




import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


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



def 							_A   (						lowerCAmelCase_     :					Any   ,				lowerCAmelCase_     :					str   ,				lowerCAmelCase_     :					Any			):
     """simple docstring"""

     lowerCAmelCase__		= WavLMForSequenceClassification.from_pretrained(lowerCAmelCase_   ,				config=lowerCAmelCase_			)
     lowerCAmelCase__		= downstream_dict["projector.weight"]
     lowerCAmelCase__		= downstream_dict["projector.bias"]
     lowerCAmelCase__		= downstream_dict["model.post_net.linear.weight"]
     lowerCAmelCase__		= downstream_dict["model.post_net.linear.bias"]
     return model



def 							_A   (						lowerCAmelCase_     :					Union[str, Any]   ,				lowerCAmelCase_     :					Optional[int]   ,				lowerCAmelCase_     :					List[str]			):
     """simple docstring"""

     lowerCAmelCase__		= WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase_   ,				config=lowerCAmelCase_			)
     lowerCAmelCase__		= downstream_dict["model.linear.weight"]
     lowerCAmelCase__		= downstream_dict["model.linear.bias"]
     return model



def 							_A   (						lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[Any]   ,				lowerCAmelCase_     :					Dict			):
     """simple docstring"""

     lowerCAmelCase__		= WavLMForXVector.from_pretrained(lowerCAmelCase_   ,				config=lowerCAmelCase_			)
     lowerCAmelCase__		= downstream_dict["connector.weight"]
     lowerCAmelCase__		= downstream_dict["connector.bias"]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
          lowerCAmelCase__		= downstream_dict[
              F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
          ]
          lowerCAmelCase__		= downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

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



@torch.no_grad()
def 							_A   (						lowerCAmelCase_     :					List[Any]   ,				lowerCAmelCase_     :					List[str]   ,				lowerCAmelCase_     :					Dict   ,				lowerCAmelCase_     :					List[Any]			):
     """simple docstring"""

     lowerCAmelCase__		= torch.load(lowerCAmelCase_   ,				map_location="cpu"			)

     lowerCAmelCase__		= checkpoint["Downstream"]

     lowerCAmelCase__		= WavLMConfig.from_pretrained(lowerCAmelCase_			)
     lowerCAmelCase__		= WavaVecaFeatureExtractor.from_pretrained(
         lowerCAmelCase_   ,				return_attention_mask=lowerCAmelCase_   ,				do_normalize=lowerCAmelCase_			)

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

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

     hf_feature_extractor.save_pretrained(lowerCAmelCase_			)
     hf_model.save_pretrained(lowerCAmelCase_			)


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 os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class  __lowerCamelCase       (    unittest.TestCase    ):




    """simple docstring"""

    snake_case__					=      EsmTokenizer


    def    a      (       self     :     str   )      ->       Tuple:
         super().setUp()
         lowerCAmelCase__		= tempfile.mkdtemp()
         # fmt: off
         lowerCAmelCase__		= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
         # fmt: on
         lowerCAmelCase__		= os.path.join(self.tmpdirname   ,	VOCAB_FILES_NAMES["vocab_file"]   )
         with open(self.vocab_file   ,	"w"   ,	encoding="utf-8"   ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]   )   )


    def    a      (       self     :     Dict   ,	**SCREAMING_SNAKE_CASE__     :     Optional[Any]   )      ->       List[PreTrainedTokenizerBase]:
         return [self.get_tokenizer(**SCREAMING_SNAKE_CASE__   )]


    def    a      (       self     :     List[str]   ,	**SCREAMING_SNAKE_CASE__     :     str   )      ->       PreTrainedTokenizer:
         return self.tokenizer_class.from_pretrained(self.tmpdirname   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     str   )      ->       Tuple:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= tokenizer.tokenize("LAGVS"   )
         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	["L", "A", "G", "V", "S"]   )
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )   ,	[4, 5, 6, 7, 8]   )


    def    a      (       self     :     int   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= "LAGVS"
         self.assertListEqual(tokenizer.encode(SCREAMING_SNAKE_CASE__   )   ,	[0, 4, 5, 6, 7, 8, 2]   )


    def    a      (       self     :     Union[str, Any]   )      ->       str:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]   )


    def    a      (       self     :     Any   )      ->       Union[str, Any]:
         lowerCAmelCase__		= self.tokenizer_class(self.vocab_file   )

         lowerCAmelCase__		= ["LAGVS", "WCB"]
         lowerCAmelCase__		= tokenizer(SCREAMING_SNAKE_CASE__   ,	padding=SCREAMING_SNAKE_CASE__   )["input_ids"]

         self.assertListEqual(SCREAMING_SNAKE_CASE__   ,	[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]   )


    def    a      (       self     :     str   )      ->       Optional[Any]:
         lowerCAmelCase__		= self.get_tokenizers(fast=SCREAMING_SNAKE_CASE__   )
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'   ):
                   lowerCAmelCase__		= "<unk>"
                   lowerCAmelCase__		= "<mask>"

                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
                   lowerCAmelCase__		= tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__   )   ,	1   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
                   self.assertEqual(token_a[0]   ,	SCREAMING_SNAKE_CASE__   )
