


"""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()
_lowerCamelCase     =  logging.get_logger()


def __lowercase     (      lowerCamelCase_  :      int						,					lowerCamelCase_  :      str						,					lowerCamelCase_  :      LevitConfig						,					lowerCamelCase_  :      Path						,					lowerCamelCase_  :      bool = True  ):
				print(F'''Converting {name}...'''  )

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

								from_model.eval()
								SCREAMING_SNAKE_CASE__     							=				LevitForImageClassificationWithTeacher(lowerCamelCase_  ).eval()
								SCREAMING_SNAKE_CASE__     							=				OrderedDict()

								SCREAMING_SNAKE_CASE__     							=				from_model.state_dict()
								SCREAMING_SNAKE_CASE__     							=				list(from_model.state_dict().keys()  )
								SCREAMING_SNAKE_CASE__     							=				list(our_model.state_dict().keys()  )
								print(len(lowerCamelCase_  )						,					len(lowerCamelCase_  )  )
								for i in range(len(lowerCamelCase_  )  ):
												SCREAMING_SNAKE_CASE__     							=				weights[og_keys[i]]
								our_model.load_state_dict(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				torch.randn((2, 3, 224, 224)  )
								SCREAMING_SNAKE_CASE__     							=				from_model(lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				our_model(lowerCamelCase_  ).logits

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

				SCREAMING_SNAKE_CASE__     							=				name
				print(lowerCamelCase_  )

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

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


def __lowercase     (      lowerCamelCase_  :      Path						,					lowerCamelCase_  :      str = None						,					lowerCamelCase_  :      bool = True  ):
				SCREAMING_SNAKE_CASE__     							=				"imagenet-1k-id2label.json"
				SCREAMING_SNAKE_CASE__     							=				1000
				SCREAMING_SNAKE_CASE__     							=				(1, num_labels)

				SCREAMING_SNAKE_CASE__     							=				"huggingface/label-files"
				SCREAMING_SNAKE_CASE__     							=				num_labels
				SCREAMING_SNAKE_CASE__     							=				json.load(open(hf_hub_download(lowerCamelCase_						,					lowerCamelCase_						,					repo_type="dataset"  )						,					"r"  )  )
				SCREAMING_SNAKE_CASE__     							=				{int(lowerCamelCase_  ): v for k, v in idalabel.items()}

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

				SCREAMING_SNAKE_CASE__     							=				partial(lowerCamelCase_						,					num_labels=lowerCamelCase_						,					idalabel=lowerCamelCase_						,					labelaid=lowerCamelCase_  )

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

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


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

	_lowerCamelCase     =  parser.parse_args()
	_lowerCamelCase     =  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"""






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 			lowerCamelCase_		(			lowercase       ,  lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					1



				@register_to_config
				def __init__(						self       ,		UpperCAmelCase__=2000       ,		UpperCAmelCase__=0.1       ,		UpperCAmelCase__=20       ,		UpperCAmelCase__=1e-3     ):
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None
								SCREAMING_SNAKE_CASE__     							=				None



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__     							=				torch.linspace(1       ,		self.config.sampling_eps       ,		UpperCAmelCase__       ,		device=UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__=None     ):
								if self.timesteps is None:
												raise ValueError(
												    "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"     )

								# TODO(Patrick) better comments + non-PyTorch
								# postprocess model score
								SCREAMING_SNAKE_CASE__     							=				(
								    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
								)
								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff     )     )
								SCREAMING_SNAKE_CASE__     							=				std.flatten()
								while len(std.shape     ) < len(score.shape     ):
												SCREAMING_SNAKE_CASE__     							=				std.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-score / std

								# compute
								SCREAMING_SNAKE_CASE__     							=				-1.0 / len(self.timesteps     )

								SCREAMING_SNAKE_CASE__     							=				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
								SCREAMING_SNAKE_CASE__     							=				beta_t.flatten()
								while len(beta_t.shape     ) < len(x.shape     ):
												SCREAMING_SNAKE_CASE__     							=				beta_t.unsqueeze(-1     )
								SCREAMING_SNAKE_CASE__     							=				-0.5 * beta_t * x

								SCREAMING_SNAKE_CASE__     							=				torch.sqrt(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				drift - diffusion**2 * score
								SCREAMING_SNAKE_CASE__     							=				x + drift * dt

								# add noise
								SCREAMING_SNAKE_CASE__     							=				randn_tensor(x.shape       ,		layout=x.layout       ,		generator=UpperCAmelCase__       ,		device=x.device       ,		dtype=x.dtype     )
								SCREAMING_SNAKE_CASE__     							=				x_mean + diffusion * math.sqrt(-dt     ) * noise

								return x, x_mean






				def __len__(						self     ):
								return self.config.num_train_timesteps
