
"""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 lowercase_  (   _snake_case						):
							SCREAMING_SNAKE_CASE__						:       str           =   SwinvaConfig()
							SCREAMING_SNAKE_CASE__						:       str           =   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__						:       List[Any]           =   int(name_split[3]						)
							if "to" in name_split[2]:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][-2:]						)
							else:
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   int(name_split[2][6:]						)

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

							if "to" in swinva_name:
														SCREAMING_SNAKE_CASE__						:       str           =   (12, 12, 12, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														SCREAMING_SNAKE_CASE__						:       Dict           =   21_841
														SCREAMING_SNAKE_CASE__						:       str           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       Any           =   """imagenet-22k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Any           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   {v: k for k, v in idalabel.items()}

							else:
														SCREAMING_SNAKE_CASE__						:       Dict           =   1_000
														SCREAMING_SNAKE_CASE__						:       Any           =   """huggingface/label-files"""
														SCREAMING_SNAKE_CASE__						:       int           =   """imagenet-1k-id2label.json"""
														SCREAMING_SNAKE_CASE__						:       str           =   json.load(open(hf_hub_download(_snake_case   ,_snake_case   ,repo_type="""dataset"""						)   ,"""r"""						)						)
														SCREAMING_SNAKE_CASE__						:       Optional[int]           =   {int(_snake_case						): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   idalabel
														SCREAMING_SNAKE_CASE__						:       List[str]           =   {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__						:       List[str]           =   img_size
							SCREAMING_SNAKE_CASE__						:       Dict           =   num_classes
							SCREAMING_SNAKE_CASE__						:       Tuple           =   embed_dim
							SCREAMING_SNAKE_CASE__						:       str           =   depths
							SCREAMING_SNAKE_CASE__						:       str           =   num_heads
							SCREAMING_SNAKE_CASE__						:       List[str]           =   window_size

							return config

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

							if "head" in name:
														SCREAMING_SNAKE_CASE__						:       Any           =   name.replace("""head"""   ,"""classifier"""						)
							else:
														SCREAMING_SNAKE_CASE__						:       str           =   """swinv2.""" + name

							return name

def lowercase_  (   _snake_case   ,_snake_case						):
							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:       Any           =   orig_state_dict.pop(_snake_case						)

														if "mask" in key:
																					continue
														elif "qkv" in key:
																					SCREAMING_SNAKE_CASE__						:       int           =   key.split("""."""						)
																					SCREAMING_SNAKE_CASE__						:       Any           =   int(key_split[1]						)
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(key_split[3]						)
																					SCREAMING_SNAKE_CASE__						:       Any           =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

							return orig_state_dict

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   timm.create_model(_snake_case   ,pretrained=_snake_case						)
							timm_model.eval()

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   get_swinva_config(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   SwinvaForImageClassification(_snake_case						)
							model.eval()

							SCREAMING_SNAKE_CASE__						:       str           =   convert_state_dict(timm_model.state_dict()   ,_snake_case						)
							model.load_state_dict(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       str           =   """http://images.cocodataset.org/val2017/000000039769.jpg"""

							SCREAMING_SNAKE_CASE__						:       Tuple           =   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   ,"""-"""						)						)						)
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   Image.open(requests.get(_snake_case   ,stream=_snake_case						).raw						)
							SCREAMING_SNAKE_CASE__						:       Dict           =   image_processor(images=_snake_case   ,return_tensors="""pt"""						)

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

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

							print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''						)
							model.save_pretrained(_snake_case						)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
							image_processor.save_pretrained(_snake_case						)

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


if __name__ == "__main__":
					UpperCAmelCase__      :    List[str]    							= 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__      :    int    							= parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






"""simple docstring"""




import math
import unittest

def lowercase_  (   _snake_case						):
							assert isinstance(_snake_case   ,_snake_case						) 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(_snake_case						) + 1						)   ,6						):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Dict:



											"""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(11    )    )
											self.assertTrue(is_prime(13    )    )
											self.assertTrue(is_prime(17    )    )
											self.assertTrue(is_prime(19    )    )
											self.assertTrue(is_prime(23    )    )
											self.assertTrue(is_prime(29    )    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											with self.assertRaises(SCREAMING_SNAKE_CASE__    ):
																		is_prime(-19    )
											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()




