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()
__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			logging.get_logger()
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase = True						)	->	Tuple:

						'''simple docstring'''



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

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

												from_model.eval()
												_snake_case						:					Union[str, Any]    =   LevitForImageClassificationWithTeacher(__lowercase						).eval()
												_snake_case						:					Union[str, Any]    =   OrderedDict()

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

												_snake_case						:					str    =   torch.randn((2, 3, 224, 224)						)
												_snake_case						:					str    =   from_model(__lowercase						)
												_snake_case						:					Union[str, Any]    =   our_model(__lowercase						).logits

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

						_snake_case						:					Optional[int]    =   name
						print(__lowercase						)

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

												print(F"""Pushed {checkpoint_name}"""						)
def  snake_case						(__lowercase ,      __lowercase = None ,      __lowercase = True						)	->	Tuple:

						'''simple docstring'''



						_snake_case						:					Any    =   "imagenet-1k-id2label.json"
						_snake_case						:					Dict    =   1_000
						_snake_case						:					List[Any]    =   (1, num_labels)

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

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

						_snake_case						:					Any    =   partial(__lowercase ,      num_labels=__lowercase ,      idalabel=__lowercase ,      labelaid=__lowercase						)

						_snake_case						:					Union[str, Any]    =   {
						    "levit-128S": 128,
						    "levit-128": 128,
						    "levit-192": 192,
						    "levit-256": 256,
						    "levit-384": 384,
						}

						_snake_case						:					List[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] ,      __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__":
				__SCREAMING_SNAKE_CASE						:   Dict         =			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',
				)

				__SCREAMING_SNAKE_CASE						:   Any         =			parser.parse_args()
				__SCREAMING_SNAKE_CASE						:   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)
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			{
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			[
				    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'AutoformerForPrediction',
				    'AutoformerModel',
				    'AutoformerPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)