



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()
_snake_case		=  logging.get_logger()






def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = True  ):





							'''simple docstring'''




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

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												_lowerCAmelCase     :							List[str]    =    timm.create_model("levit_128s"				, pretrained=_lowerCamelCase  )
																					else:
																												_lowerCAmelCase     :							List[Any]    =    timm.create_model("levit_128"				, pretrained=_lowerCamelCase  )
														if hidden_sizes == 192:
																					_lowerCAmelCase     :							Any    =    timm.create_model("levit_192"				, pretrained=_lowerCamelCase  )
														if hidden_sizes == 256:
																					_lowerCAmelCase     :							Union[str, Any]    =    timm.create_model("levit_256"				, pretrained=_lowerCamelCase  )
														if hidden_sizes == 384:
																					_lowerCAmelCase     :							str    =    timm.create_model("levit_384"				, pretrained=_lowerCamelCase  )

														from_model.eval()
														_lowerCAmelCase     :							List[Any]    =    LevitForImageClassificationWithTeacher(_lowerCamelCase  ).eval()
														_lowerCAmelCase     :							Tuple    =    OrderedDict()

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

														_lowerCAmelCase     :							str    =    torch.randn((2, 3, 224, 224)  )
														_lowerCAmelCase     :							Optional[int]    =    from_model(_lowerCamelCase  )
														_lowerCAmelCase     :							int    =    our_model(_lowerCamelCase  ).logits

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

							_lowerCAmelCase     :							Tuple    =    name
							print(_lowerCamelCase  )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name  )
														_lowerCAmelCase     :							List[Any]    =    LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name  )

														print(F"Pushed {checkpoint_name}"  )






def 				A				(					_lowerCamelCase				, _lowerCamelCase = None				, _lowerCamelCase = True  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Union[str, Any]    =    1_000
							_lowerCAmelCase     :							Optional[int]    =    (1, num_labels)

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

							_lowerCAmelCase     :							List[str]    =    idalabel
							_lowerCAmelCase     :							Optional[Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    partial(_lowerCamelCase				, num_labels=_lowerCamelCase				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase  )

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

							_lowerCAmelCase     :							str    =    {
							    "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__":
						_snake_case		=  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",
						)

						_snake_case		=  parser.parse_args()
						_snake_case		=  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 copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
