





'''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()
__UpperCamelCase										=  logging.get_logger(__name__)




@dataclass
class    _A   :
		lowercase__:      nn.Module
		lowercase__:      List[nn.Module]							    =      field(default_factory=lowercase__		)
		lowercase__:      list							    =      field(default_factory=lowercase__		)

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      List[str]     ,			__magic_name__				:      Tensor     ,			__magic_name__				:      Tensor )				->						Union[str, Any]:


					"""simple docstring"""

					__snake_case				:			Optional[int]       			=    len(list(m.modules() ) ) == 1 or isinstance(_A     ,			nn.Convad ) or isinstance(_A     ,			nn.BatchNormad )
					if has_not_submodules:
								if not isinstance(_A     ,			_A ):
											self.traced.append(_A )

		def __call__(      self				:      Union[str, Any]     ,			__magic_name__				:      Tensor )				->						Union[str, Any]:


					"""simple docstring"""

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

		@property
		def   lowercase__    (      self				:      Union[str, Any] )				->						Union[str, Any]:


					"""simple docstring"""

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





@dataclass
class    _A   :
		lowercase__:      nn.Module
		lowercase__:      nn.Module
		lowercase__:      int							    =      0
		lowercase__:      List							    =      field(default_factory=lowercase__		)
		lowercase__:      List							    =      field(default_factory=lowercase__		)

		def __call__(      self				:      Tuple     ,			__magic_name__				:      Tensor )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    Tracker(self.dest )(_A ).parametrized
					__snake_case				:			List[Any]       			=    Tracker(self.src )(_A ).parametrized

					__snake_case				:			List[Any]       			=    list(filter(lambda __magic_name__ : type(_A ) not in self.src_skip     ,			_A ) )
					__snake_case				:			List[Any]       			=    list(filter(lambda __magic_name__ : type(_A ) not in self.dest_skip     ,			_A ) )

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

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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase   )   ->							Optional[Any]:



			"""simple docstring"""


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

									all_keys.append((from_key, to_key)   )
									__snake_case				:			List[str]       			=    F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
									__snake_case				:			Optional[Any]       			=    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:
						__snake_case				:			Optional[Any]       			=    from_state_dict.pop(_lowerCamelCase   )

			our_model.load_state_dict(_lowerCamelCase   )
			return our_model







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase = True						,  )   ->							List[str]:



			"""simple docstring"""


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

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

			__snake_case				:			Any       			=    name
			print(_lowerCamelCase   )

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

						# we can use the convnext one
						__snake_case				:			Optional[int]       			=    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=_lowerCamelCase						,  )

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







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase = None						,  _lowerCamelCase = True   )   ->							Union[str, Any]:



			"""simple docstring"""


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

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

			__snake_case				:			str       			=    idalabel
			__snake_case				:			Union[str, Any]       			=    {v: k for k, v in idalabel.items()}

			__snake_case				:			int       			=    partial(_lowerCamelCase						,  num_labels=_lowerCamelCase						,  idalabel=_lowerCamelCase						,  labelaid=_lowerCamelCase   )

			__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]						,  ),
			}

			__snake_case				:			Optional[int]       			=    {
			    'van-tiny': van_tiny,
			    'van-small': van_small,
			    'van-base': van_base,
			    'van-large': van_large,
			}

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


if __name__ == "__main__":
	__UpperCamelCase										=  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.",
	)

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






def 				_a	(					_lowerCamelCase   )   ->							bool:



			"""simple docstring"""


			__snake_case				:			Optional[Any]       			=    [list(_lowerCamelCase   ) for row in matrix]
			if matrix and isinstance(_lowerCamelCase						,  _lowerCamelCase   ):
						if isinstance(matrix[0]						,  _lowerCamelCase   ):
									__snake_case				:			str       			=    0
									for row in matrix:
												if prev_len == 0:
															__snake_case				:			Dict       			=    len(_lowerCamelCase   )
															__snake_case				:			List[Any]       			=    True
												else:
															__snake_case				:			Union[str, Any]       			=    prev_len == len(_lowerCamelCase   )
						else:
									__snake_case				:			List[Any]       			=    True
			else:
						__snake_case				:			List[str]       			=    False

			return result







def 				_a	(					_lowerCamelCase   )   ->							None:



			"""simple docstring"""


			if check_matrix(_lowerCamelCase   ) and len(_lowerCamelCase   ) > 0:
						__snake_case				:			List[str]       			=    [list(_lowerCamelCase   ) for row in a]
						__snake_case				:			Optional[Any]       			=    len(_lowerCamelCase   )
						if isinstance(a[0]						,  _lowerCamelCase   ):
									__snake_case				:			Tuple       			=    len(a[0]   )
						else:
									for dat in a:
												print(_lowerCamelCase   )
									return

						# horizotal printing increasing
						for i in range(0						,  _lowerCamelCase   ):
									print(a[0][i]   )
						# vertical printing down
						for i in range(1						,  _lowerCamelCase   ):
									print(a[i][mat_col - 1]   )
						# horizotal printing decreasing
						if mat_row > 1:
									for i in range(mat_col - 2						,  -1						,  -1   ):
												print(a[mat_row - 1][i]   )
        # vertical printing up
						for i in range(mat_row - 2						,  0						,  -1   ):
									print(a[i][0]   )
						__snake_case				:			Union[str, Any]       			=    [row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
						if len(_lowerCamelCase   ) > 0:
									spiral_print_clockwise(_lowerCamelCase   )
						else:
									return
			else:
						print("""Not a valid matrix"""   )
						return







def 				_a	(					_lowerCamelCase   )   ->							list[int]:



			"""simple docstring"""


			if matrix:
						return list(matrix.pop(0   )   ) + spiral_traversal(list(zip(*_lowerCamelCase   )   )[::-1]   )
			else:
						return []


# driver code
if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__UpperCamelCase										=  [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
	spiral_print_clockwise(a)


