
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()
snake_case_      :					Optional[Any]				      =     logging.get_logger(__name__)





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



							def     A__ (     self	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     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	,				__snake_case							)						->     str:






								"""simple docstring"""



								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     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								return list(filter(lambda __snake_case							: 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=lowercase		)
							lowerCamelCase__:				List      =							field(default_factory=lowercase		)



							def __call__(     self	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     Tracker(self.dest							)(__snake_case							).parametrized
								UpperCAmelCase:					Optional[Any] 						=     Tracker(self.src							)(__snake_case							).parametrized

								UpperCAmelCase:					Optional[Any] 						=     list(filter(lambda __snake_case							: type(__snake_case							) not in self.src_skip	,				__snake_case							)							)
								UpperCAmelCase:					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      __UpperCAmelCase    (  snake_case_							:		nn.Module				,					snake_case_							:		nn.Module				):



	'''simple docstring'''



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

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

	our_model.load_state_dict(snake_case_				)
	return our_model



def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		VanConfig				,					snake_case_							:		str				,					snake_case_							:		nn.Module				,					snake_case_							:		Path				,					snake_case_							:		bool = True				,					):



	'''simple docstring'''



	print(F'Downloading weights for {name}...'				)
	UpperCAmelCase:					Any 						=     cached_download(snake_case_				)
	print(F'Converting {name}...'				)
	UpperCAmelCase:					Optional[Any] 						=     torch.load(snake_case_				)["state_dict"]
	from_model.load_state_dict(snake_case_				)
	from_model.eval()
	with torch.no_grad():
		UpperCAmelCase:					Optional[int] 						=     VanForImageClassification(snake_case_				).eval()
		UpperCAmelCase:					int 						=     ModuleTransfer(src=snake_case_				,					dest=snake_case_				)
		UpperCAmelCase:					Union[str, Any] 						=     torch.randn((1, 3, 2_2_4, 2_2_4)				)
		module_transfer(snake_case_				)
		UpperCAmelCase:					Optional[int] 						=     copy_parameters(snake_case_				,					snake_case_				)

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

	UpperCAmelCase:					Optional[Any] 						=     name
	print(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=snake_case_				,					)

		# we can use the convnext one
		UpperCAmelCase:					Optional[Any] 						=     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=snake_case_				,					)

		print(F'Pushed {checkpoint_name}'				)



def      __UpperCAmelCase    (  snake_case_							:		Path				,					snake_case_							:		str = None				,					snake_case_							:		bool = True				):



	'''simple docstring'''



	UpperCAmelCase:					int 						=     "imagenet-1k-id2label.json"
	UpperCAmelCase:					Optional[Any] 						=     1_0_0_0

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

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

	UpperCAmelCase:					int 						=     partial(snake_case_				,					num_labels=snake_case_				,					idalabel=snake_case_				,					labelaid=snake_case_				)

	UpperCAmelCase:					Optional[int] 						=     {
	    "van-tiny": ImageNetPreTrainedConfig(
	        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]				,					depths=[3, 3, 5, 2]				,					mlp_ratios=[8, 8, 4, 4]				,					),
	    "van-small": ImageNetPreTrainedConfig(
	        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]				,					depths=[2, 2, 4, 2]				,					mlp_ratios=[8, 8, 4, 4]				,					),
	    "van-base": ImageNetPreTrainedConfig(
	        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]				,					depths=[3, 3, 1_2, 3]				,					mlp_ratios=[8, 8, 4, 4]				,					),
	    "van-large": ImageNetPreTrainedConfig(
	        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]				,					depths=[3, 5, 2_7, 3]				,					mlp_ratios=[8, 8, 4, 4]				,					),
	}

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

	UpperCAmelCase:					Optional[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(
		    snake_case_				,					names_to_config[model_name]				,					checkpoint=names_to_original_checkpoints[model_name]				,					from_model=names_to_original_models[model_name]()				,					save_directory=snake_case_				,					push_to_hub=snake_case_				,					)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(
			    snake_case_				,					snake_case_				,					checkpoint=names_to_original_checkpoints[model_name]				,					from_model=names_to_original_models[model_name]()				,					save_directory=snake_case_				,					push_to_hub=snake_case_				,					)


if __name__ == "__main__":
	snake_case_      :					Optional[Any]				      =     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.',
	)

	snake_case_      :					int				      =     parser.parse_args()
	snake_case_      :					Path				      =     args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	snake_case_      :					List[str]				      =     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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class 		__lowerCamelCase     (   lowercase		):
							lowerCamelCase__:				Any      =							['''image_processor''', '''tokenizer''']
							lowerCamelCase__:				List[Any]      =							'''EfficientNetImageProcessor'''
							lowerCamelCase__:				str      =							('''BertTokenizer''', '''BertTokenizerFast''')



							def __init__(     self	,				__snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								super().__init__(__snake_case	,				__snake_case							)



							def __call__(     self	,				__snake_case=None	,				__snake_case=None	,				__snake_case="max_length"	,				__snake_case=6_4	,				__snake_case=None	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."							)

								if text is not None:
									UpperCAmelCase:					Dict 						=     self.tokenizer(
									    __snake_case	,				padding=__snake_case	,				max_length=__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if images is not None:
									UpperCAmelCase:					List[str] 						=     self.image_processor(__snake_case	,				return_tensors=__snake_case	,				**__snake_case							)

								if text is not None and images is not None:
									UpperCAmelCase:					Optional[int] 						=     image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**__snake_case							)	,				tensor_type=__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     Any:






								"""simple docstring"""



								return self.tokenizer.batch_decode(*__snake_case	,				**__snake_case							)



							def     A__ (     self	,				*__snake_case	,				**__snake_case							)						->     List[str]:






								"""simple docstring"""



								return self.tokenizer.decode(*__snake_case	,				**__snake_case							)




							@property
							def     A__ (     self							)						->     Optional[int]:






								"""simple docstring"""



								UpperCAmelCase:					Optional[int] 						=     self.tokenizer.model_input_names
								UpperCAmelCase:					int 						=     self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)
