






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      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       str,						UpperCAmelCase	:       LevitConfig,						UpperCAmelCase	:       Path,						UpperCAmelCase	:       bool = True   ):
				print(f'Converting {name}...'   )

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

								from_model.eval()
								A	 =      LevitForImageClassificationWithTeacher(UpperCAmelCase   ).eval()
								A	 =      OrderedDict()

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

								A	 =      torch.randn((2, 3, 2_24, 2_24)   )
								A	 =      from_model(UpperCAmelCase   )
								A	 =      our_model(UpperCAmelCase   ).logits

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

				A	 =      name
				print(UpperCAmelCase   )

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

								print(f'Pushed {checkpoint_name}'   )







def      snake_case		(							UpperCAmelCase	:       Path,						UpperCAmelCase	:       str = None,						UpperCAmelCase	:       bool = True   ):
				A	 =      'imagenet-1k-id2label.json'
				A	 =      10_00
				A	 =      (1, num_labels)

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

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

				A	 =      partial(UpperCAmelCase,						num_labels=UpperCAmelCase,						idalabel=UpperCAmelCase,						labelaid=UpperCAmelCase   )

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

				A	 =      {
				    '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],						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__":
					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)













from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache





