

"""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()
a    :Tuple			=       logging.get_logger()




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase     ,    __lowerCAmelCase = True							)				-> Optional[Any]:
      print(F'''Converting {name}...'''							)

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

            from_model.eval()
            SCREAMING_SNAKE_CASE__      :					Optional[int]											=		LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__							).eval()
            SCREAMING_SNAKE_CASE__      :					Tuple											=		OrderedDict()

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

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

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

      SCREAMING_SNAKE_CASE__      :					Tuple											=		name
      print(SCREAMING_SNAKE_CASE__							)

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name							)
            SCREAMING_SNAKE_CASE__      :					Optional[int]											=		LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name							)

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




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase = None     ,    __lowerCAmelCase = True							)				-> Optional[int]:
      SCREAMING_SNAKE_CASE__      :					Any											=		'imagenet-1k-id2label.json'
      SCREAMING_SNAKE_CASE__      :					Tuple											=		1000
      SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		(1, num_labels)

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

      SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		idalabel
      SCREAMING_SNAKE_CASE__      :					int											=		{v: k for k, v in idalabel.items()}

      SCREAMING_SNAKE_CASE__      :					Dict											=		partial(SCREAMING_SNAKE_CASE__     ,    num_labels=SCREAMING_SNAKE_CASE__     ,    idalabel=SCREAMING_SNAKE_CASE__     ,    labelaid=SCREAMING_SNAKE_CASE__							)

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

      SCREAMING_SNAKE_CASE__      :					Tuple											=		{
          '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]     ,    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__":
  a    :List[str]			=       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",
  )

  a    :Optional[int]			=       parser.parse_args()
  a    :Union[str, Any]			=       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 (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
 from .camera import create_pan_cameras
 from .pipeline_shap_e import ShapEPipeline
 from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
 from .renderer import (
     BoundingBoxVolume,
     ImportanceRaySampler,
     MLPNeRFModelOutput,
     MLPNeRSTFModel,
     ShapEParamsProjModel,
     ShapERenderer,
     StratifiedRaySampler,
     VoidNeRFModel,
 )


