"""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    :							Dict													=						logging.get_logger()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__ = True     ):





		"""simple docstring"""




		print(f"""Converting {name}..."""     )

		with torch.no_grad():
				if hidden_sizes == 128:
						if name[-1] == "S":
								A        =   timm.create_model("""levit_128s"""			,  pretrained=UpperCamelCase__     )
						else:
								A        =   timm.create_model("""levit_128"""			,  pretrained=UpperCamelCase__     )
				if hidden_sizes == 192:
						A        =   timm.create_model("""levit_192"""			,  pretrained=UpperCamelCase__     )
				if hidden_sizes == 256:
						A        =   timm.create_model("""levit_256"""			,  pretrained=UpperCamelCase__     )
				if hidden_sizes == 384:
						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, 224, 224)     )
				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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = None			,  lowerCamelCase__ = True     ):





		"""simple docstring"""




		A        =   """imagenet-1k-id2label.json"""
		A        =   1000
		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""": 128,
		    """levit-128""": 128,
		    """levit-192""": 192,
		    """levit-256""": 256,
		    """levit-384""": 384,
		}

		A        =   {
		    """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]			,  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    :							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",
			)

			__lowerCAmelCase    :							List[Any]													=						parser.parse_args()
			__lowerCAmelCase    :							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)





"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MobileBertConfig.from_json_file(lowerCamelCase__     )
		print(f"""Building PyTorch model from configuration: {config}"""     )
		lowerCAmelCase__        =   MobileBertForPreTraining(lowerCamelCase__     )
		# Load weights from tf checkpoint
		lowerCAmelCase__        =   load_tf_weights_in_mobilebert(lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     )
		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""     )
		torch.save(model.state_dict()			,  lowerCamelCase__     )


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
			)
			parser.add_argument(
			    "--mobilebert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained MobileBERT model. \n"
			        "This specifies the model architecture."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			__lowerCAmelCase    :							Optional[int]													=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)




