"""simple docstring"""



import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 				snake_case__     (   snake_case_,		unittest.TestCase   ):
 _snake_case   :			List[str]           =		BioGptTokenizer
 _snake_case   :			int           =		False


 def 		a__ (				self		):
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __a     				=  [
            "l",
            "o",
            "w",
            "e",
            "r",
            "s",
            "t",
            "i",
            "d",
            "n",
            "w</w>",
            "r</w>",
            "t</w>",
            "lo",
            "low",
            "er</w>",
            "low</w>",
            "lowest</w>",
            "newer</w>",
            "wider</w>",
            "<unk>",
        ]
        __a     				=  dict(zip(lowerCamelCase  ,						range(len(lowerCamelCase		)		)		)		)
        __a     				=  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

        __a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["vocab_file"]		)
        __a     				=  os.path.join(self.tmpdirname  ,						VOCAB_FILES_NAMES["merges_file"]		)
        with open(self.vocab_file  ,						"w"		) as fp:
               fp.write(json.dumps(lowerCamelCase		)		)
        with open(self.merges_file  ,						"w"		) as fp:
               fp.write("\n".join(lowerCamelCase		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  "lower newer"
        __a     				=  "lower newer"
        return input_text, output_text


 def 		a__ (				self		):
        __a     				=  BioGptTokenizer(self.vocab_file  ,						self.merges_file		)

        __a     				=  "lower"
        __a     				=  ["low", "er</w>"]
        __a     				=  tokenizer.tokenize(lowerCamelCase		)
        self.assertListEqual(lowerCamelCase  ,						lowerCamelCase		)

        __a     				=  tokens + ["<unk>"]
        __a     				=  [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)  ,						lowerCamelCase		)






 @slow
 def 		a__ (				self		):
        __a     				=  BioGptTokenizer.from_pretrained("microsoft/biogpt"		)

        __a     				=  tokenizer.encode("sequence builders"  ,						add_special_tokens=lowerCamelCase		)
        __a     				=  tokenizer.encode("multi-sequence build"  ,						add_special_tokens=lowerCamelCase		)

        __a     				=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
        __a     				=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase  ,						lowerCamelCase		)

        self.assertTrue(encoded_sentence == [2] + text		)
        self.assertTrue(encoded_pair == [2] + text + [2] + text_a		)

"""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)
