


'''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()
__A	=logging.get_logger()







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = True					):
			print(f'''Converting {name}...'''					)

			with torch.no_grad():
						if hidden_sizes == 1_2_8:
									if name[-1] == "S":
												UpperCAmelCase__  :					Any        =     timm.create_model("""levit_128s""" ,      pretrained=UpperCamelCase__					)
									else:
												UpperCAmelCase__  :					Tuple        =     timm.create_model("""levit_128""" ,      pretrained=UpperCamelCase__					)
						if hidden_sizes == 1_9_2:
									UpperCAmelCase__  :					Optional[Any]        =     timm.create_model("""levit_192""" ,      pretrained=UpperCamelCase__					)
						if hidden_sizes == 2_5_6:
									UpperCAmelCase__  :					int        =     timm.create_model("""levit_256""" ,      pretrained=UpperCamelCase__					)
						if hidden_sizes == 3_8_4:
									UpperCAmelCase__  :					Optional[int]        =     timm.create_model("""levit_384""" ,      pretrained=UpperCamelCase__					)

						from_model.eval()
						UpperCAmelCase__  :					Optional[int]        =     LevitForImageClassificationWithTeacher(UpperCamelCase__					).eval()
						UpperCAmelCase__  :					Optional[Any]        =     OrderedDict()

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

						UpperCAmelCase__  :					Tuple        =     torch.randn((2, 3, 2_2_4, 2_2_4)					)
						UpperCAmelCase__  :					List[str]        =     from_model(UpperCamelCase__					)
						UpperCAmelCase__  :					int        =     our_model(UpperCamelCase__					).logits

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

			UpperCAmelCase__  :					Tuple        =     name
			print(UpperCamelCase__					)

			if push_to_hub:
						our_model.save_pretrained(save_directory / checkpoint_name					)
						UpperCAmelCase__  :					Any        =     LevitImageProcessor()
						image_processor.save_pretrained(save_directory / checkpoint_name					)

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = None ,      UpperCamelCase__ = True					):
			UpperCAmelCase__  :					Union[str, Any]        =     """imagenet-1k-id2label.json"""
			UpperCAmelCase__  :					Union[str, Any]        =     1_0_0_0
			UpperCAmelCase__  :					str        =     (1, num_labels)

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

			UpperCAmelCase__  :					List[str]        =     idalabel
			UpperCAmelCase__  :					Tuple        =     {v: k for k, v in idalabel.items()}

			UpperCAmelCase__  :					str        =     partial(UpperCamelCase__ ,      num_labels=UpperCamelCase__ ,      idalabel=UpperCamelCase__ ,      labelaid=UpperCamelCase__					)

			UpperCAmelCase__  :					Tuple        =     {
			    """levit-128S""": 1_2_8,
			    """levit-128""": 1_2_8,
			    """levit-192""": 1_9_2,
			    """levit-256""": 2_5_6,
			    """levit-384""": 3_8_4,
			}

			UpperCAmelCase__  :					Optional[Any]        =     {
			    """levit-128S""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,      num_attention_heads=[4, 6, 8] ,      depths=[2, 3, 4] ,      key_dim=[1_6, 1_6, 1_6] ,      drop_path_rate=0 ,      ),
			    """levit-128""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,      num_attention_heads=[4, 8, 1_2] ,      depths=[4, 4, 4] ,      key_dim=[1_6, 1_6, 1_6] ,      drop_path_rate=0 ,      ),
			    """levit-192""": ImageNetPreTrainedConfig(
			        hidden_sizes=[1_9_2, 2_8_8, 3_8_4] ,      num_attention_heads=[3, 5, 6] ,      depths=[4, 4, 4] ,      key_dim=[3_2, 3_2, 3_2] ,      drop_path_rate=0 ,      ),
			    """levit-256""": ImageNetPreTrainedConfig(
			        hidden_sizes=[2_5_6, 3_8_4, 5_1_2] ,      num_attention_heads=[4, 6, 8] ,      depths=[4, 4, 4] ,      key_dim=[3_2, 3_2, 3_2] ,      drop_path_rate=0 ,      ),
			    """levit-384""": ImageNetPreTrainedConfig(
			        hidden_sizes=[3_8_4, 5_1_2, 7_6_8] ,      num_attention_heads=[6, 9, 1_2] ,      depths=[4, 4, 4] ,      key_dim=[3_2, 3_2, 3_2] ,      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__":
		__A	=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',
		)

		__A	=parser.parse_args()
		__A	=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'''



from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)


class 		_snake_case					(     a__	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							_lowerCamelCase=0):
								UpperCAmelCase__  :					Dict        =     1.0 if scale is None else scale
								UpperCAmelCase__  :					Dict        =     0.0 if loc is None else loc

								super().__init__(_lowerCamelCase							,							[AffineTransform(loc=self.loc							,							scale=self.scale							,							event_dim=_lowerCamelCase)])




					@property
					def 				snake_case__						(      self):
								return self.base_dist.mean * self.scale + self.loc




					@property
					def 				snake_case__						(      self):
								return self.base_dist.variance * self.scale**2




					@property
					def 				snake_case__						(      self):
								return self.variance.sqrt()


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							**_lowerCamelCase):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					int        =     args_dim
								UpperCAmelCase__  :					Optional[int]        =     nn.ModuleList([nn.Linear(_lowerCamelCase							,							_lowerCamelCase) for dim in args_dim.values()])
								UpperCAmelCase__  :					Optional[Any]        =     domain_map




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     [proj(_lowerCamelCase) for proj in self.proj]

								return self.domain_map(*_lowerCamelCase)


class 		_snake_case					(     nn.Module	):




					def __init__(      self							,							_lowerCamelCase):
								super().__init__()
								UpperCAmelCase__  :					Optional[int]        =     function




					def 				snake_case__						(      self							,							_lowerCamelCase							,							*_lowerCamelCase):
								return self.function(_lowerCamelCase							,							*_lowerCamelCase)


class 		_snake_case					:
					lowerCAmelCase      :type
					lowerCAmelCase      :int
					lowerCAmelCase      :Dict[str, int]




					def __init__(      self							,							_lowerCamelCase = 1):
								UpperCAmelCase__  :					Optional[Any]        =     dim
								UpperCAmelCase__  :					int        =     {k: dim * self.args_dim[k] for k in self.args_dim}




					def 				snake_case__						(      self							,							_lowerCamelCase):
								if self.dim == 1:
											return self.distribution_class(*_lowerCamelCase)
								else:
											return Independent(self.distribution_class(*_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							):
								UpperCAmelCase__  :					Dict        =     self._base_distribution(_lowerCamelCase)
								if loc is None and scale is None:
											return distr
								else:
											return AffineTransformed(_lowerCamelCase							,							loc=_lowerCamelCase							,							scale=_lowerCamelCase							,							event_dim=self.event_dim)




					@property
					def 				snake_case__						(      self):
								return () if self.dim == 1 else (self.dim,)




					@property
					def 				snake_case__						(      self):
								return len(self.event_shape)




					@property
					def 				snake_case__						(      self):
								return 0.0




					def 				snake_case__						(      self							,							_lowerCamelCase):
								return ParameterProjection(
								    in_features=_lowerCamelCase							,							args_dim=self.args_dim							,							domain_map=LambdaLayer(self.domain_map)							,							)




					def 				snake_case__						(      self							,							*_lowerCamelCase):
								raise NotImplementedError()




					@staticmethod
					def 				snake_case__						(      _lowerCamelCase):
								return (x + torch.sqrt(torch.square(_lowerCamelCase) + 4.0)) / 2.0


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"df": 1, "loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					StudentT




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								UpperCAmelCase__  :					Any        =     2.0 + cls.squareplus(_lowerCamelCase)
								return df.squeeze(-1), loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"loc": 1, "scale": 1}
					lowerCAmelCase      :type   =					Normal




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase).clamp_min(torch.finfo(scale.dtype).eps)
								return loc.squeeze(-1), scale.squeeze(-1)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Dict[str, int]   =					{"total_count": 1, "logits": 1}
					lowerCAmelCase      :type   =					NegativeBinomial




					@classmethod
					def 				snake_case__						(      cls							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					str        =     cls.squareplus(_lowerCamelCase)
								return total_count.squeeze(-1), logits.squeeze(-1)




					def 				snake_case__						(      self							,							_lowerCamelCase):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     distr_args
								if self.dim == 1:
											return self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)
								else:
											return Independent(self.distribution_class(total_count=_lowerCamelCase							,							logits=_lowerCamelCase)							,							1)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Any        =     distr_args

								if scale is not None:
											# See scaling property of Gamma.
											logits += scale.log()

								return self._base_distribution((total_count, logits))