
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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int	,       SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							LevitConfig	,       SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							bool = True      )			->      Optional[int]:




				'''simple docstring'''





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

				with torch.no_grad():
								if hidden_sizes == 1_2_8:
												if name[-1] == "S":
																A__        =			timm.create_model("levit_128s"	,       pretrained=SCREAMING_SNAKE_CASE_      )
												else:
																A__        =			timm.create_model("levit_128"	,       pretrained=SCREAMING_SNAKE_CASE_      )
								if hidden_sizes == 1_9_2:
												A__        =			timm.create_model("levit_192"	,       pretrained=SCREAMING_SNAKE_CASE_      )
								if hidden_sizes == 2_5_6:
												A__        =			timm.create_model("levit_256"	,       pretrained=SCREAMING_SNAKE_CASE_      )
								if hidden_sizes == 3_8_4:
												A__        =			timm.create_model("levit_384"	,       pretrained=SCREAMING_SNAKE_CASE_      )

								from_model.eval()
								A__        =			LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE_      ).eval()
								A__        =			OrderedDict()

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

								A__        =			torch.randn((2, 3, 2_2_4, 2_2_4)      )
								A__        =			from_model(SCREAMING_SNAKE_CASE_      )
								A__        =			our_model(SCREAMING_SNAKE_CASE_      ).logits

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

				A__        =			name
				print(SCREAMING_SNAKE_CASE_      )

				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 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Path	,       SCREAMING_SNAKE_CASE_:							str = None	,       SCREAMING_SNAKE_CASE_:							bool = True      )			->      Union[str, Any]:




				'''simple docstring'''





				A__        =			"imagenet-1k-id2label.json"
				A__        =			1_0_0_0
				A__        =			(1, num_labels)

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

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

				A__        =			partial(SCREAMING_SNAKE_CASE_	,       num_labels=SCREAMING_SNAKE_CASE_	,       idalabel=SCREAMING_SNAKE_CASE_	,       labelaid=SCREAMING_SNAKE_CASE_      )

				A__        =			{
				    "levit-128S": 1_2_8,
				    "levit-128": 1_2_8,
				    "levit-192": 1_9_2,
				    "levit-256": 2_5_6,
				    "levit-384": 3_8_4,
				}

				A__        =			{
				    "levit-128S": ImageNetPreTrainedConfig(
				        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]	,       num_attention_heads=[4, 6, 8]	,       depths=[2, 3, 4]	,       key_dim=[1_6, 1_6, 1_6]	,       drop_path_rate=0	,       ),
				    "levit-128": ImageNetPreTrainedConfig(
				        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]	,       num_attention_heads=[4, 8, 1_2]	,       depths=[4, 4, 4]	,       key_dim=[1_6, 1_6, 1_6]	,       drop_path_rate=0	,       ),
				    "levit-192": ImageNetPreTrainedConfig(
				        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]	,       num_attention_heads=[3, 5, 6]	,       depths=[4, 4, 4]	,       key_dim=[3_2, 3_2, 3_2]	,       drop_path_rate=0	,       ),
				    "levit-256": ImageNetPreTrainedConfig(
				        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]	,       num_attention_heads=[4, 6, 8]	,       depths=[4, 4, 4]	,       key_dim=[3_2, 3_2, 3_2]	,       drop_path_rate=0	,       ),
				    "levit-384": ImageNetPreTrainedConfig(
				        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]	,       num_attention_heads=[6, 9, 1_2]	,       depths=[4, 4, 4]	,       key_dim=[3_2, 3_2, 3_2]	,       drop_path_rate=0.1	,       ),
				}

				if model_name:
								convert_weight_and_push(
								    names_to_hidden_sizes[model_name]	,       SCREAMING_SNAKE_CASE_	,       names_to_config[model_name]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )
				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 ..utils import is_flax_available, is_torch_available


if is_torch_available():
	from .autoencoder_kl import AutoencoderKL
	from .controlnet import ControlNetModel
	from .dual_transformer_ad import DualTransformeraDModel
	from .modeling_utils import ModelMixin
	from .prior_transformer import PriorTransformer
	from .ta_film_transformer import TaFilmDecoder
	from .transformer_ad import TransformeraDModel
	from .unet_ad import UNetaDModel
	from .unet_ad import UNetaDModel
	from .unet_ad_condition import UNetaDConditionModel
	from .unet_ad_condition import UNetaDConditionModel
	from .vq_model import VQModel

if is_flax_available():
	from .controlnet_flax import FlaxControlNetModel
	from .unet_ad_condition_flax import FlaxUNetaDConditionModel
	from .vae_flax import FlaxAutoencoderKL

