






"""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		:	Tuple      =		logging.get_logger(__name__)





@dataclass
class  _UpperCAmelCase	:
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    List[nn.Module]							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    list							   =       field(default_factory=_A							)




					def   A  (    self					:						List[str]   ,						A					:						List[Any]   ,						A					:						Tensor   ,						A					:						Tensor       )  ->  List[str]:
							lowercase_			:			Optional[int]								=       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					:						Dict   ,						A					:						Tensor       )  ->  str:
							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   A  (    self					:						List[str]       )  ->  int:
							# 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	:
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    int							   =       0
					SCREAMING_SNAKE_CASE_		:    List							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    List							   =       field(default_factory=_A							)




					def __call__(    self					:						Dict   ,						A					:						Tensor       )  ->  int:
							lowercase_			:			Union[str, Any]								=       Tracker(self.dest       )(A       ).parametrized
							lowercase_			:			str								=       Tracker(self.src       )(A       ).parametrized

							lowercase_			:			List[str]								=       list(filter(lambda A       : type(A       ) not in self.src_skip   ,						A       )       )
							lowercase_			:			Dict								=       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 						lowercase   (    __snake_case    :			nn.Module		, __snake_case    :			nn.Module     ):
		# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
		lowercase_			:			Union[str, Any]								=       from_model.state_dict()
		lowercase_			:			List[str]								=       our_model.state_dict()
		lowercase_			:			List[Any]								=       our_model.config
		lowercase_			:			Dict								=       []
		for stage_idx in range(len(config.hidden_sizes     )     ):
				for block_id in range(config.depths[stage_idx]     ):
						lowercase_			:			int								=       F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
						lowercase_			:			List[Any]								=       F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

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

		our_model.load_state_dict(__snake_case     )
		return our_model



def 						lowercase   (    __snake_case    :			str		, __snake_case    :			VanConfig		, __snake_case    :			str		, __snake_case    :			nn.Module		, __snake_case    :			Path		, __snake_case    :			bool = True		, ):
		print(F'''Downloading weights for {name}...'''     )
		lowercase_			:			Tuple								=       cached_download(__snake_case     )
		print(F'''Converting {name}...'''     )
		lowercase_			:			Union[str, Any]								=       torch.load(__snake_case     )['''state_dict''']
		from_model.load_state_dict(__snake_case     )
		from_model.eval()
		with torch.no_grad():
				lowercase_			:			Optional[Any]								=       VanForImageClassification(__snake_case     ).eval()
				lowercase_			:			List[str]								=       ModuleTransfer(src=__snake_case		, dest=__snake_case     )
				lowercase_			:			str								=       torch.randn((1, 3, 2_2_4, 2_2_4)     )
				module_transfer(__snake_case     )
				lowercase_			:			List[str]								=       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.'''     )

		lowercase_			:			Union[str, 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
				lowercase_			:			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=__snake_case		, )

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



def 						lowercase   (    __snake_case    :			Path		, __snake_case    :			str = None		, __snake_case    :			bool = True     ):
		lowercase_			:			Dict								=       '''imagenet-1k-id2label.json'''
		lowercase_			:			int								=       1_0_0_0

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

		lowercase_			:			int								=       idalabel
		lowercase_			:			Any								=       {v: k for k, v in idalabel.items()}

		lowercase_			:			Union[str, Any]								=       partial(__snake_case		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid=__snake_case     )

		lowercase_			:			Dict								=       {
		    '''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]		, ),
		}

		lowercase_			:			List[str]								=       {
		    '''van-tiny''': van_tiny,
		    '''van-small''': van_small,
		    '''van-base''': van_base,
		    '''van-large''': van_large,
		}

		lowercase_			:			str								=       {
		    '''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__":
						__A		:	Union[str, 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.''',
						)

						__A		:	Dict      =		parser.parse_args()
						__A		:	Path      =		args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						__A		:	Dict      =		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_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Tuple      =		['''RealmTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RealmEmbedder''',
						    '''RealmForOpenQA''',
						    '''RealmKnowledgeAugEncoder''',
						    '''RealmPreTrainedModel''',
						    '''RealmReader''',
						    '''RealmScorer''',
						    '''load_tf_weights_in_realm''',
						]
						__A		:	Union[str, Any]      =		['''RealmRetriever''']


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

						__A		:	Optional[int]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



