




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig
def 						lowerCAmelCase_       (	__a	,	__a	,	__a )       ->			Dict:





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Optional[int]	     =TaConfig.from_pretrained(__a )
							SCREAMING_SNAKE_CASE			: Dict	     =FlaxTaForConditionalGeneration(config=__a )
							SCREAMING_SNAKE_CASE			: List[Any]	     =checkpoints.load_tax_checkpoint(__a )

							SCREAMING_SNAKE_CASE			: List[str]	     ='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

							# Encoder
							for layer_index in range(config.num_layers ):
														SCREAMING_SNAKE_CASE			: str	     =f'layers_{str(__a )}'

														# Self-Attention
														SCREAMING_SNAKE_CASE			: Dict	     =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
														SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
														SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
														SCREAMING_SNAKE_CASE			: Tuple	     =tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

														# Layer Normalization
														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

														if split_mlp_wi:
																					SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					SCREAMING_SNAKE_CASE			: Tuple	     =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														SCREAMING_SNAKE_CASE			: Optional[Any]	     =tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														SCREAMING_SNAKE_CASE			: Dict	     =tax_attention_key
														SCREAMING_SNAKE_CASE			: str	     =tax_attention_out
														SCREAMING_SNAKE_CASE			: Any	     =tax_attention_query
														SCREAMING_SNAKE_CASE			: str	     =tax_attention_value

														SCREAMING_SNAKE_CASE			: List[Any]	     =tax_attention_layer_norm

														if split_mlp_wi:
																					SCREAMING_SNAKE_CASE			: int	     =tax_mlp_wi_a
																					SCREAMING_SNAKE_CASE			: Dict	     =tax_mlp_wi_a
														else:
																					SCREAMING_SNAKE_CASE			: Tuple	     =tax_mlp_wi

														SCREAMING_SNAKE_CASE			: List[str]	     =tax_mlp_wo
														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tax_mlp_layer_norm

							# Only for layer 0:
							SCREAMING_SNAKE_CASE			: Optional[Any]	     =tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tax_encoder_rel_embedding

							# Assigning
							SCREAMING_SNAKE_CASE			: List[str]	     =tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tax_encoder_norm

							# Decoder
							for layer_index in range(config.num_decoder_layers ):
														SCREAMING_SNAKE_CASE			: Optional[int]	     =f'layers_{str(__a )}'

														# Self-Attention
														SCREAMING_SNAKE_CASE			: Optional[Any]	     =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
														SCREAMING_SNAKE_CASE			: str	     =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
														SCREAMING_SNAKE_CASE			: Dict	     =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
														SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

														# Layer Normalization
														SCREAMING_SNAKE_CASE			: Tuple	     =tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
														    '''scale'''
														]

														# Encoder-Decoder-Attention
														SCREAMING_SNAKE_CASE			: List[str]	     =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
														    '''kernel'''
														]
														SCREAMING_SNAKE_CASE			: List[str]	     =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
														    '''kernel'''
														]
														SCREAMING_SNAKE_CASE			: Optional[Any]	     =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
														    '''kernel'''
														]
														SCREAMING_SNAKE_CASE			: List[Any]	     =tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
														    '''kernel'''
														]

														# Layer Normalization
														SCREAMING_SNAKE_CASE			: Any	     =tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

														# MLP
														if split_mlp_wi:
																					SCREAMING_SNAKE_CASE			: List[Any]	     =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																					SCREAMING_SNAKE_CASE			: Any	     =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
														else:
																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

														SCREAMING_SNAKE_CASE			: Dict	     =tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

														# Layer Normalization
														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

														# Assigning
														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_attention_key
														SCREAMING_SNAKE_CASE			: Tuple	     =tax_attention_out
														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_attention_query
														SCREAMING_SNAKE_CASE			: Dict	     =tax_attention_value

														SCREAMING_SNAKE_CASE			: List[Any]	     =tax_pre_attention_layer_norm

														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_enc_dec_attention_key
														SCREAMING_SNAKE_CASE			: Optional[int]	     =tax_enc_dec_attention_out
														SCREAMING_SNAKE_CASE			: Optional[Any]	     =tax_enc_dec_attention_query
														SCREAMING_SNAKE_CASE			: List[Any]	     =tax_enc_dec_attention_value

														SCREAMING_SNAKE_CASE			: List[Any]	     =tax_cross_layer_norm

														if split_mlp_wi:
																					SCREAMING_SNAKE_CASE			: Tuple	     =tax_mlp_wi_a
																					SCREAMING_SNAKE_CASE			: Tuple	     =tax_mlp_wi_a
														else:
																					SCREAMING_SNAKE_CASE			: Any	     =tax_mlp_wi

														SCREAMING_SNAKE_CASE			: int	     =tax_mlp_wo

														SCREAMING_SNAKE_CASE			: Dict	     =txa_mlp_layer_norm

							# Decoder Normalization
							SCREAMING_SNAKE_CASE			: int	     =tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
							SCREAMING_SNAKE_CASE			: Tuple	     =txa_decoder_norm

							# Only for layer 0:
							SCREAMING_SNAKE_CASE			: List[str]	     =tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
							SCREAMING_SNAKE_CASE			: Tuple	     =tax_decoder_rel_embedding

							# Token Embeddings
							SCREAMING_SNAKE_CASE			: List[str]	     =tax_model['''target''']['''token_embedder''']['''embedding''']
							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =txa_token_embeddings

							# LM Head (only in v1.1 checkpoints)
							if "logits_dense" in tax_model["target"]["decoder"]:
														SCREAMING_SNAKE_CASE			: str	     =tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

							flax_model.save_pretrained(__a )
							print('''T5X Model was sucessfully converted!''' )


if __name__ == "__main__":
							_A							    =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
							)
							parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
							parser.add_argument(
							    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
							)
							_A							    =	parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)












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)






