



'''simple docstring'''




import math
import unittest


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		assert isinstance(_lowerCamelCase ,   _lowerCamelCase      ) and (
		    number >= 0
		), "'number' must been an int and positive"

		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(_lowerCamelCase      ) + 1      ) ,   6      ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True






class 		_UpperCamelCase						(							unittest.TestCase					):


			'''simple docstring'''

			def UpperCamelCase__      (							self  :				str		):





					"""simple docstring"""






					self.assertTrue(is_prime(2		)		)
					self.assertTrue(is_prime(3		)		)
					self.assertTrue(is_prime(5		)		)
					self.assertTrue(is_prime(7		)		)
					self.assertTrue(is_prime(1_1		)		)
					self.assertTrue(is_prime(1_3		)		)
					self.assertTrue(is_prime(1_7		)		)
					self.assertTrue(is_prime(1_9		)		)
					self.assertTrue(is_prime(2_3		)		)
					self.assertTrue(is_prime(2_9		)		)



			def UpperCamelCase__      (							self  :				Optional[Any]		):





					"""simple docstring"""






					with self.assertRaises(lowerCAmelCase__		):
							is_prime(-1_9		)
					self.assertFalse(
					    is_prime(0		)							,  """Zero doesn't have any positive factors, primes must have exactly two."""							,  )
					self.assertFalse(
					    is_prime(1		)							,  """One only has 1 positive factor, primes must have exactly two."""							,  )
					self.assertFalse(is_prime(2 * 2		)		)
					self.assertFalse(is_prime(2 * 3		)		)
					self.assertFalse(is_prime(3 * 3		)		)
					self.assertFalse(is_prime(3 * 5		)		)
					self.assertFalse(is_prime(3 * 5 * 7		)		)


if __name__ == "__main__":
						unittest.main()




'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple      ):
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	SwinvaConfig()
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	swinva_name.split("""_"""      )

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name_split[1]
		if "to" in name_split[3]:
				__SCREAMING_SNAKE_CASE :	Dict					 =	int(name_split[3][-3:]      )
		else:
				__SCREAMING_SNAKE_CASE :	str					 =	int(name_split[3]      )
		if "to" in name_split[2]:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	int(name_split[2][-2:]      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	int(name_split[2][6:]      )

		if model_size == "tiny":
				__SCREAMING_SNAKE_CASE :	Dict					 =	96
				__SCREAMING_SNAKE_CASE :	List[str]					 =	(2, 2, 6, 2)
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(3, 6, 12, 24)
		elif model_size == "small":
				__SCREAMING_SNAKE_CASE :	List[str]					 =	96
				__SCREAMING_SNAKE_CASE :	int					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	int					 =	(3, 6, 12, 24)
		elif model_size == "base":
				__SCREAMING_SNAKE_CASE :	int					 =	1_28
				__SCREAMING_SNAKE_CASE :	str					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	(4, 8, 16, 32)
		else:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	1_92
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	(2, 2, 18, 2)
				__SCREAMING_SNAKE_CASE :	Dict					 =	(6, 12, 24, 48)

		if "to" in swinva_name:
				__SCREAMING_SNAKE_CASE :	int					 =	(12, 12, 12, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				__SCREAMING_SNAKE_CASE :	int					 =	2_18_41
				__SCREAMING_SNAKE_CASE :	str					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	"""imagenet-22k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	List[str]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	str					 =	{v: k for k, v in idalabel.items()}

		else:
				__SCREAMING_SNAKE_CASE :	str					 =	10_00
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""huggingface/label-files"""
				__SCREAMING_SNAKE_CASE :	Any					 =	"""imagenet-1k-id2label.json"""
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	json.load(open(hf_hub_download(_lowerCamelCase ,   _lowerCamelCase ,   repo_type="""dataset"""      ) ,   """r"""      )      )
				__SCREAMING_SNAKE_CASE :	int					 =	{int(_lowerCamelCase      ): v for k, v in idalabel.items()}
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	idalabel
				__SCREAMING_SNAKE_CASE :	Dict					 =	{v: k for k, v in idalabel.items()}

		__SCREAMING_SNAKE_CASE :	Any					 =	img_size
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	num_classes
		__SCREAMING_SNAKE_CASE :	int					 =	embed_dim
		__SCREAMING_SNAKE_CASE :	Dict					 =	depths
		__SCREAMING_SNAKE_CASE :	str					 =	num_heads
		__SCREAMING_SNAKE_CASE :	int					 =	window_size

		return config


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if "patch_embed.proj" in name:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	name.replace("""patch_embed.proj""" ,   """embeddings.patch_embeddings.projection"""      )
		if "patch_embed.norm" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""patch_embed.norm""" ,   """embeddings.norm"""      )
		if "layers" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""encoder.""" + name
		if "attn.proj" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""attn.proj""" ,   """attention.output.dense"""      )
		if "attn" in name:
				__SCREAMING_SNAKE_CASE :	Any					 =	name.replace("""attn""" ,   """attention.self"""      )
		if "norm1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""norm1""" ,   """layernorm_before"""      )
		if "norm2" in name:
				__SCREAMING_SNAKE_CASE :	Dict					 =	name.replace("""norm2""" ,   """layernorm_after"""      )
		if "mlp.fc1" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""mlp.fc1""" ,   """intermediate.dense"""      )
		if "mlp.fc2" in name:
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	name.replace("""mlp.fc2""" ,   """output.dense"""      )
		if "q_bias" in name:
				__SCREAMING_SNAKE_CASE :	Tuple					 =	name.replace("""q_bias""" ,   """query.bias"""      )
		if "k_bias" in name:
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	name.replace("""k_bias""" ,   """key.bias"""      )
		if "v_bias" in name:
				__SCREAMING_SNAKE_CASE :	List[str]					 =	name.replace("""v_bias""" ,   """value.bias"""      )
		if "cpb_mlp" in name:
				__SCREAMING_SNAKE_CASE :	str					 =	name.replace("""cpb_mlp""" ,   """continuous_position_bias_mlp"""      )
		if name == "norm.weight":
				__SCREAMING_SNAKE_CASE :	Tuple					 =	"""layernorm.weight"""
		if name == "norm.bias":
				__SCREAMING_SNAKE_CASE :	Optional[int]					 =	"""layernorm.bias"""

		if "head" in name:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	name.replace("""head""" ,   """classifier"""      )
		else:
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	"""swinv2.""" + name

		return name


def        lowerCAmelCase_     (			_lowerCamelCase:     int ,   _lowerCamelCase:     Optional[Any]      ):
		for key in orig_state_dict.copy().keys():
				__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	orig_state_dict.pop(_lowerCamelCase      )

				if "mask" in key:
						continue
				elif "qkv" in key:
						__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	key.split("""."""      )
						__SCREAMING_SNAKE_CASE :	List[str]					 =	int(key_split[1]      )
						__SCREAMING_SNAKE_CASE :	Dict					 =	int(key_split[3]      )
						__SCREAMING_SNAKE_CASE :	str					 =	model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								__SCREAMING_SNAKE_CASE :	Optional[int]					 =	val[:dim, :]
								__SCREAMING_SNAKE_CASE :	str					 =	val[dim : dim * 2, :]
								__SCREAMING_SNAKE_CASE :	Dict					 =	val[-dim:, :]
						else:
								__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val[:dim]
								__SCREAMING_SNAKE_CASE :	int					 =	val[
								    dim : dim * 2
								]
								__SCREAMING_SNAKE_CASE :	int					 =	val[-dim:]
				else:
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	val

		return orig_state_dict


def        lowerCAmelCase_     (			_lowerCamelCase:     Tuple ,   _lowerCamelCase:     int      ):
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	timm.create_model(_lowerCamelCase ,   pretrained=_lowerCamelCase      )
		timm_model.eval()

		__SCREAMING_SNAKE_CASE :	int					 =	get_swinva_config(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	SwinvaForImageClassification(_lowerCamelCase      )
		model.eval()

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	convert_state_dict(timm_model.state_dict() ,   _lowerCamelCase      )
		model.load_state_dict(_lowerCamelCase      )

		__SCREAMING_SNAKE_CASE :	int					 =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_""" ,   """-"""      )      )      )
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	Image.open(requests.get(_lowerCamelCase ,   stream=_lowerCamelCase      ).raw      )
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	image_processor(images=_lowerCamelCase ,   return_tensors="""pt"""      )

		__SCREAMING_SNAKE_CASE :	int					 =	timm_model(inputs["""pixel_values"""]      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model(**_lowerCamelCase      ).logits

		assert torch.allclose(_lowerCamelCase ,   _lowerCamelCase ,   atol=1E-3      )

		print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}"      )
		model.save_pretrained(_lowerCamelCase      )

		print(F"Saving image processor to {pytorch_dump_folder_path}"      )
		image_processor.save_pretrained(_lowerCamelCase      )

		model.push_to_hub(
		    repo_path_or_name=Path(_lowerCamelCase ,   _lowerCamelCase      ) ,   organization="""nandwalritik""" ,   commit_message="""Add model""" ,   )


if __name__ == "__main__":
						UpperCamelCase__ :			Union[str, Any]												=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--swinv2_name''',
						    default='''swinv2_tiny_patch4_window8_256''',
						    type=str,
						    help='''Name of the Swinv2 timm model you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
						)

						UpperCamelCase__ :			Optional[int]												=				parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)