

'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 							_snake_case	(    lowerCAmelCase_  ):
			_A    :       Union[str, Any]      				=   ['''image_processor''', '''tokenizer''']
			_A    :       Optional[int]      				=   '''AutoImageProcessor'''
			_A    :       List[str]      				=   '''AutoTokenizer'''
			def __init__(	self     : List[str]       ,SCREAMING_SNAKE_CASE__     : List[str]=None       ,SCREAMING_SNAKE_CASE__     : int=None       ,**SCREAMING_SNAKE_CASE__     : Tuple							):
					SCREAMING_SNAKE_CASE:Union[str, Any]   =   None
					if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."       ,_snake_case       ,)
							SCREAMING_SNAKE_CASE:Optional[int]   =   kwargs.pop("feature_extractor"							)

					SCREAMING_SNAKE_CASE:Any   =   image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."							)
					if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."							)

					super().__init__(_snake_case       ,_snake_case							)
					SCREAMING_SNAKE_CASE:Optional[int]   =   self.image_processor
					SCREAMING_SNAKE_CASE:Any   =   False
			def __call__(	self     : Optional[Any]       ,*SCREAMING_SNAKE_CASE__     : List[str]       ,**SCREAMING_SNAKE_CASE__     : int							):
					# For backward compatibility
					if self._in_target_context_manager:
							return self.current_processor(*_snake_case       ,**_snake_case							)

					SCREAMING_SNAKE_CASE:Any   =   kwargs.pop("images"       ,_snake_case							)
					SCREAMING_SNAKE_CASE:List[str]   =   kwargs.pop("text"       ,_snake_case							)
					if len(_snake_case							) > 0:
							SCREAMING_SNAKE_CASE:str   =   args[0]
							SCREAMING_SNAKE_CASE:Dict   =   args[1:]

					if images is None and text is None:
							raise ValueError("You need to specify either an `images` or `text` input to process."							)

					if images is not None:
							SCREAMING_SNAKE_CASE:Optional[Any]   =   self.image_processor(_snake_case       ,*_snake_case       ,**_snake_case							)
					if text is not None:
							SCREAMING_SNAKE_CASE:List[str]   =   self.tokenizer(_snake_case       ,**_snake_case							)

					if text is None:
							return inputs
					elif images is None:
							return encodings
					else:
							SCREAMING_SNAKE_CASE:Optional[int]   =   encodings['input_ids']
							return inputs
			def       __UpperCamelCase  (	self     : List[str]       ,*SCREAMING_SNAKE_CASE__     : Dict       ,**SCREAMING_SNAKE_CASE__     : List[str]							):
					return self.tokenizer.batch_decode(*_snake_case       ,**_snake_case							)
			def       __UpperCamelCase  (	self     : int       ,*SCREAMING_SNAKE_CASE__     : Optional[int]       ,**SCREAMING_SNAKE_CASE__     : str							):
					return self.tokenizer.decode(*_snake_case       ,**_snake_case							)
			@contextmanager
			def       __UpperCamelCase  (	self     : Optional[Any]							):
					warnings.warn(
					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
					    "your images inputs, or in a separate call."							)
					SCREAMING_SNAKE_CASE:Tuple   =   True
					SCREAMING_SNAKE_CASE:Union[str, Any]   =   self.tokenizer
					yield
					SCREAMING_SNAKE_CASE:str   =   self.image_processor
					SCREAMING_SNAKE_CASE:int   =   False
			@property
			def       __UpperCamelCase  (	self     : Dict							):
					warnings.warn(
					    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."       ,_snake_case       ,)
					return self.image_processor_class







			@property
			def       __UpperCamelCase  (	self     : List[str]							):
					warnings.warn(
					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       ,_snake_case       ,)
					return self.image_processor









'''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)






