
"""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()
snake_case			       =		logging.get_logger(__name__)




@dataclass
class 			UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				List[nn.Module]						  =    field(default_factory=__magic_name__				)
				UpperCAmelCase_    :				list						  =    field(default_factory=__magic_name__				)


				def 							A (    self		,     lowercase__		,     lowercase__		,     lowercase__      )   ->   Optional[Any]:
									"""simple docstring"""







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


				def __call__(    self		,     lowercase__      )   ->   Union[str, Any]:
									"""simple docstring"""







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




				@property
				def 							A (    self      )   ->   Tuple:
									"""simple docstring"""







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






@dataclass
class 			UpperCamelCase     :

				"""simple docstring"""



				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				nn.Module
				UpperCAmelCase_    :				int						  =    0
				UpperCAmelCase_    :				List						  =    field(default_factory=__magic_name__				)
				UpperCAmelCase_    :				List						  =    field(default_factory=__magic_name__				)


				def __call__(    self		,     lowercase__      )   ->   List[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     Tracker(self.dest      )(lowercase__      ).parametrized
									SCREAMING_SNAKE_CASE           =     Tracker(self.src      )(lowercase__      ).parametrized

									SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : type(lowercase__      ) not in self.src_skip		,     lowercase__      )      )
									SCREAMING_SNAKE_CASE           =     list(filter(lambda lowercase__      : type(lowercase__      ) not in self.dest_skip		,     lowercase__      )      )

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

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






def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
					SCREAMING_SNAKE_CASE           =     from_model.state_dict()
					SCREAMING_SNAKE_CASE           =     our_model.state_dict()
					SCREAMING_SNAKE_CASE           =     our_model.config
					SCREAMING_SNAKE_CASE           =     []
					for stage_idx in range(len(config.hidden_sizes      )      ):
										for block_id in range(config.depths[stage_idx]      ):
															SCREAMING_SNAKE_CASE           =     f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
															SCREAMING_SNAKE_CASE           =     f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

															all_keys.append((from_key, to_key)      )
															SCREAMING_SNAKE_CASE           =     f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
															SCREAMING_SNAKE_CASE           =     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           =     from_state_dict.pop(SCREAMING_SNAKE_CASE_      )

					our_model.load_state_dict(SCREAMING_SNAKE_CASE_      )
					return our_model

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = True,					):
					print(f'''Downloading weights for {name}...'''      )
					SCREAMING_SNAKE_CASE           =     cached_download(SCREAMING_SNAKE_CASE_      )
					print(f'''Converting {name}...'''      )
					SCREAMING_SNAKE_CASE           =     torch.load(SCREAMING_SNAKE_CASE_      )['state_dict']
					from_model.load_state_dict(SCREAMING_SNAKE_CASE_      )
					from_model.eval()
					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =     VanForImageClassification(SCREAMING_SNAKE_CASE_      ).eval()
										SCREAMING_SNAKE_CASE           =     ModuleTransfer(src=SCREAMING_SNAKE_CASE_,					dest=SCREAMING_SNAKE_CASE_      )
										SCREAMING_SNAKE_CASE           =     torch.randn((1, 3, 2_2_4, 2_2_4)      )
										module_transfer(SCREAMING_SNAKE_CASE_      )
										SCREAMING_SNAKE_CASE           =     copy_parameters(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

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

					SCREAMING_SNAKE_CASE           =     name
					print(SCREAMING_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=SCREAMING_SNAKE_CASE_,					)

										# we can use the convnext one
										SCREAMING_SNAKE_CASE           =     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=SCREAMING_SNAKE_CASE_,					)

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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ = None,					SCREAMING_SNAKE_CASE_ = True      ):
					SCREAMING_SNAKE_CASE           =     'imagenet-1k-id2label.json'
					SCREAMING_SNAKE_CASE           =     1_0_0_0

					SCREAMING_SNAKE_CASE           =     'huggingface/label-files'
					SCREAMING_SNAKE_CASE           =     num_labels
					SCREAMING_SNAKE_CASE           =     json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					repo_type='dataset'      ),					'r'      )      )
					SCREAMING_SNAKE_CASE           =     {int(SCREAMING_SNAKE_CASE_      ): v for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE           =     idalabel
					SCREAMING_SNAKE_CASE           =     {v: k for k, v in idalabel.items()}

					SCREAMING_SNAKE_CASE           =     partial(SCREAMING_SNAKE_CASE_,					num_labels=SCREAMING_SNAKE_CASE_,					idalabel=SCREAMING_SNAKE_CASE_,					labelaid=SCREAMING_SNAKE_CASE_      )

					SCREAMING_SNAKE_CASE           =     {
					    '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],					),
					}

					SCREAMING_SNAKE_CASE           =     {
					    'van-tiny': van_tiny,
					    'van-small': van_small,
					    'van-base': van_base,
					    'van-large': van_large,
					}

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


if __name__ == "__main__":
	snake_case			       =		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			       =		parser.parse_args()
	snake_case			       =		args.pytorch_dump_folder_path
	pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
	snake_case			       =		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"""




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


snake_case			       =		logging.getLogger(__name__)

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'      )
					parser.add_argument(
					    '--dataset_name',					type=SCREAMING_SNAKE_CASE_,					default='wikitext',					help='Name of the training. Explore datasets at: hf.co/datasets.',					)
					parser.add_argument(
					    '--dataset_config',					type=SCREAMING_SNAKE_CASE_,					default='wikitext-103-raw-v1',					help='Configuration name of the dataset.'      )
					parser.add_argument(
					    '--batch_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_0,					help='Batch size during training.',					)
					parser.add_argument(
					    '--vocab_size',					type=SCREAMING_SNAKE_CASE_,					default=1_0_0_4_8,					help='Size of the desired vocabulary.',					)
					parser.add_argument(
					    '--limit',					default=SCREAMING_SNAKE_CASE_,					type=SCREAMING_SNAKE_CASE_,					help='Limit the number of shards (used for debugging).',					)
					parser.add_argument(
					    '--export_to_hub',					action='store_true',					)

					SCREAMING_SNAKE_CASE           =     parser.parse_args()
					return args

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.load_dataset(args.dataset_name,					args.dataset_config,					split='train'      )

					if args.limit is not None:
										SCREAMING_SNAKE_CASE           =     min(len(SCREAMING_SNAKE_CASE_      ),					args.limit      )
										SCREAMING_SNAKE_CASE           =     dataset.select(range(SCREAMING_SNAKE_CASE_      )      )
										logger.info(f'''Limiting the dataset to {args.limit} entries.'''      )

					def batch_iterator():
										for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					args.batch_size      ):
															yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
					SCREAMING_SNAKE_CASE           =     Tokenizer(Unigram()      )
					SCREAMING_SNAKE_CASE           =     normalizers.Sequence([normalizers.Replace('``',					'"'      ), normalizers.Replace('\'\'',					'"'      )]      )
					SCREAMING_SNAKE_CASE           =     pre_tokenizers.Metaspace()

					# Prepare the trainer.
					SCREAMING_SNAKE_CASE           =     UnigramTrainer(
					    unk_token='<unk>',					special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]'],					vocab_size=args.vocab_size,					)

					logger.info('Training the tokenizer.'      )
					tokenizer.train_from_iterator(batch_iterator(),					trainer=SCREAMING_SNAKE_CASE_      )
					logger.info('Tokenizer training complete!'      )

					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[CLS]'      )
					SCREAMING_SNAKE_CASE           =     tokenizer.token_to_id('[SEP]'      )
					SCREAMING_SNAKE_CASE           =     processors.TemplateProcessing(
					    single='[CLS]:0 $A:0 [SEP]:0',					pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1',					special_tokens=[
					        ('[CLS]', cls_token_id),
					        ('[SEP]', sep_token_id),
					    ],					)
					SCREAMING_SNAKE_CASE           =     decoders.Metaspace()

					if args.export_to_hub:
										logger.info('Exporting the trained tokenzier to Hub.'      )
										SCREAMING_SNAKE_CASE           =     AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_      )
										new_tokenizer.push_to_hub('unigram-tokenizer-dataset'      )


if __name__ == "__main__":
	snake_case			       =		parse_args()
	main(args)
