






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()
__lowerCamelCase						= logging.get_logger(__name__)

@dataclass
class      UpperCamelCase_							:
						lowercase									=  42
						lowercase									=  field(default_factory=UpperCamelCase       )
						lowercase									=  field(default_factory=UpperCamelCase       )




						def      snake_case__(    self		,	lowercase		,	lowercase		,	lowercase       )			->						str:
													_a  :    str			=						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       )			->						List[Any]:
													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      snake_case__(    self       )			->						List[Any]:
													# check the len of the state_dict keys to see if we have learnable params
													return list(filter(lambda lowercase       : len(list(x.state_dict().keys()       )       ) > 0		,	self.traced       )       )

@dataclass
class      UpperCamelCase_							:
						lowercase									=  42
						lowercase									=  42
						lowercase									=  0
						lowercase									=  field(default_factory=UpperCamelCase       )
						lowercase									=  field(default_factory=UpperCamelCase       )




						def __call__(    self		,	lowercase       )			->						str:
													_a  :    Optional[int]			=						Tracker(self.dest       )(lowercase       ).parametrized
													_a  :    Optional[int]			=						Tracker(self.src       )(lowercase       ).parametrized

													_a  :    Optional[int]			=						list(filter(lambda lowercase       : type(lowercase       ) not in self.src_skip		,	lowercase       )       )
													_a  :    str			=						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__  (   UpperCAmelCase    ,	UpperCAmelCase     )     ->		nn.Module:







							"""simple docstring"""





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

																					all_keys.append((from_key, to_key)     )
																					_a  :    int			=						F'block{stage_idx + 1}.{block_id}.layer_scale_2'
																					_a  :    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:
														_a  :    Optional[Any]			=						from_state_dict.pop(UpperCAmelCase     )

							our_model.load_state_dict(UpperCAmelCase     )
							return our_model






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase = True    ,	)     ->		List[str]:







							"""simple docstring"""





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

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

							_a  :    List[str]			=						name
							print(UpperCAmelCase     )

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

														# we can use the convnext one
														_a  :    List[Any]			=						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=UpperCAmelCase    ,	)

														print(F'Pushed {checkpoint_name}'     )






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase = None    ,	UpperCAmelCase = True     )     ->		Dict:







							"""simple docstring"""





							_a  :    List[str]			=						'''imagenet-1k-id2label.json'''
							_a  :    List[str]			=						1000

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

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

							_a  :    List[str]			=						partial(UpperCAmelCase    ,	num_labels=UpperCAmelCase    ,	idalabel=UpperCAmelCase    ,	labelaid=UpperCAmelCase     )

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

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

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


if __name__ == "__main__":
						__lowerCamelCase						= 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.',
						)

						__lowerCamelCase						= parser.parse_args()
						__lowerCamelCase						= args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						__lowerCamelCase						= 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)







import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						__lowerCamelCase						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402

class      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

													# Pad with silence if necessary.
													if len(self.audio       ) < self.x_res * self.hop_length:
																				_a  :    str			=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowercase       )		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_mels=self.n_mels       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						librosa.feature.inverse.mel_to_audio(
													    lowercase		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_iter=self.n_iter       )
													return audio