






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__UpperCAmelCase      =				["UtteranceLevel"]





@torch.no_grad()
def 						A_					(						lowercase_	,  lowercase_	,  lowercase_	,  lowercase_			)				->Union[str, Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		torch.load(lowercase_	,  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				    =		checkpoint['Downstream']

 SCREAMING_SNAKE_CASE				    =		HubertConfig.from_pretrained(lowercase_			)
 SCREAMING_SNAKE_CASE				    =		HubertForSequenceClassification.from_pretrained(lowercase_	,  config=lowercase_			)
 SCREAMING_SNAKE_CASE				    =		WavaVecaFeatureExtractor.from_pretrained(
     lowercase_	,  return_attention_mask=lowercase_	,  do_normalize=lowercase_			)

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

 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['projector.bias']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.weight']
 SCREAMING_SNAKE_CASE				    =		downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(lowercase_			)
 hf_model.save_pretrained(lowercase_			)


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






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)							->						List[str]:
			# Construct configuration depending on size
			lowerCAmelCase__ : Tuple   =						{'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			lowerCAmelCase__ ,							lowerCAmelCase__ ,							lowerCAmelCase__ : Optional[int]   =						MODELS[model_size]  # set model hyperparameters
			lowerCAmelCase__ : Optional[int]   =						ImageGPTConfig(n_embd=SCREAMING_SNAKE_CASE_			,				n_layer=SCREAMING_SNAKE_CASE_			,				n_head=SCREAMING_SNAKE_CASE_	)
			lowerCAmelCase__ : List[Any]   =						ImageGPTForCausalLM(SCREAMING_SNAKE_CASE_	)

			# Load weights from numpy
			load_tf_weights_in_imagegpt(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_	)

			# Save pytorch-model
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			lowerCAmelCase__ : Optional[int]   =						pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
			torch.save(model.state_dict()			,				SCREAMING_SNAKE_CASE_	)
			print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
			with open(SCREAMING_SNAKE_CASE_			,				'w'			,				encoding='utf-8'	) as f:
						f.write(config.to_json_string()	)


if __name__ == "__main__":
					lowerCamelCase__        =						argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--imagegpt_checkpoint_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the TensorFlow checkpoint path.""",
					)
					parser.add_argument(
					    """--model_size""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					lowerCamelCase__        =						parser.parse_args()
					convert_imagegpt_checkpoint_to_pytorch(
					    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
					)