




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


_A							    =	logging.get_logger(__name__)

_A							    =	{"""vocab_file""": """vocab.txt"""}

_A							    =	{
    """vocab_file""": {
        """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt""",
        """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt""",
        """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt""",
        """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt""",
    }
}

_A							    =	{
    """tau/splinter-base""": 512,
    """tau/splinter-base-qass""": 512,
    """tau/splinter-large""": 512,
    """tau/splinter-large-qass""": 512,
}

_A							    =	{
    """tau/splinter-base""": {"""do_lower_case""": False},
    """tau/splinter-base-qass""": {"""do_lower_case""": False},
    """tau/splinter-large""": {"""do_lower_case""": False},
    """tau/splinter-large-qass""": {"""do_lower_case""": False},
}


class 							_lowerCAmelCase		(     UpperCamelCase__	):
				lowerCamelCase__									=    VOCAB_FILES_NAMES
				lowerCamelCase__									=    PRETRAINED_VOCAB_FILES_MAP
				lowerCamelCase__									=    PRETRAINED_INIT_CONFIGURATION
				lowerCamelCase__									=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowerCamelCase__									=    SplinterTokenizer




				def __init__( self		,		snake_case_=None		,		snake_case_=None		,		snake_case_=True		,		snake_case_="[UNK]"		,		snake_case_="[SEP]"		,		snake_case_="[PAD]"		,		snake_case_="[CLS]"		,		snake_case_="[MASK]"		,		snake_case_="[QUESTION]"		,		snake_case_=True		,		snake_case_=None		,		**snake_case_		,		)    ->      Optional[Any]:
											super().__init__(
											    snake_case_		,		tokenizer_file=snake_case_		,		do_lower_case=snake_case_		,		unk_token=snake_case_		,		sep_token=snake_case_		,		pad_token=snake_case_		,		cls_token=snake_case_		,		mask_token=snake_case_		,		tokenize_chinese_chars=snake_case_		,		strip_accents=snake_case_		,		additional_special_tokens=(question_token,)		,		**snake_case_		,		)

											SCREAMING_SNAKE_CASE			: Any	     =json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
											if (
											    pre_tok_state.get('''lowercase'''		,		snake_case_							) != do_lower_case
											    or pre_tok_state.get('''strip_accents'''		,		snake_case_							) != strip_accents
											):
																		SCREAMING_SNAKE_CASE			: List[Any]	     =getattr(snake_case_		,		pre_tok_state.pop('''type'''							)							)
																		SCREAMING_SNAKE_CASE			: List[str]	     =do_lower_case
																		SCREAMING_SNAKE_CASE			: List[Any]	     =strip_accents
																		SCREAMING_SNAKE_CASE			: Tuple	     =pre_tok_class(**snake_case_							)

											SCREAMING_SNAKE_CASE			: Any	     =do_lower_case




				@property
				def     __a		( self							)    ->      Dict:
											return self.convert_tokens_to_ids(self.question_token							)




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

											SCREAMING_SNAKE_CASE			: Tuple	     =[self.cls_token_id]
											SCREAMING_SNAKE_CASE			: Any	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: List[Any]	     =[self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
											if self.padding_side == "right":
																		# Input is question-then-context
																		return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
											else:
																		# Input is context-then-question
																		return cls + token_ids_a + sep + token_ids_a + question_suffix + sep




				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      List[int]:
											SCREAMING_SNAKE_CASE			: Any	     =[self.sep_token_id]
											SCREAMING_SNAKE_CASE			: List[Any]	     =[self.cls_token_id]
											SCREAMING_SNAKE_CASE			: Dict	     =[self.question_token_id] + [self.convert_tokens_to_ids('''.'''							)]
											if token_ids_a is None:
																		return len(cls + token_ids_a + sep							) * [0]

											if self.padding_side == "right":
																		# Input is question-then-context
																		return len(cls + token_ids_a + question_suffix + sep							) * [0] + len(token_ids_a + sep							) * [1]
											else:
																		# Input is context-then-question
																		return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + question_suffix + sep							) * [1]







				def     __a		( self		,		snake_case_		,		snake_case_ = None							)    ->      Tuple[str]:
											SCREAMING_SNAKE_CASE			: List[Any]	     =self._tokenizer.model.save(snake_case_		,		name=snake_case_							)
											return tuple(snake_case_							)












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		:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)




				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Tuple:
											SCREAMING_SNAKE_CASE			: Tuple	     =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_							)    ->      List[Any]:
											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							)    ->      int:
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda snake_case_							: len(list(x.state_dict().keys()							)							) > 0		,		self.traced							)							)







@dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    42
				lowerCamelCase__									=    0
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)
				lowerCamelCase__									=    field(default_factory=UpperCamelCase__	)




				def __call__( self		,		snake_case_							)    ->      List[str]:
											SCREAMING_SNAKE_CASE			: List[Any]	     =Tracker(self.dest							)(snake_case_							).parametrized
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =Tracker(self.src							)(snake_case_							).parametrized

											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =list(filter(lambda snake_case_							: type(snake_case_							) not in self.src_skip		,		snake_case_							)							)
											SCREAMING_SNAKE_CASE			: Dict	     =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 						lowerCAmelCase_       (	__a	,	__a )       ->			nn.Module:





							"""simple docstring"""





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

																					all_keys.append((from_key, to_key) )
																					SCREAMING_SNAKE_CASE			: int	     =f'block{stage_idx + 1}.{block_id}.layer_scale_2'
																					SCREAMING_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:
														SCREAMING_SNAKE_CASE			: List[Any]	     =from_state_dict.pop(__a )

							our_model.load_state_dict(__a )
							return our_model
def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a	,	__a	,	__a = True	,	)       ->			Any:





							"""simple docstring"""





							print(f'Downloading weights for {name}...' )
							SCREAMING_SNAKE_CASE			: Dict	     =cached_download(__a )
							print(f'Converting {name}...' )
							SCREAMING_SNAKE_CASE			: Dict	     =torch.load(__a )['''state_dict''']
							from_model.load_state_dict(__a )
							from_model.eval()
							with torch.no_grad():
														SCREAMING_SNAKE_CASE			: int	     =VanForImageClassification(__a ).eval()
														SCREAMING_SNAKE_CASE			: Tuple	     =ModuleTransfer(src=__a	,	dest=__a )
														SCREAMING_SNAKE_CASE			: Any	     =torch.randn((1, 3, 224, 224) )
														module_transfer(__a )
														SCREAMING_SNAKE_CASE			: str	     =copy_parameters(__a	,	__a )

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

							SCREAMING_SNAKE_CASE			: Tuple	     =name
							print(__a )

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

														# we can use the convnext one
														SCREAMING_SNAKE_CASE			: Tuple	     =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=__a	,	)

														print(f'Pushed {checkpoint_name}' )
def 						lowerCAmelCase_       (	__a	,	__a = None	,	__a = True )       ->			int:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Tuple	     ='''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE			: Tuple	     =1000

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

							SCREAMING_SNAKE_CASE			: Optional[int]	     =idalabel
							SCREAMING_SNAKE_CASE			: Tuple	     ={v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE			: Tuple	     =partial(__a	,	num_labels=__a	,	idalabel=__a	,	labelaid=__a )

							SCREAMING_SNAKE_CASE			: int	     ={
							    '''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]	,	),
							}

							SCREAMING_SNAKE_CASE			: Union[str, Any]	     ={
							    '''van-tiny''': van_tiny,
							    '''van-small''': van_small,
							    '''van-base''': van_base,
							    '''van-large''': van_large,
							}

							SCREAMING_SNAKE_CASE			: List[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(
														    __a	,	names_to_config[model_name]	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    __a	,	__a	,	checkpoint=names_to_original_checkpoints[model_name]	,	from_model=names_to_original_models[model_name]()	,	save_directory=__a	,	push_to_hub=__a	,	)


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)






