



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		:				str      		=logging.get_logger()

def      lowerCAmelCase_					(  _lowercase    :       List[str]						,       _lowercase    :       List[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Dict						,       _lowercase    :       Union[str, Any] = True)							->	Optional[Any]:







			"""simple docstring"""
			print(F'''Converting {name}...''')

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

						from_model.eval()
						a__       :						List[str]							=  LevitForImageClassificationWithTeacher(_lowercase).eval()
						a__       :						Union[str, Any]							=  OrderedDict()

						a__       :						List[str]							=  from_model.state_dict()
						a__       :						Union[str, Any]							=  list(from_model.state_dict().keys())
						a__       :						Tuple							=  list(our_model.state_dict().keys())
						print(len(_lowercase)						,       len(_lowercase))
						for i in range(len(_lowercase)):
									a__       :						Optional[int]							=  weights[og_keys[i]]
						our_model.load_state_dict(_lowercase)

						a__       :						Tuple							=  torch.randn((2, 3, 224, 224))
						a__       :						Optional[Any]							=  from_model(_lowercase)
						a__       :						str							=  our_model(_lowercase).logits

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

			a__       :						List[str]							=  name
			print(_lowercase)

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

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

def      lowerCAmelCase_					(  _lowercase    :       Optional[Any]						,       _lowercase    :       int = None						,       _lowercase    :       Optional[Any] = True)							->	Any:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  """imagenet-1k-id2label.json"""
			a__       :						int							=  1000
			a__       :						Dict							=  (1, num_labels)

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

			a__       :						Dict							=  idalabel
			a__       :						Optional[int]							=  {v: k for k, v in idalabel.items()}

			a__       :						Dict							=  partial(_lowercase						,       num_labels=_lowercase						,       idalabel=_lowercase						,       labelaid=_lowercase)

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

			a__       :						List[Any]							=  {
			    """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]						,       _lowercase						,       names_to_config[model_name]						,       _lowercase						,       _lowercase)
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(names_to_hidden_sizes[model_name]						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase)
			return config, expected_shape


if __name__ == "__main__":
							_lowercase		:				Union[str, Any]      		=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		:				str      		=parser.parse_args()
							_lowercase		:				Path      		=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)









import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

class        snake_case__    (A__		):







						"""simple docstring"""




						def __init__(       self ,       *__lowercase ,       **__lowercase  ) ->				None:
									"""simple docstring"""




									warnings.warn(
									    """The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use MobileViTImageProcessor instead.""" ,       __lowercase ,       )
									super().__init__(*__lowercase ,       **__lowercase  )




