
"""simple docstring"""




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__      :    int    							= logging.get_logger()

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case = True						):
							print(f'''Converting {name}...'''						)

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE__						:       Dict           =   timm.create_model("""levit_128s"""   ,pretrained=_snake_case						)
																					else:
																												SCREAMING_SNAKE_CASE__						:       List[Any]           =   timm.create_model("""levit_128"""   ,pretrained=_snake_case						)
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE__						:       Dict           =   timm.create_model("""levit_192"""   ,pretrained=_snake_case						)
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE__						:       Dict           =   timm.create_model("""levit_256"""   ,pretrained=_snake_case						)
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE__						:       List[str]           =   timm.create_model("""levit_384"""   ,pretrained=_snake_case						)

														from_model.eval()
														SCREAMING_SNAKE_CASE__						:       List[str]           =   LevitForImageClassificationWithTeacher(_snake_case						).eval()
														SCREAMING_SNAKE_CASE__						:       Any           =   OrderedDict()

														SCREAMING_SNAKE_CASE__						:       str           =   from_model.state_dict()
														SCREAMING_SNAKE_CASE__						:       Dict           =   list(from_model.state_dict().keys()						)
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   list(our_model.state_dict().keys()						)
														print(len(_snake_case						)   ,len(_snake_case						)						)
														for i in range(len(_snake_case						)						):
																					SCREAMING_SNAKE_CASE__						:       Dict           =   weights[og_keys[i]]
														our_model.load_state_dict(_snake_case						)

														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   torch.randn((2, 3, 224, 224)						)
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   from_model(_snake_case						)
														SCREAMING_SNAKE_CASE__						:       Tuple           =   our_model(_snake_case						).logits

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

							SCREAMING_SNAKE_CASE__						:       Tuple           =   name
							print(_snake_case						)

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name						)
														SCREAMING_SNAKE_CASE__						:       str           =   LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name						)

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

def lowercase_  (   _snake_case   ,_snake_case = None   ,_snake_case = True						):
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   """imagenet-1k-id2label.json"""
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   1_000
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   (1, num_labels)

							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   """huggingface/label-files"""
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   num_labels
							SCREAMING_SNAKE_CASE__						:       Any           =   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[int]           =   idalabel
							SCREAMING_SNAKE_CASE__						:       Any           =   {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE__						:       Any           =   partial(_snake_case   ,num_labels=_snake_case   ,idalabel=_snake_case   ,labelaid=_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Tuple           =   {
							    """levit-128S""": 128,
							    """levit-128""": 128,
							    """levit-192""": 192,
							    """levit-256""": 256,
							    """levit-384""": 384,
							}

							SCREAMING_SNAKE_CASE__						:       int           =   {
							    """levit-128S""": ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]   ,num_attention_heads=[4, 6, 8]   ,depths=[2, 3, 4]   ,key_dim=[16, 16, 16]   ,drop_path_rate=0   ,),
							    """levit-128""": ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]   ,num_attention_heads=[4, 8, 12]   ,depths=[4, 4, 4]   ,key_dim=[16, 16, 16]   ,drop_path_rate=0   ,),
							    """levit-192""": ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]   ,num_attention_heads=[3, 5, 6]   ,depths=[4, 4, 4]   ,key_dim=[32, 32, 32]   ,drop_path_rate=0   ,),
							    """levit-256""": ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]   ,num_attention_heads=[4, 6, 8]   ,depths=[4, 4, 4]   ,key_dim=[32, 32, 32]   ,drop_path_rate=0   ,),
							    """levit-384""": ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]   ,num_attention_heads=[6, 9, 12]   ,depths=[4, 4, 4]   ,key_dim=[32, 32, 32]   ,drop_path_rate=0.1   ,),
							}

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


if __name__ == "__main__":
					UpperCAmelCase__      :    str    							= 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 Levit* architecture,',
					)
					parser.add_argument(
					    '--pytorch_dump_folder_path',
					    default='levit-dump-folder/',
					    type=Path,
					    required=False,
					    help='Path to the output PyTorch model directory.',
					)
					parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
					parser.add_argument(
					    '--no-push_to_hub',
					    dest='push_to_hub',
					    action='store_false',
					    help='Do not push model and image processor to the hub',
					)

					UpperCAmelCase__      :    Union[str, Any]    							= parser.parse_args()
					UpperCAmelCase__      :    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 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()




