



				def __call__(self							: List[str]      ,    _A							: Tensor)       ->      List[str]:
					__snake_case     :				List[Any] 						=					Tracker(self.dest)(_A).parametrized
					__snake_case     :				Dict 						=					Tracker(self.src)(_A).parametrized

					__snake_case     :				Dict 						=					list(filter(lambda _A: type(_A) not in self.src_skip      ,    _A))
					__snake_case     :				Optional[int] 						=					list(filter(lambda _A: type(_A) not in self.dest_skip      ,    _A))

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

					for dest_m, src_m in zip(_A      ,    _A):
						dest_m.load_state_dict(src_m.state_dict())
						if self.verbose == 1:
							print(f"Transfered from={src_m} to={dest_m}")







def     __UpperCAmelCase							(       UpperCAmelCase_       :							nn.Module     ,				UpperCAmelCase_       :							nn.Module    )				->     nn.Module:

	'''simple docstring'''





	__snake_case     :				str 						=					from_model.state_dict()
	__snake_case     :				Tuple 						=					our_model.state_dict()
	__snake_case     :				int 						=					our_model.config
	__snake_case     :				Tuple 						=					[]
	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     :				List[Any] 						=					F"block{stage_idx + 1}.{block_id}.layer_scale_2"
			__snake_case     :				int 						=					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     :				Any 						=					from_state_dict.pop(UpperCAmelCase_    )

	our_model.load_state_dict(UpperCAmelCase_    )
	return our_model
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							VanConfig     ,				UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							nn.Module     ,				UpperCAmelCase_       :							Path     ,				UpperCAmelCase_       :							bool = True     ,				)				->     Dict:

	'''simple docstring'''





	print(F"Downloading weights for {name}..."    )
	__snake_case     :				List[Any] 						=					cached_download(UpperCAmelCase_    )
	print(F"Converting {name}..."    )
	__snake_case     :				List[str] 						=					torch.load(UpperCAmelCase_    )['state_dict']
	from_model.load_state_dict(UpperCAmelCase_    )
	from_model.eval()
	with torch.no_grad():
		__snake_case     :				Optional[int] 						=					VanForImageClassification(UpperCAmelCase_    ).eval()
		__snake_case     :				List[str] 						=					ModuleTransfer(src=UpperCAmelCase_     ,				dest=UpperCAmelCase_    )
		__snake_case     :				Optional[int] 						=					torch.randn((1, 3, 2_24, 2_24)    )
		module_transfer(UpperCAmelCase_    )
		__snake_case     :				List[Any] 						=					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.'    )

	__snake_case     :				Any 						=					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
		__snake_case     :				Dict 						=					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     __UpperCAmelCase							(       UpperCAmelCase_       :							Path     ,				UpperCAmelCase_       :							str = None     ,				UpperCAmelCase_       :							bool = True    )				->     List[str]:

	'''simple docstring'''





	__snake_case     :				Dict 						=					'imagenet-1k-id2label.json'
	__snake_case     :				Tuple 						=					10_00

	__snake_case     :				Any 						=					'huggingface/label-files'
	__snake_case     :				Union[str, Any] 						=					num_labels
	__snake_case     :				Tuple 						=					json.load(open(hf_hub_download(UpperCAmelCase_     ,				UpperCAmelCase_     ,				repo_type='dataset'    )     ,				'r'    )    )
	__snake_case     :				List[str] 						=					{int(UpperCAmelCase_    ): v for k, v in idalabel.items()}

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

	__snake_case     :				Tuple 						=					partial(UpperCAmelCase_     ,				num_labels=UpperCAmelCase_     ,				idalabel=UpperCAmelCase_     ,				labelaid=UpperCAmelCase_    )

	__snake_case     :				Optional[Any] 						=					{
	    'van-tiny': ImageNetPreTrainedConfig(
	        hidden_sizes=[32, 64, 1_60, 2_56]     ,				depths=[3, 3, 5, 2]     ,				mlp_ratios=[8, 8, 4, 4]     ,				),
	    'van-small': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12]     ,				depths=[2, 2, 4, 2]     ,				mlp_ratios=[8, 8, 4, 4]     ,				),
	    'van-base': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12]     ,				depths=[3, 3, 12, 3]     ,				mlp_ratios=[8, 8, 4, 4]     ,				),
	    'van-large': ImageNetPreTrainedConfig(
	        hidden_sizes=[64, 1_28, 3_20, 5_12]     ,				depths=[3, 5, 27, 3]     ,				mlp_ratios=[8, 8, 4, 4]     ,				),
	}

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

	__snake_case     :				Any 						=					{
	    '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	:	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.",
	)

	_a	:	Any=					parser.parse_args()
	_a	:	Path=					args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	_a	:	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)






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_a	:	Optional[Any]=					{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Dict=					[
	    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "GPTJForCausalLM",
	    "GPTJForQuestionAnswering",
	    "GPTJForSequenceClassification",
	    "GPTJModel",
	    "GPTJPreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	Optional[Any]=					[
	    "TFGPTJForCausalLM",
	    "TFGPTJForQuestionAnswering",
	    "TFGPTJForSequenceClassification",
	    "TFGPTJModel",
	    "TFGPTJPreTrainedModel",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a	:	List[str]=					[
	    "FlaxGPTJForCausalLM",
	    "FlaxGPTJModel",
	    "FlaxGPTJPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gptj import (
		    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTJForCausalLM,
		    GPTJForQuestionAnswering,
		    GPTJForSequenceClassification,
		    GPTJModel,
		    GPTJPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    TFGPTJPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
	import sys

	_a	:	Any=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





