




"""simple docstring"""

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase  :			str        =       logging.get_logger()

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







				def 					a_  (							self   :					int      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					List[str]      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		len(list(m.modules()       )       ) == 1 or isinstance(_lowercase      ,     nn.Convad       ) or isinstance(_lowercase      ,     nn.BatchNormad       )
					if has_not_submodules:
						self.traced.append(_lowercase       )







				def __call__(							self   :					Dict      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					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 					a_  (							self   :					Dict       ):



					"""simple docstring"""

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

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







				def __call__(							self   :					Any      ,     _lowerCamelCase   :					List[str]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		Tracker(self.dest       )(_lowercase       ).parametrized
					A_  :				Any	   =		Tracker(self.src       )(_lowercase       ).parametrized

					A_  :				Optional[Any]	   =		list(filter(lambda _lowerCamelCase       : type(_lowercase       ) not in self.src_skip      ,     _lowercase       )       )
					A_  :				Tuple	   =		list(filter(lambda _lowerCamelCase       : 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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase = True	):




	"""simple docstring"""






	print(f"""Converting {name}..."""	)
	with torch.no_grad():
		A_  :				List[str]	   =		timm.create_model(_UpperCAmelCase  ,      pretrained=_UpperCAmelCase	).eval()
		A_  :				List[str]	   =		ResNetForImageClassification(_UpperCAmelCase	).eval()
		A_  :				Tuple	   =		ModuleTransfer(src=_UpperCAmelCase  ,      dest=_UpperCAmelCase	)
		A_  :				Optional[int]	   =		torch.randn((1, 3, 224, 224)	)
		module_transfer(_UpperCAmelCase	)

	assert torch.allclose(from_model(_UpperCAmelCase	)  ,      our_model(_UpperCAmelCase	).logits	), "The model logits don't match the original one."

	A_  :				Optional[Any]	   =		f"""resnet{'-'.join(name.split('resnet'	)	)}"""
	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_  :				Dict	   =		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 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = True	):




	"""simple docstring"""






	A_  :				List[Any]	   =		'imagenet-1k-id2label.json'
	A_  :				Union[str, Any]	   =		1000
	A_  :				str	   =		(1, num_labels)

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

	A_  :				Any	   =		idalabel
	A_  :				int	   =		{v: k for k, v in idalabel.items()}

	A_  :				List[str]	   =		partial(_UpperCAmelCase  ,      num_labels=_UpperCAmelCase  ,      idalabel=_UpperCAmelCase  ,      labelaid=_UpperCAmelCase	)

	A_  :				List[str]	   =		{
	    'resnet18': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]  ,      hidden_sizes=[64, 128, 256, 512]  ,      layer_type='''basic'''	),
	    'resnet26': ImageNetPreTrainedConfig(
	        depths=[2, 2, 2, 2]  ,      hidden_sizes=[256, 512, 1024, 2048]  ,      layer_type='''bottleneck'''	),
	    'resnet34': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]  ,      hidden_sizes=[64, 128, 256, 512]  ,      layer_type='''basic'''	),
	    'resnet50': ImageNetPreTrainedConfig(
	        depths=[3, 4, 6, 3]  ,      hidden_sizes=[256, 512, 1024, 2048]  ,      layer_type='''bottleneck'''	),
	    'resnet101': ImageNetPreTrainedConfig(
	        depths=[3, 4, 23, 3]  ,      hidden_sizes=[256, 512, 1024, 2048]  ,      layer_type='''bottleneck'''	),
	    'resnet152': ImageNetPreTrainedConfig(
	        depths=[3, 8, 36, 3]  ,      hidden_sizes=[256, 512, 1024, 2048]  ,      layer_type='''bottleneck'''	),
	}

	if model_name:
		convert_weight_and_push(_UpperCAmelCase  ,      names_to_config[model_name]  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
	return config, expected_shape


if __name__ == "__main__":
		_lowerCamelCase  :			Tuple        =       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: resnet18,26,34,50,101,152. 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(
		    '--push_to_hub',
		    default=True,
		    type=bool,
		    required=False,
		    help='If True, push model and image processor to the hub.',
		)

		_lowerCamelCase  :			Union[str, Any]        =       parser.parse_args()
		_lowerCamelCase  :			Path        =       args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












"""simple docstring"""

import random

from .binary_exp_mod import bin_exp_mod

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase=1000	):




	"""simple docstring"""






	if n < 2:
		return False

	if n % 2 == 0:
		return n == 2

	# this means n is odd
	A_  :				List[Any]	   =		n - 1
	A_  :				Dict	   =		0
	while d % 2 == 0:
		d /= 2
		exp += 1

	# n - 1=d*(2**exp)
	A_  :				Optional[Any]	   =		0
	while count < prec:
		A_  :				Optional[int]	   =		random.randint(2  ,      n - 1	)
		A_  :				Any	   =		bin_exp_mod(_UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase	)
		if b != 1:
			A_  :				Dict	   =		True
			for _ in range(_UpperCAmelCase	):
				if b == n - 1:
					A_  :				Optional[int]	   =		False
					break
				A_  :				Any	   =		b * b
				b %= n
			if flag:
				return False
			count += 1
	return True


if __name__ == "__main__":
		_lowerCamelCase  :			List[str]        =       abs(int(input('Enter bound : ').strip()))
		print('Here\'s the list of primes:')
		print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))





