"""simple docstring"""






import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	Optional[Any]    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	int=sys.maxsize			):
									UpperCAmelCase__             =							'bilinear'
									UpperCAmelCase__             =							max_size
									UpperCAmelCase__             =							short_edge_length







					def __call__(				self   :	List[Any]    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							[]
									for img in imgs:
													UpperCAmelCase__      ,	UpperCAmelCase__             =							img.shape[:2]
													# later: provide list and randomly choose index for resize
													UpperCAmelCase__             =							np.random.randint(self.short_edge_length[0]    ,self.short_edge_length[1] + 1			)
													if size == 0:
																	return img
													UpperCAmelCase__             =							size * 1.0 / min(lowerCamelCase__    ,lowerCamelCase__			)
													if h < w:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							size, scale * w
													else:
																	UpperCAmelCase__      ,	UpperCAmelCase__             =							scale * h, size
													if max(lowerCamelCase__    ,lowerCamelCase__			) > self.max_size:
																	UpperCAmelCase__             =							self.max_size * 1.0 / max(lowerCamelCase__    ,lowerCamelCase__			)
																	UpperCAmelCase__             =							newh * scale
																	UpperCAmelCase__             =							neww * scale
													UpperCAmelCase__             =							int(neww + 0.5			)
													UpperCAmelCase__             =							int(newh + 0.5			)

													if img.dtype == np.uinta:
																	UpperCAmelCase__             =							Image.fromarray(lowerCamelCase__			)
																	UpperCAmelCase__             =							pil_image.resize((neww, newh)    ,PILImageResampling.BILINEAR			)
																	UpperCAmelCase__             =							np.asarray(lowerCamelCase__			)
													else:
																	UpperCAmelCase__             =							img.permute(2    ,0    ,1			).unsqueeze(0			)  # 3, 0, 1)  # hw(c) -> nchw
																	UpperCAmelCase__             =							nn.functional.interpolate(
																	    lowerCamelCase__    ,(newh, neww)    ,mode=self.interp_method    ,align_corners=lowerCamelCase__			).squeeze(0			)
													img_augs.append(lowerCamelCase__			)

									return img_augs






class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	List[str]    ,lowerCamelCase__   :	Tuple			):
									UpperCAmelCase__             =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]    ,cfg.INPUT.MAX_SIZE_TEST			)
									UpperCAmelCase__             =							cfg.INPUT.FORMAT
									UpperCAmelCase__             =							cfg.SIZE_DIVISIBILITY
									UpperCAmelCase__             =							cfg.PAD_VALUE
									UpperCAmelCase__             =							cfg.INPUT.MAX_SIZE_TEST
									UpperCAmelCase__             =							cfg.MODEL.DEVICE
									UpperCAmelCase__             =							torch.tensor(cfg.MODEL.PIXEL_STD			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)    ,1    ,1			)
									UpperCAmelCase__             =							torch.tensor(cfg.MODEL.PIXEL_MEAN			).to(self.device			).view(len(cfg.MODEL.PIXEL_STD			)    ,1    ,1			)
									UpperCAmelCase__             =							lambda lowerCamelCase__			: (x - self.pixel_mean) / self.pixel_std







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							tuple(max(lowerCamelCase__			) for s in zip(*[img.shape for img in images]			)			)
									UpperCAmelCase__             =							[im.shape[-2:] for im in images]
									UpperCAmelCase__             =							[
									    nn.functional.pad(
									        lowerCamelCase__    ,[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]    ,value=self.pad_value    ,)
									    for size, im in zip(lowerCamelCase__    ,lowerCamelCase__			)
									]

									return torch.stack(lowerCamelCase__			), torch.tensor(lowerCamelCase__			)







					def __call__(				self   :	Dict    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	str=False			):
									with torch.no_grad():
													if not isinstance(lowerCamelCase__    ,lowerCamelCase__			):
																	UpperCAmelCase__             =							[images]
													if single_image:
																	assert len(lowerCamelCase__			) == 1
													for i in range(len(lowerCamelCase__			)			):
																	if isinstance(images[i]    ,torch.Tensor			):
																					images.insert(lowerCamelCase__    ,images.pop(lowerCamelCase__			).to(self.device			).float()			)
																	elif not isinstance(images[i]    ,torch.Tensor			):
																					images.insert(
																					    lowerCamelCase__    ,torch.as_tensor(img_tensorize(images.pop(lowerCamelCase__			)    ,input_format=self.input_format			)			)
																					    .to(self.device			)
																					    .float()    ,)
            # resize smallest edge
													UpperCAmelCase__             =							torch.tensor([im.shape[:2] for im in images]			)
													UpperCAmelCase__             =							self.aug(lowerCamelCase__			)
													# transpose images and convert to torch tensors
													# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
													# now normalize before pad to avoid useless arithmetic
													UpperCAmelCase__             =							[self.normalizer(lowerCamelCase__			) for x in images]
													# now pad them to do the following operations
													UpperCAmelCase__      ,	UpperCAmelCase__             =							self.pad(lowerCamelCase__			)
													# Normalize

													if self.size_divisibility > 0:
																	raise NotImplementedError()
													# pad
													UpperCAmelCase__             =							torch.true_divide(lowerCamelCase__    ,lowerCamelCase__			)
													if single_image:
																	return images[0], sizes[0], scales_yx[0]
													else:
																	return images, sizes, scales_yx






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				boxes[:, 0::2] *= scale_yx[:, 1]
				boxes[:, 1::2] *= scale_yx[:, 0]
				return boxes







def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				assert torch.isfinite(lowerCamelCase       ).all(), "Box tensor contains infinite or NaN!"
				UpperCAmelCase__      ,	UpperCAmelCase__             =							box_size
				tensor[:, 0].clamp_(min=0					,			max=lowerCamelCase       )
				tensor[:, 1].clamp_(min=0					,			max=lowerCamelCase       )
				tensor[:, 2].clamp_(min=0					,			max=lowerCamelCase       )
				tensor[:, 3].clamp_(min=0					,			max=lowerCamelCase       )








"""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      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

		with torch.no_grad():
				if hidden_sizes == 1_28:
						if name[-1] == "S":
								lowercase__   :    str				=			timm.create_model('''levit_128s'''						,     pretrained=__lowerCamelCase	)
						else:
								lowercase__   :    Tuple				=			timm.create_model('''levit_128'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 1_92:
						lowercase__   :    Union[str, Any]				=			timm.create_model('''levit_192'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 2_56:
						lowercase__   :    str				=			timm.create_model('''levit_256'''						,     pretrained=__lowerCamelCase	)
				if hidden_sizes == 3_84:
						lowercase__   :    str				=			timm.create_model('''levit_384'''						,     pretrained=__lowerCamelCase	)

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

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

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

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

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

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

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

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

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

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

		lowercase__   :    Tuple				=			{
		    '''levit-128S''': 1_28,
		    '''levit-128''': 1_28,
		    '''levit-192''': 1_92,
		    '''levit-256''': 2_56,
		    '''levit-384''': 3_84,
		}

		lowercase__   :    Any				=			{
		    '''levit-128S''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 6, 8]						,     depths=[2, 3, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-128''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_28, 2_56, 3_84]						,     num_attention_heads=[4, 8, 12]						,     depths=[4, 4, 4]						,     key_dim=[16, 16, 16]						,     drop_path_rate=0						,     ),
		    '''levit-192''': ImageNetPreTrainedConfig(
		        hidden_sizes=[1_92, 2_88, 3_84]						,     num_attention_heads=[3, 5, 6]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-256''': ImageNetPreTrainedConfig(
		        hidden_sizes=[2_56, 3_84, 5_12]						,     num_attention_heads=[4, 6, 8]						,     depths=[4, 4, 4]						,     key_dim=[32, 32, 32]						,     drop_path_rate=0						,     ),
		    '''levit-384''': ImageNetPreTrainedConfig(
		        hidden_sizes=[3_84, 5_12, 7_68]						,     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)
