




"""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        UpperCAmelCase_  :
		UpperCamelCase									=42
		UpperCamelCase									=field(default_factory=snake_case						)
		UpperCamelCase									=field(default_factory=snake_case						)
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Optional[Any]:
					__lowercase	:       Optional[int]				=   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							,		UpperCamelCase_   )		->   Union[str, 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 					_lowerCamelCase    (		self   )		->   Tuple:
					return list(filter(lambda UpperCamelCase_   : len(list(x.state_dict().keys()   )   ) > 0							,		self.traced   )   )


@dataclass
class        UpperCAmelCase_  :
		UpperCamelCase									=42
		UpperCamelCase									=42
		UpperCamelCase									=0
		UpperCamelCase									=field(default_factory=snake_case						)
		UpperCamelCase									=field(default_factory=snake_case						)
		def __call__(		self							,		UpperCamelCase_   )		->   List[Any]:
					__lowercase	:       Optional[int]				=   Tracker(self.dest   )(lowercase__   ).parametrized
					__lowercase	:       int				=   Tracker(self.src   )(lowercase__   ).parametrized

					__lowercase	:       Any				=   list(filter(lambda UpperCamelCase_   : type(lowercase__   ) not in self.src_skip							,		lowercase__   )   )
					__lowercase	:       Optional[int]				=   list(filter(lambda UpperCamelCase_   : 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.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
			__lowercase	:       Any				=   from_model.state_dict()
			__lowercase	:       int				=   our_model.state_dict()
			__lowercase	:       Union[str, Any]				=   our_model.config
			__lowercase	:       Optional[int]				=   []
			for stage_idx in range(len(config.hidden_sizes					)					):
						for block_id in range(config.depths[stage_idx]					):
									__lowercase	:       int				=   f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
									__lowercase	:       Optional[Any]				=   f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

									all_keys.append((from_key, to_key)					)
									__lowercase	:       List[Any]				=   f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
									__lowercase	:       List[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:
						__lowercase	:       List[Any]				=   from_state_dict.pop(SCREAMING_SNAKE_CASE_					)

			our_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
			return our_model







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase = True					,							):
			print(f"""Downloading weights for {name}..."""					)
			__lowercase	:       Optional[int]				=   cached_download(SCREAMING_SNAKE_CASE_					)
			print(f"""Converting {name}..."""					)
			__lowercase	:       List[Any]				=   torch.load(SCREAMING_SNAKE_CASE_					)['''state_dict''']
			from_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
			from_model.eval()
			with torch.no_grad():
						__lowercase	:       Any				=   VanForImageClassification(SCREAMING_SNAKE_CASE_					).eval()
						__lowercase	:       int				=   ModuleTransfer(src=SCREAMING_SNAKE_CASE_					,							dest=SCREAMING_SNAKE_CASE_					)
						__lowercase	:       str				=   torch.randn((1, 3, 2_24, 2_24)					)
						module_transfer(SCREAMING_SNAKE_CASE_					)
						__lowercase	:       str				=   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.'''					)

			__lowercase	:       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
						__lowercase	:       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=SCREAMING_SNAKE_CASE_					,							)

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







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase = None					,							__UpperCamelCase = True					):
			__lowercase	:       List[Any]				=   '''imagenet-1k-id2label.json'''
			__lowercase	:       List[str]				=   10_00

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

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

			__lowercase	:       Dict				=   partial(SCREAMING_SNAKE_CASE_					,							num_labels=SCREAMING_SNAKE_CASE_					,							idalabel=SCREAMING_SNAKE_CASE_					,							labelaid=SCREAMING_SNAKE_CASE_					)

			__lowercase	:       Optional[Any]				=   {
			    '''van-tiny''': ImageNetPreTrainedConfig(
			        hidden_sizes=[32, 64, 1_60, 2_56]					,							depths=[3, 3, 5, 2]					,							mlp_ratios=[8, 8, 4, 4]					,							),
			    '''van-small''': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 1_28, 3_20, 5_12]					,							depths=[2, 2, 4, 2]					,							mlp_ratios=[8, 8, 4, 4]					,							),
			    '''van-base''': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 1_28, 3_20, 5_12]					,							depths=[3, 3, 12, 3]					,							mlp_ratios=[8, 8, 4, 4]					,							),
			    '''van-large''': ImageNetPreTrainedConfig(
			        hidden_sizes=[64, 1_28, 3_20, 5_12]					,							depths=[3, 5, 27, 3]					,							mlp_ratios=[8, 8, 4, 4]					,							),
			}

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

			__lowercase	:       Optional[int]				=   {
			    '''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__":
							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"""




from collections.abc import Callable

a_            =   Callable[[float], float]  # type alias for a real -> real function







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							):
			__lowercase	:       str				=   float(__UpperCamelCase					)
			while True:
						try:
									__lowercase	:       Dict				=   prev_guess - function(__UpperCamelCase					) / derivative(__UpperCamelCase					)
						except ZeroDivisionError:
									raise ZeroDivisionError('''Could not find root'''					) from None
						if abs(prev_guess - next_guess					) < 10**-5:
									return next_guess
						__lowercase	:       List[Any]				=   next_guess







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return (x**3) - (2 * x) - 5







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return 3 * (x**2) - 2


if __name__ == "__main__":
			print(newton(f, fa, 3))



