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    :      Tuple	 =						logging.get_logger(__name__)


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




							def 		__snake_case(  self      :    Any		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Tensor		,							_UpperCamelCase      :    Tensor					)    -> Any:



												'''simple docstring'''

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




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



												'''simple docstring'''

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





							@property
							def 		__snake_case(  self      :    Optional[Any]					)    -> str:



												'''simple docstring'''

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



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




							def __call__(  self      :    str		,							_UpperCamelCase      :    Tensor					)    -> Optional[Any]:



												'''simple docstring'''

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

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

												if len(_UpperCamelCase					) != len(_UpperCamelCase					):
																	raise Exception(
																	    F"Numbers of operations are different. Source module has {len(_UpperCamelCase					)} operations while"
																	    F" destination module has {len(_UpperCamelCase					)}."					)

												for dest_m, src_m in zip(_UpperCamelCase		,							_UpperCamelCase					):
																	dest_m.load_state_dict(src_m.state_dict()					)
																	if self.verbose == 1:
																						print(F"Transfered from={src_m} to={dest_m}"					)





def 		__lowerCamelCase							(UpperCAmelCase__   :							nn.Module					,       UpperCAmelCase__   :							nn.Module     ):
					# 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(UpperCAmelCase__     )

					our_model.load_state_dict(UpperCAmelCase__     )
					return our_model





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							VanConfig					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							nn.Module					,       UpperCAmelCase__   :							Path					,       UpperCAmelCase__   :							bool = True					,       ):
					print(F"Downloading weights for {name}..."     )
					SCREAMING_SNAKE_CASE				=				cached_download(UpperCAmelCase__     )
					print(F"Converting {name}..."     )
					SCREAMING_SNAKE_CASE				=				torch.load(UpperCAmelCase__     )["state_dict"]
					from_model.load_state_dict(UpperCAmelCase__     )
					from_model.eval()
					with torch.no_grad():
										SCREAMING_SNAKE_CASE				=				VanForImageClassification(UpperCAmelCase__     ).eval()
										SCREAMING_SNAKE_CASE				=				ModuleTransfer(src=UpperCAmelCase__					,       dest=UpperCAmelCase__     )
										SCREAMING_SNAKE_CASE				=				torch.randn((1, 3, 2_2_4, 2_2_4)     )
										module_transfer(UpperCAmelCase__     )
										SCREAMING_SNAKE_CASE				=				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."     )

					SCREAMING_SNAKE_CASE				=				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
										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=UpperCAmelCase__					,       )

										print(F"Pushed {checkpoint_name}"     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							Path					,       UpperCAmelCase__   :							str = None					,       UpperCAmelCase__   :							bool = 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(UpperCAmelCase__					,       UpperCAmelCase__					,       repo_type="dataset"     )					,       "r"     )     )
					SCREAMING_SNAKE_CASE				=				{int(UpperCAmelCase__     ): 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(UpperCAmelCase__					,       num_labels=UpperCAmelCase__					,       idalabel=UpperCAmelCase__					,       labelaid=UpperCAmelCase__     )

					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(
										    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    :      Optional[Any]	 =						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    :      List[Any]	 =						parser.parse_args()
							_lowerCamelCase    :      Path	 =						args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							_lowerCamelCase    :      Dict	 =						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 math





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):

					if 1 < number < 4:
										# 2 and 3 are primes
										return True
					elif number < 2 or number % 2 == 0 or number % 3 == 0:
										# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
										return False

					# All primes number are in format of 6k +/- 1
					for i in range(5					,       int(math.sqrt(UpperCAmelCase__     ) + 1     )					,       6     ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True





def 		__lowerCamelCase							(UpperCAmelCase__   :							int = 2_0_0_0_0_0_0     ):
					return sum(num for num in range(3					,       UpperCAmelCase__					,       2     ) if is_prime(UpperCAmelCase__     )     ) + 2 if n > 2 else 0


if __name__ == "__main__":
							print(f"""{solution() = }""")




