

"""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__   :					Any												=				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__		: str     =     timm.create_model("""levit_128"""     ,pretrained=_snake_case		)
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     timm.create_model("""levit_192"""     ,pretrained=_snake_case		)
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     timm.create_model("""levit_256"""     ,pretrained=_snake_case		)
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     timm.create_model("""levit_384"""     ,pretrained=_snake_case		)

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

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

														SCREAMING_SNAKE_CASE__		: List[str]     =     torch.randn((2, 3, 224, 224)		)
														SCREAMING_SNAKE_CASE__		: Dict     =     from_model(_snake_case		)
														SCREAMING_SNAKE_CASE__		: str     =     our_model(_snake_case		).logits

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

							SCREAMING_SNAKE_CASE__		: str     =     name
							print(_snake_case		)

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name		)
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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__		: str     =     """imagenet-1k-id2label.json"""
							SCREAMING_SNAKE_CASE__		: List[Any]     =     1_000
							SCREAMING_SNAKE_CASE__		: int     =     (1, num_labels)

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

							SCREAMING_SNAKE_CASE__		: str     =     idalabel
							SCREAMING_SNAKE_CASE__		: int     =     {v: k for k, v in idalabel.items()}

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

							SCREAMING_SNAKE_CASE__		: List[str]     =     {
							    """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__   :					Optional[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"""




UpperCAmelCase__   :					Dict												=				[
    (1_0_0_0, 'M'),
    (9_0_0, 'CM'),
    (5_0_0, 'D'),
    (4_0_0, 'CD'),
    (1_0_0, 'C'),
    (9_0, 'XC'),
    (5_0, 'L'),
    (4_0, 'XL'),
    (1_0, 'X'),
    (9, 'IX'),
    (5, 'V'),
    (4, 'IV'),
    (1, 'I'),
]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1_000}
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     0
							while place < len(_snake_case		):
														if (place + 1 < len(_snake_case		)) and (vals[roman[place]] < vals[roman[place + 1]]):
																					total += vals[roman[place + 1]] - vals[roman[place]]
																					place += 2
														else:
																					total += vals[roman[place]]
																					place += 1
							return total

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for arabic, roman in ROMAN:
														((SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__))		: List[Any]     =     divmod(_snake_case     ,_snake_case		)
														result.append(roman * factor		)
														if number == 0:
																					break
							return "".join(_snake_case		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




