



import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					str:


				"""simple docstring"""

				snake_case__	:      int     		=	SwinConfig()
				snake_case__	:      List[str]     		=	swin_name.split('''_'''      )

				snake_case__	:      Union[str, Any]     		=	name_split[1]
				snake_case__	:      List[str]     		=	int(name_split[4]      )
				snake_case__	:      List[str]     		=	int(name_split[3][-1]      )

				if model_size == "tiny":
								snake_case__	:      Tuple     		=	96
								snake_case__	:      Optional[int]     		=	(2, 2, 6, 2)
								snake_case__	:      Any     		=	(3, 6, 12, 24)
				elif model_size == "small":
								snake_case__	:      List[Any]     		=	96
								snake_case__	:      List[str]     		=	(2, 2, 18, 2)
								snake_case__	:      Optional[int]     		=	(3, 6, 12, 24)
				elif model_size == "base":
								snake_case__	:      Any     		=	128
								snake_case__	:      str     		=	(2, 2, 18, 2)
								snake_case__	:      int     		=	(4, 8, 16, 32)
				else:
								snake_case__	:      Dict     		=	192
								snake_case__	:      Union[str, Any]     		=	(2, 2, 18, 2)
								snake_case__	:      str     		=	(6, 12, 24, 48)

				if "in22k" in swin_name:
								snake_case__	:      int     		=	21841
				else:
								snake_case__	:      Dict     		=	1000
								snake_case__	:      List[Any]     		=	'''huggingface/label-files'''
								snake_case__	:      List[str]     		=	'''imagenet-1k-id2label.json'''
								snake_case__	:      List[Any]     		=	json.load(open(hf_hub_download(__lowerCAmelCase  ,  __lowerCAmelCase  ,  repo_type='''dataset'''      )  ,  '''r'''      )      )
								snake_case__	:      Dict     		=	{int(__lowerCAmelCase      ): v for k, v in idalabel.items()}
								snake_case__	:      Tuple     		=	idalabel
								snake_case__	:      Union[str, Any]     		=	{v: k for k, v in idalabel.items()}

				snake_case__	:      List[Any]     		=	img_size
				snake_case__	:      Any     		=	num_classes
				snake_case__	:      Optional[Any]     		=	embed_dim
				snake_case__	:      Dict     		=	depths
				snake_case__	:      List[Any]     		=	num_heads
				snake_case__	:      List[str]     		=	window_size

				return config



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				if "patch_embed.proj" in name:
								snake_case__	:      Optional[Any]     		=	name.replace('''patch_embed.proj'''  ,  '''embeddings.patch_embeddings.projection'''      )
				if "patch_embed.norm" in name:
								snake_case__	:      List[Any]     		=	name.replace('''patch_embed.norm'''  ,  '''embeddings.norm'''      )
				if "layers" in name:
								snake_case__	:      Dict     		=	'''encoder.''' + name
				if "attn.proj" in name:
								snake_case__	:      Union[str, Any]     		=	name.replace('''attn.proj'''  ,  '''attention.output.dense'''      )
				if "attn" in name:
								snake_case__	:      Optional[int]     		=	name.replace('''attn'''  ,  '''attention.self'''      )
				if "norm1" in name:
								snake_case__	:      Optional[int]     		=	name.replace('''norm1'''  ,  '''layernorm_before'''      )
				if "norm2" in name:
								snake_case__	:      Union[str, Any]     		=	name.replace('''norm2'''  ,  '''layernorm_after'''      )
				if "mlp.fc1" in name:
								snake_case__	:      List[Any]     		=	name.replace('''mlp.fc1'''  ,  '''intermediate.dense'''      )
				if "mlp.fc2" in name:
								snake_case__	:      List[str]     		=	name.replace('''mlp.fc2'''  ,  '''output.dense'''      )

				if name == "norm.weight":
								snake_case__	:      int     		=	'''layernorm.weight'''
				if name == "norm.bias":
								snake_case__	:      Union[str, Any]     		=	'''layernorm.bias'''

				if "head" in name:
								snake_case__	:      Any     		=	name.replace('''head'''  ,  '''classifier'''      )
				else:
								snake_case__	:      Optional[Any]     		=	'''swin.''' + name

				return name



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Optional[Any]:


				"""simple docstring"""

				for key in orig_state_dict.copy().keys():
								snake_case__	:      Tuple     		=	orig_state_dict.pop(__lowerCAmelCase      )

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

												if "weight" in key:
																snake_case__	:      Optional[int]     		=	val[:dim, :]
																snake_case__	:      List[Any]     		=	val[
																    dim : dim * 2, :
																]
																snake_case__	:      Union[str, Any]     		=	val[-dim:, :]
												else:
																snake_case__	:      List[Any]     		=	val[
																    :dim
																]
																snake_case__	:      Optional[int]     		=	val[
																    dim : dim * 2
																]
																snake_case__	:      Tuple     		=	val[
																    -dim:
																]
								else:
												snake_case__	:      List[str]     		=	val

				return orig_state_dict



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase      )   ->					Any:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	timm.create_model(__lowerCAmelCase  ,  pretrained=__lowerCAmelCase      )
				timm_model.eval()

				snake_case__	:      Any     		=	get_swin_config(__lowerCAmelCase      )
				snake_case__	:      str     		=	SwinForImageClassification(__lowerCAmelCase      )
				model.eval()

				snake_case__	:      Any     		=	convert_state_dict(timm_model.state_dict()  ,  __lowerCAmelCase      )
				model.load_state_dict(__lowerCAmelCase      )

				snake_case__	:      Any     		=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''

				snake_case__	:      Optional[int]     		=	AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''  ,  '''-'''      )      )      )
				snake_case__	:      int     		=	Image.open(requests.get(__lowerCAmelCase  ,  stream=__lowerCAmelCase      ).raw      )
				snake_case__	:      Tuple     		=	image_processor(images=__lowerCAmelCase  ,  return_tensors='''pt'''      )

				snake_case__	:      int     		=	timm_model(inputs['''pixel_values''']      )
				snake_case__	:      List[str]     		=	model(**__lowerCAmelCase      ).logits

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

				print(f"""Saving model {swin_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      )


if __name__ == "__main__":
				A__								=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--swin_name''',
				    default='''swin_tiny_patch4_window7_224''',
				    type=str,
				    help='''Name of the Swin 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.'''
				)

				A__								=						parser.parse_args()
				convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)






import random

from .binary_exp_mod import bin_exp_mod



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase=1000      )   ->					str:


				"""simple docstring"""

				if n < 2:
								return False

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

				# this means n is odd
				snake_case__	:      Tuple     		=	n - 1
				snake_case__	:      Tuple     		=	0
				while d % 2 == 0:
								d /= 2
								exp += 1

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


if __name__ == "__main__":
				A__								=						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)))

