

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A_      =							{"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_      =							[
				    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "GPTJForCausalLM",
				    "GPTJForQuestionAnswering",
				    "GPTJForSequenceClassification",
				    "GPTJModel",
				    "GPTJPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_      =							[
				    "TFGPTJForCausalLM",
				    "TFGPTJForQuestionAnswering",
				    "TFGPTJForSequenceClassification",
				    "TFGPTJModel",
				    "TFGPTJPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_      =							[
				    "FlaxGPTJForCausalLM",
				    "FlaxGPTJModel",
				    "FlaxGPTJPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_gptj import (
								    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
								    GPTJForCausalLM,
								    GPTJForQuestionAnswering,
								    GPTJForSequenceClassification,
								    GPTJModel,
								    GPTJPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_gptj import (
								    TFGPTJForCausalLM,
								    TFGPTJForQuestionAnswering,
								    TFGPTJForSequenceClassification,
								    TFGPTJModel,
								    TFGPTJPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
				import sys

				A_      =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









'''simple docstring'''

import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

A_      =							["UtteranceLevel"]





@torch.no_grad()
def 				A_					(	snake_case ,    snake_case ,    snake_case ,    snake_case	):
  SCREAMING_SNAKE_CASE:Optional[int]   =   torch.load(snake_case ,    map_location="cpu"	)
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(F'''The supported s3prl models are {SUPPORTED_MODELS}'''	)

  SCREAMING_SNAKE_CASE:Tuple   =   checkpoint["Downstream"]

  SCREAMING_SNAKE_CASE:Union[str, Any]   =   HubertConfig.from_pretrained(snake_case	)
  SCREAMING_SNAKE_CASE:int   =   HubertForSequenceClassification.from_pretrained(snake_case ,    config=snake_case	)
  SCREAMING_SNAKE_CASE:Union[str, Any]   =   WavaVecaFeatureExtractor.from_pretrained(
      snake_case ,    return_attention_mask=snake_case ,    do_normalize=snake_case	)

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

  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["projector.weight"]
  SCREAMING_SNAKE_CASE:Optional[Any]   =   downstream_dict["projector.bias"]
  SCREAMING_SNAKE_CASE:List[Any]   =   downstream_dict["model.post_net.linear.weight"]
  SCREAMING_SNAKE_CASE:Tuple   =   downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(snake_case	)
  hf_model.save_pretrained(snake_case	)


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






