





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()
lowerCAmelCase_												=       logging.get_logger(__name__)



@dataclass
class       __lowerCAmelCase :
			lowerCamelCase_  :  nn.Module
			lowerCamelCase_  :  List[nn.Module]     =   field(default_factory=_a  )
			lowerCamelCase_  :  list     =   field(default_factory=_a  )






			def     lowerCamelCase						(self      ,							__magic_name__      ,							__magic_name__      ,							__magic_name__					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Tuple			    =						len(list(m.modules()					)					) == 1 or isinstance(__magic_name__      ,							nn.Convad					) or isinstance(__magic_name__      ,							nn.BatchNormad					)
							if has_not_submodules:
											if not isinstance(__magic_name__      ,							__magic_name__					):
															self.traced.append(__magic_name__					)






			def __call__(self      ,							__magic_name__					)  ->     List[Any]:


							'''simple docstring'''



							for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook					)					)
							self.module(__magic_name__					)
							[x.remove() for x in self.handles]
							return self






			@property
			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							return list(filter(lambda __magic_name__					: len(list(x.state_dict().keys()					)					) > 0      ,							self.traced					)					)




@dataclass
class       __lowerCAmelCase :
			lowerCamelCase_  :  nn.Module
			lowerCamelCase_  :  nn.Module
			lowerCamelCase_  :  int     =   0
			lowerCamelCase_  :  List     =   field(default_factory=_a  )
			lowerCamelCase_  :  List     =   field(default_factory=_a  )






			def __call__(self      ,							__magic_name__					)  ->     Optional[Any]:


							'''simple docstring'''



							snake_case_		:					Dict			    =						Tracker(self.dest					)(__magic_name__					).parametrized
							snake_case_		:					Union[str, Any]			    =						Tracker(self.src					)(__magic_name__					).parametrized

							snake_case_		:					Dict			    =						list(filter(lambda __magic_name__					: type(__magic_name__					) not in self.src_skip      ,							__magic_name__					)					)
							snake_case_		:					Union[str, Any]			    =						list(filter(lambda __magic_name__					: type(__magic_name__					) not in self.dest_skip      ,							__magic_name__					)					)

							if len(__magic_name__					) != len(__magic_name__					):
											raise Exception(
											    F'''Numbers of operations are different. Source module has {len(__magic_name__					)} operations while'''
											    F''' destination module has {len(__magic_name__					)}.'''					)

							for dest_m, src_m in zip(__magic_name__      ,							__magic_name__					):
											dest_m.load_state_dict(src_m.state_dict()					)
											if self.verbose == 1:
															print(F'''Transfered from={src_m} to={dest_m}'''					)






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				)	->		nn.Module:






				"""simple docstring"""


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

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

				our_model.load_state_dict(_UpperCamelCase				)
				return our_model






def   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase				,       _UpperCamelCase = True				,       )	->		Tuple:






				"""simple docstring"""


				print(f'''Downloading weights for {name}...'''				)
				snake_case_		:					str			    =						cached_download(_UpperCamelCase				)
				print(f'''Converting {name}...'''				)
				snake_case_		:					str			    =						torch.load(_UpperCamelCase				)['''state_dict''']
				from_model.load_state_dict(_UpperCamelCase				)
				from_model.eval()
				with torch.no_grad():
								snake_case_		:					Any			    =						VanForImageClassification(_UpperCamelCase				).eval()
								snake_case_		:					Optional[int]			    =						ModuleTransfer(src=_UpperCamelCase				,       dest=_UpperCamelCase				)
								snake_case_		:					List[Any]			    =						torch.randn((1, 3, 224, 224)				)
								module_transfer(_UpperCamelCase				)
								snake_case_		:					List[str]			    =						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_		:					Tuple			    =						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_		:					Optional[int]			    =						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   lowerCamelCase_		(       _UpperCamelCase				,       _UpperCamelCase = None				,       _UpperCamelCase = True				)	->		Union[str, Any]:






				"""simple docstring"""


				snake_case_		:					Optional[int]			    =						'''imagenet-1k-id2label.json'''
				snake_case_		:					str			    =						1_000

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

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

				snake_case_		:					Dict			    =						partial(_UpperCamelCase				,       num_labels=_UpperCamelCase				,       idalabel=_UpperCamelCase				,       labelaid=_UpperCamelCase				)

				snake_case_		:					List[Any]			    =						{
				    '''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_		:					Dict			    =						{
				    '''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__":
	lowerCAmelCase_												=       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.''',
	)

	lowerCAmelCase_												=       parser.parse_args()
	lowerCAmelCase_												=       args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	lowerCAmelCase_												=       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_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_												=       {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa import MBartaaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
