



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()
_lowercase     							=						logging.get_logger()






def      _A				(UpperCamelCase		:      int      ,			UpperCamelCase		:      str      ,			UpperCamelCase		:      LevitConfig      ,			UpperCamelCase		:      Path      ,			UpperCamelCase		:      bool = True )  ->str:



			'''simple docstring'''







			print(f"Converting {name}..." )

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

						from_model.eval()
						lowerCamelCase__ :      int								=		LevitForImageClassificationWithTeacher(UpperCamelCase ).eval()
						lowerCamelCase__ :      str								=		OrderedDict()

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

						lowerCamelCase__ :      int								=		torch.randn((2, 3, 224, 224) )
						lowerCamelCase__ :      Tuple								=		from_model(UpperCamelCase )
						lowerCamelCase__ :      str								=		our_model(UpperCamelCase ).logits

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

			lowerCamelCase__ :      int								=		name
			print(UpperCamelCase )

			if push_to_hub:
						our_model.save_pretrained(save_directory / checkpoint_name )
						lowerCamelCase__ :      Optional[Any]								=		LevitImageProcessor()
						image_processor.save_pretrained(save_directory / checkpoint_name )

						print(f"Pushed {checkpoint_name}" )






def      _A				(UpperCamelCase		:      Path      ,			UpperCamelCase		:      str = None      ,			UpperCamelCase		:      bool = True )  ->List[str]:



			'''simple docstring'''







			lowerCamelCase__ :      List[Any]								=		"""imagenet-1k-id2label.json"""
			lowerCamelCase__ :      str								=		1000
			lowerCamelCase__ :      int								=		(1, num_labels)

			lowerCamelCase__ :      Tuple								=		"""huggingface/label-files"""
			lowerCamelCase__ :      Dict								=		num_labels
			lowerCamelCase__ :      Dict								=		json.load(open(hf_hub_download(UpperCamelCase      ,			UpperCamelCase      ,			repo_type="""dataset""" )      ,			"""r""" ) )
			lowerCamelCase__ :      List[Any]								=		{int(UpperCamelCase ): v for k, v in idalabel.items()}

			lowerCamelCase__ :      Union[str, Any]								=		idalabel
			lowerCamelCase__ :      Any								=		{v: k for k, v in idalabel.items()}

			lowerCamelCase__ :      Union[str, Any]								=		partial(UpperCamelCase      ,			num_labels=UpperCamelCase      ,			idalabel=UpperCamelCase      ,			labelaid=UpperCamelCase )

			lowerCamelCase__ :      int								=		{
			    """levit-128S""": 128,
			    """levit-128""": 128,
			    """levit-192""": 192,
			    """levit-256""": 256,
			    """levit-384""": 384,
			}

			lowerCamelCase__ :      Tuple								=		{
			    """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]      ,			UpperCamelCase      ,			names_to_config[model_name]      ,			UpperCamelCase      ,			UpperCamelCase )
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(names_to_hidden_sizes[model_name]      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase      ,			UpperCamelCase )
			return config, expected_shape


if __name__ == "__main__":
				_lowercase     							=						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''',
				)

				_lowercase     							=						parser.parse_args()
				_lowercase     							=						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)








from math import factorial, pi






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_sin() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_sin() requires a positive int for accuracy""" )

			lowerCamelCase__ :      Dict								=		float(UpperCamelCase )
			lowerCamelCase__ :      int								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum(
			    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(UpperCamelCase ) )






def      _A				(UpperCamelCase		:      float      ,			UpperCamelCase		:      int = 30 )  ->float:



			'''simple docstring'''







			if not isinstance(UpperCamelCase      ,			(int, float) ):
						raise ValueError("""maclaurin_cos() requires either an int or float for theta""" )

			if not isinstance(UpperCamelCase      ,			UpperCamelCase ) or accuracy <= 0:
						raise ValueError("""maclaurin_cos() requires a positive int for accuracy""" )

			lowerCamelCase__ :      int								=		float(UpperCamelCase )
			lowerCamelCase__ :      Optional[int]								=		theta // (2 * pi)
			theta -= 2 * div * pi
			return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(UpperCamelCase ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				print(maclaurin_sin(10))
				print(maclaurin_sin(-10))
				print(maclaurin_sin(10, 15))
				print(maclaurin_sin(-10, 15))

				print(maclaurin_cos(5))
				print(maclaurin_cos(-5))
				print(maclaurin_cos(10, 15))
				print(maclaurin_cos(-10, 15))



