

"""simple docstring"""



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Optional[int]           =		["""image_processor""", """tokenizer"""]
 _snake_case   :			Dict           =		"""BlipImageProcessor"""
 _snake_case   :			Any           =		"""AutoTokenizer"""


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase		):
        super().__init__(lowerCamelCase  ,						lowerCamelCase		)

        # add QFormer tokenizer
        __a     				=  qformer_tokenizer


 def __call__(				self  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = True  ,						lowerCamelCase = False  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = 0  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = False  ,						lowerCamelCase = True  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
        if images is None and text is None:
               raise ValueError("You have to specify at least images or text."		)

        __a     				=  BatchFeature()

        if text is not None:
               __a     				=  self.tokenizer(
                   text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
               encoding.update(lowerCamelCase		)
               __a     				=  self.qformer_tokenizer(
                   text=lowerCamelCase  ,						add_special_tokens=lowerCamelCase  ,						padding=lowerCamelCase  ,						truncation=lowerCamelCase  ,						max_length=lowerCamelCase  ,						stride=lowerCamelCase  ,						pad_to_multiple_of=lowerCamelCase  ,						return_attention_mask=lowerCamelCase  ,						return_overflowing_tokens=lowerCamelCase  ,						return_special_tokens_mask=lowerCamelCase  ,						return_offsets_mapping=lowerCamelCase  ,						return_token_type_ids=lowerCamelCase  ,						return_length=lowerCamelCase  ,						verbose=lowerCamelCase  ,						return_tensors=lowerCamelCase  ,						**lowerCamelCase  ,						)
               __a     				=  qformer_text_encoding.pop("input_ids"		)
               __a     				=  qformer_text_encoding.pop("attention_mask"		)

        if images is not None:
               __a     				=  self.image_processor(lowerCamelCase  ,						return_tensors=lowerCamelCase		)
               encoding.update(lowerCamelCase		)

        return encoding


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.batch_decode(*lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        return self.tokenizer.decode(*lowerCamelCase  ,						**lowerCamelCase		)


 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 		a__ (				self		):
        __a     				=  self.tokenizer.model_input_names
        __a     				=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)


 def 		a__ (				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        if os.path.isfile(lowerCamelCase		):
               raise ValueError(F"Provided path ({save_directory}) should be a directory, not a file"		)
        os.makedirs(lowerCamelCase  ,						exist_ok=lowerCamelCase		)
        __a     				=  os.path.join(lowerCamelCase  ,						"qformer_tokenizer"		)
        self.qformer_tokenizer.save_pretrained(lowerCamelCase		)
        return super().save_pretrained(lowerCamelCase  ,						**lowerCamelCase		)






 @classmethod
 def 		a__ (				cls  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a     				=  AutoTokenizer.from_pretrained(lowerCamelCase  ,						subfolder="qformer_tokenizer"		)
        __a     				=  cls._get_arguments_from_pretrained(lowerCamelCase  ,						**lowerCamelCase		)
        args.append(lowerCamelCase		)
        return cls(*lowerCamelCase		)

"""simple docstring"""



import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__:Dict			=	logging.get_logger(__name__)



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForSequenceClassification.from_pretrained(a ,  config=a							)
       __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"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForAudioFrameClassification.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["model.linear.weight"]
       __a     				=  downstream_dict["model.linear.bias"]
       return model



def 					_lowerCamelCase(					a ,  a ,  a							):
       __a     				=  WavaVecaForXVector.from_pretrained(a ,  config=a							)
       __a     				=  downstream_dict["connector.weight"]
       __a     				=  downstream_dict["connector.bias"]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
              __a     				=  downstream_dict[
                  F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
              ]
              __a     				=  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       __a     				=  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       __a     				=  downstream_dict["objective.W"]
       return model



@torch.no_grad()
def 					_lowerCamelCase(					a ,  a ,  a ,  a							):
       __a     				=  torch.load(a ,  map_location="cpu"							)

       __a     				=  checkpoint["Downstream"]

       __a     				=  WavaVecaConfig.from_pretrained(a							)
       __a     				=  WavaVecaFeatureExtractor.from_pretrained(
           a ,  return_attention_mask=a ,  do_normalize=a							)

       __a     				=  hf_config.architectures[0]
       if arch.endswith("ForSequenceClassification"							):
              __a     				=  convert_classification(a ,  a ,  a							)
       elif arch.endswith("ForAudioFrameClassification"							):
              __a     				=  convert_diarization(a ,  a ,  a							)
       elif arch.endswith("ForXVector"							):
              __a     				=  convert_xvector(a ,  a ,  a							)
       else:
              raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"							)

       if hf_config.use_weighted_layer_sum:
              __a     				=  checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(a							)
       hf_model.save_pretrained(a							)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, 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.""")
   SCREAMING_SNAKE_CASE__:Any			=	parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
