



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					:				Optional[int]   	=logging.get_logger(__name__)






@dataclass
class 	_a			:
			_UpperCamelCase:   nn.Module
			_UpperCamelCase:   List[nn.Module]	     =		field(default_factory=snake_case_  )
			_UpperCamelCase:   list	     =		field(default_factory=snake_case_  )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[int]:
									lowerCAmelCase	:   Union[str, Any]					     =					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_ )	->					str:
									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 )	->					Tuple:
									# 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 	_a			:
			_UpperCamelCase:   nn.Module
			_UpperCamelCase:   nn.Module
			_UpperCamelCase:   int	     =		0
			_UpperCamelCase:   List	     =		field(default_factory=snake_case_  )
			_UpperCamelCase:   List	     =		field(default_factory=snake_case_  )






			def __call__(    self    ,  lowercase_ )	->					Optional[int]:
									lowerCAmelCase	:   Any					     =					Tracker(self.dest )(lowercase_ ).parametrized
									lowerCAmelCase	:   List[Any]					     =					Tracker(self.src )(lowercase_ ).parametrized

									lowerCAmelCase	:   Any					     =					list(filter(lambda lowercase_ : type(lowercase_ ) not in self.src_skip    ,  lowercase_ ) )
									lowerCAmelCase	:   Optional[int]					     =					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__  ):


						'''simple docstring'''




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

																		all_keys.append((from_key, to_key)  )
																		lowerCAmelCase	:   Tuple					     =					F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		lowerCAmelCase	:   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:
												lowerCAmelCase	:   str					     =					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			,):


						'''simple docstring'''




						print(F"""Downloading weights for {name}..."""  )
						lowerCAmelCase	:   int					     =					cached_download(SCREAMING_SNAKE_CASE__  )
						print(F"""Converting {name}..."""  )
						lowerCAmelCase	:   Union[str, Any]					     =					torch.load(SCREAMING_SNAKE_CASE__  )["""state_dict"""]
						from_model.load_state_dict(SCREAMING_SNAKE_CASE__  )
						from_model.eval()
						with torch.no_grad():
												lowerCAmelCase	:   List[Any]					     =					VanForImageClassification(SCREAMING_SNAKE_CASE__  ).eval()
												lowerCAmelCase	:   List[str]					     =					ModuleTransfer(src=SCREAMING_SNAKE_CASE__			,dest=SCREAMING_SNAKE_CASE__  )
												lowerCAmelCase	:   Union[str, Any]					     =					torch.randn((1, 3, 2_2_4, 2_2_4)  )
												module_transfer(SCREAMING_SNAKE_CASE__  )
												lowerCAmelCase	:   Any					     =					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."""  )

						lowerCAmelCase	:   List[str]					     =					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
												lowerCAmelCase	:   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=SCREAMING_SNAKE_CASE__			,)

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







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__ = None			,SCREAMING_SNAKE_CASE__ = True  ):


						'''simple docstring'''




						lowerCAmelCase	:   Tuple					     =					"""imagenet-1k-id2label.json"""
						lowerCAmelCase	:   Optional[int]					     =					1_0_0_0

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

						lowerCAmelCase	:   str					     =					idalabel
						lowerCAmelCase	:   List[str]					     =					{v: k for k, v in idalabel.items()}

						lowerCAmelCase	:   str					     =					partial(SCREAMING_SNAKE_CASE__			,num_labels=SCREAMING_SNAKE_CASE__			,idalabel=SCREAMING_SNAKE_CASE__			,labelaid=SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   List[Any]					     =					{
						    """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]			,),
						}

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

						lowerCAmelCase	:   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(
												    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__":
		lowerCAmelCase					:				str   	=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					:				Optional[int]   	=parser.parse_args()
		lowerCAmelCase					:				Path   	=args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		lowerCAmelCase					:				Optional[int]   	=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






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

									return np.mean((labels - prediction) ** 2 )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															print("""Error: X and y have different lengths""" )
															return
									if y.ndim != 1:
															print("""Error: Data set labels must be one dimensional""" )
															return

									if len(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

															lowerCAmelCase	:   Any					     =					x[best_split]
															lowerCAmelCase	:   str					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															lowerCAmelCase	:   List[str]					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															self.left.train(lowercase_    ,  lowercase_ )
															self.right.train(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					np.mean(lowercase_ )

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									if self.prediction is not None:
															return self.prediction
									elif self.left or self.right is not None:
															if x >= self.decision_boundary:
																					return self.right.predict(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

						print("""Test values: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Predictions: """ + str(SCREAMING_SNAKE_CASE__  )  )
						print("""Average error: """ + str(SCREAMING_SNAKE_CASE__  )  )


if __name__ == "__main__":
		main()
		import doctest

		doctest.testmod(name='mean_squarred_error', verbose=True)

