


'''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   :    List[Any]											= logging.get_logger()







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  str				,       A__   :  LevitConfig				,       A__   :  Path				,       A__   :  bool = True   ):
  '''simple docstring'''





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

  with torch.no_grad():
    if hidden_sizes == 1_28:
      if name[-1] == "S":
        lowerCAmelCase_     : List[Any]     =  timm.create_model("""levit_128s"""				,       pretrained=A__   )
      else:
        lowerCAmelCase_     : Optional[Any]     =  timm.create_model("""levit_128"""				,       pretrained=A__   )
    if hidden_sizes == 1_92:
      lowerCAmelCase_     : str     =  timm.create_model("""levit_192"""				,       pretrained=A__   )
    if hidden_sizes == 2_56:
      lowerCAmelCase_     : int     =  timm.create_model("""levit_256"""				,       pretrained=A__   )
    if hidden_sizes == 3_84:
      lowerCAmelCase_     : Union[str, Any]     =  timm.create_model("""levit_384"""				,       pretrained=A__   )

    from_model.eval()
    lowerCAmelCase_     : Any     =  LevitForImageClassificationWithTeacher(A__   ).eval()
    lowerCAmelCase_     : List[Any]     =  OrderedDict()

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

    lowerCAmelCase_     : str     =  torch.randn((2, 3, 2_24, 2_24)   )
    lowerCAmelCase_     : List[str]     =  from_model(A__   )
    lowerCAmelCase_     : List[Any]     =  our_model(A__   ).logits

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

  lowerCAmelCase_     : Optional[Any]     =  name
  print(A__   )

  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 							UpperCamelCase_     (       A__   :  Path				,       A__   :  str = None				,       A__   :  bool = True   ):
  '''simple docstring'''





  lowerCAmelCase_     : Any     =  """imagenet-1k-id2label.json"""
  lowerCAmelCase_     : str     =  10_00
  lowerCAmelCase_     : Optional[int]     =  (1, num_labels)

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

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

  lowerCAmelCase_     : Dict     =  partial(A__				,       num_labels=A__				,       idalabel=A__				,       labelaid=A__   )

  lowerCAmelCase_     : Any     =  {
      """levit-128S""": 1_28,
      """levit-128""": 1_28,
      """levit-192""": 1_92,
      """levit-256""": 2_56,
      """levit-384""": 3_84,
  }

  lowerCAmelCase_     : Tuple     =  {
      """levit-128S""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]				,       num_attention_heads=[4, 6, 8]				,       depths=[2, 3, 4]				,       key_dim=[16, 16, 16]				,       drop_path_rate=0				,       ),
      """levit-128""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]				,       num_attention_heads=[4, 8, 12]				,       depths=[4, 4, 4]				,       key_dim=[16, 16, 16]				,       drop_path_rate=0				,       ),
      """levit-192""": ImageNetPreTrainedConfig(
          hidden_sizes=[1_92, 2_88, 3_84]				,       num_attention_heads=[3, 5, 6]				,       depths=[4, 4, 4]				,       key_dim=[32, 32, 32]				,       drop_path_rate=0				,       ),
      """levit-256""": ImageNetPreTrainedConfig(
          hidden_sizes=[2_56, 3_84, 5_12]				,       num_attention_heads=[4, 6, 8]				,       depths=[4, 4, 4]				,       key_dim=[32, 32, 32]				,       drop_path_rate=0				,       ),
      """levit-384""": ImageNetPreTrainedConfig(
          hidden_sizes=[3_84, 5_12, 7_68]				,       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]				,       A__				,       names_to_config[model_name]				,       A__				,       A__   )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]				,       A__				,       A__				,       A__				,       A__   )
  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   :    Union[str, Any]											= parser.parse_args()
  __A   :    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)







'''simple docstring'''






import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

  lowerCAmelCase_     : Optional[int]     =  hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""   ):
    lowerCAmelCase_     : List[str]     =  convert_classification(A__				,       A__				,       A__   )
  elif arch.endswith("""ForAudioFrameClassification"""   ):
    lowerCAmelCase_     : Dict     =  convert_diarization(A__				,       A__				,       A__   )
  elif arch.endswith("""ForXVector"""   ):
    lowerCAmelCase_     : List[Any]     =  convert_xvector(A__				,       A__				,       A__   )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'   )

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[Any]											= argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



