import argparse

import torch

from transformers import ChineseCLIPConfig, ChineseCLIPModel
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  a__		,a__		,a__  		=  pt_weights[f"{prefix}.in_proj_weight"].chunk(3			,					dim=0       )
  a__		,a__		,a__  		=  pt_weights[f"{prefix}.in_proj_bias"].chunk(3			,					dim=0       )

  a__  		=  pt_weights[f"{prefix}.out_proj.weight"]
  a__  		=  pt_weights[f"{prefix}.out_proj.bias"]

  a__  		=  q_proj
  a__  		=  q_proj_bias

  a__  		=  k_proj
  a__  		=  k_proj_bias

  a__  		=  v_proj
  a__  		=  v_proj_bias

  a__  		=  out_proj_weights
  a__  		=  out_proj_bias
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  copy_linear(hf_mlp.fca			,					lowerCamelCase__			,					f"{prefix}.c_fc"       )
  copy_linear(hf_mlp.fca			,					lowerCamelCase__			,					f"{prefix}.c_proj"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->Union[str, Any]:

  '''simple docstring'''




  a__  		=  pt_weights[f"{prefix}.weight"].data
  a__  		=  pt_weights[f"{prefix}.bias"].data
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->str:

  '''simple docstring'''




  copy_linear(hf_layer.layer_norma			,					lowerCamelCase__			,					f"{prefix}.ln_1"       )
  copy_linear(hf_layer.layer_norma			,					lowerCamelCase__			,					f"{prefix}.ln_2"       )

  # copy MLP
  copy_mlp(hf_layer.mlp			,					lowerCamelCase__			,					f"{prefix}.mlp"       )

  # copy attn
  copy_attn_layer(hf_layer.self_attn			,					lowerCamelCase__			,					f"{prefix}.attn"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->str:

  '''simple docstring'''




  for layer_id, hf_layer in enumerate(lowerCamelCase__       ):
    copy_layer(lowerCamelCase__			,					lowerCamelCase__			,					f"{prefix}.{layer_id}"       )
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  a__  		=  pt_weights["text_projection"].data.T

  # copy text encoder
  for name, param in hf_model.text_model.named_parameters():
    a__  		=  pt_weights[f"bert.{name}"].data
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  pt_weights["visual.proj"].data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layrnorm			,					lowerCamelCase__			,					"visual.ln_pre"       )
  copy_linear(hf_model.vision_model.post_layernorm			,					lowerCamelCase__			,					"visual.ln_post"       )

  # copy embeddings
  a__  		=  pt_weights["visual.conv1.weight"].data
  a__  		=  pt_weights["visual.class_embedding"].data
  a__  		=  pt_weights["visual.positional_embedding"].data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers			,					lowerCamelCase__			,					"visual.transformer.resblocks"       )
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None       )					->int:

  '''simple docstring'''




  assert config_path is not None, "Please specify the ChineseCLIP model config of the corresponding model size."
  a__  		=  ChineseCLIPConfig.from_pretrained(lowerCamelCase__       )

  a__  		=  ChineseCLIPModel(lowerCamelCase__       ).eval()

  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )["state_dict"]
  a__  		=  {(name[7:] if name.startswith("module."       ) else name): value for name, value in pt_weights.items()}

  copy_text_model_and_projection(lowerCamelCase__			,					lowerCamelCase__       )
  copy_vision_model_and_projection(lowerCamelCase__			,					lowerCamelCase__       )
  a__  		=  pt_weights["logit_scale"].data

  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         help="""Path to the output folder storing converted hf PyTorch model.""",
     )
     parser.add_argument(
         """--checkpoint_path""", default=None, type=str, help="""Path to original github format ChineseCLIP checkpoint."""
     )
     parser.add_argument(
         """--config_path""", default=None, required=True, type=str, help="""Path to hf config.json of model to convert."""
     )
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()

     convert_chinese_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
     print("""The conversion is finished!""")


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     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.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

