




"""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								=							logging.get_logger(__name__)






@dataclass
class  SCREAMING_SNAKE_CASE__   :
						_a        =  42
						_a        =  field(default_factory=_a   )
						_a        =  field(default_factory=_a   )


						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Tensor  ,  lowerCAmelCase			:     Tensor   ):
										lowerCAmelCase		=			len(list(m.modules()   )   ) == 1 or isinstance(lowerCAmelCase  ,  nn.Convad   ) or isinstance(lowerCAmelCase  ,  nn.BatchNormad   )
										if has_not_submodules:
														if not isinstance(lowerCAmelCase  ,  lowerCAmelCase   ):
																		self.traced.append(lowerCAmelCase   )


						def __call__(							self			:     Optional[int]  ,  lowerCAmelCase			:     Tensor   ):
										for m in self.module.modules():
														self.handles.append(m.register_forward_hook(self._forward_hook   )   )
										self.module(lowerCAmelCase   )
										[x.remove() for x in self.handles]
										return self




						@property
						def 					__lowercase	(							self			:     Tuple   ):
										# check the len of the state_dict keys to see if we have learnable params
										return list(filter(lambda lowerCAmelCase   : len(list(x.state_dict().keys()   )   ) > 0  ,  self.traced   )   )






@dataclass
class  SCREAMING_SNAKE_CASE__   :
						_a        =  42
						_a        =  42
						_a        =  0
						_a        =  field(default_factory=_a   )
						_a        =  field(default_factory=_a   )


						def __call__(							self			:     Dict  ,  lowerCAmelCase			:     Tensor   ):
										lowerCAmelCase		=			Tracker(self.dest   )(lowerCAmelCase   ).parametrized
										lowerCAmelCase		=			Tracker(self.src   )(lowerCAmelCase   ).parametrized

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

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

										for dest_m, src_m in zip(lowerCAmelCase  ,  lowerCAmelCase   ):
														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.Module:


				'''simple docstring'''






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

												all_keys.append((from_key, to_key)	)
												lowerCAmelCase		=			f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
												lowerCAmelCase		=			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:
								lowerCAmelCase		=			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 ,    )       ->					str:


				'''simple docstring'''






				print(f'''Downloading weights for {name}...'''	)
				lowerCAmelCase		=			cached_download(snake_case__	)
				print(f'''Converting {name}...'''	)
				lowerCAmelCase		=			torch.load(snake_case__	)["""state_dict"""]
				from_model.load_state_dict(snake_case__	)
				from_model.eval()
				with torch.no_grad():
								lowerCAmelCase		=			VanForImageClassification(snake_case__	).eval()
								lowerCAmelCase		=			ModuleTransfer(src=snake_case__ ,    dest=snake_case__	)
								lowerCAmelCase		=			torch.randn((1, 3, 224, 224)	)
								module_transfer(snake_case__	)
								lowerCAmelCase		=			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."""	)

				lowerCAmelCase		=			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
								lowerCAmelCase		=			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	)       ->					Dict:


				'''simple docstring'''






				lowerCAmelCase		=			"""imagenet-1k-id2label.json"""
				lowerCAmelCase		=			1_000

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

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

				lowerCAmelCase		=			partial(snake_case__ ,    num_labels=snake_case__ ,    idalabel=snake_case__ ,    labelaid=snake_case__	)

				lowerCAmelCase		=			{
				    """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] ,    ),
				}

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

				lowerCAmelCase		=			{
				    """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								=							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)






"""simple docstring"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""  ,  lowerCAmelCase  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError("""You need to specify an `image_processor`."""   )
										if tokenizer is None:
														raise ValueError("""You need to specify a `tokenizer`."""   )

										super().__init__(lowerCAmelCase  ,  lowerCAmelCase   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     str=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     int=None  ,  **lowerCAmelCase			:     str   ):

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

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and videos is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor_class




						@property
						def 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





