


"""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()
__snake_case             =   logging.get_logger()







def   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  str						,							lowercase  :  LevitConfig						,							lowercase  :  Path						,							lowercase  :  bool = True			)     ->						Any:


    """simple docstring"""





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

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

        from_model.eval()
        snake_case   :				Any				 =   LevitForImageClassificationWithTeacher(lowercase			).eval()
        snake_case   :				Optional[Any]				 =   OrderedDict()

        snake_case   :				Dict				 =   from_model.state_dict()
        snake_case   :				List[Any]				 =   list(from_model.state_dict().keys()			)
        snake_case   :				Tuple				 =   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   :				Optional[Any]				 =   from_model(lowercase			)
        snake_case   :				Dict				 =   our_model(lowercase			).logits

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

    snake_case   :				str				 =   name
    print(lowercase			)

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

        print(F'Pushed {checkpoint_name}'			)







def   __lowerCAmelCase						(						lowercase  :  Path						,							lowercase  :  str = None						,							lowercase  :  bool = True			)     ->						Tuple:


    """simple docstring"""





    snake_case   :				List[Any]				 =   "imagenet-1k-id2label.json"
    snake_case   :				Tuple				 =   1000
    snake_case   :				Tuple				 =   (1, num_labels)

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

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

    snake_case   :				Union[str, Any]				 =   partial(lowercase						,							num_labels=lowercase						,							idalabel=lowercase						,							labelaid=lowercase			)

    snake_case   :				List[str]				 =   {
        "levit-128S": 128,
        "levit-128": 128,
        "levit-192": 192,
        "levit-256": 256,
        "levit-384": 384,
    }

    snake_case   :				Optional[Any]				 =   {
        "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__":
  __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)









"""simple docstring"""



from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)





