"""simple docstring"""



import os
import sys
import unittest


SCREAMING_SNAKE_CASE__:str			=	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


SCREAMING_SNAKE_CASE__:Tuple			=	os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
SCREAMING_SNAKE_CASE__:Dict			=	os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")






class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self		):
        __a     				=  get_test_to_tester_mapping(lowerCamelCase		)
        __a     				=  get_test_to_tester_mapping(lowerCamelCase		)

        __a     				=  {"BertModelTest": "BertModelTester"}

        __a     				=  {
            "BlipModelTest": "BlipModelTester",
            "BlipTextImageModelTest": "BlipTextImageModelsModelTester",
            "BlipTextModelTest": "BlipTextModelTester",
            "BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
            "BlipVQAModelTest": "BlipVQAModelTester",
            "BlipVisionModelTest": "BlipVisionModelTester",
        }

        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						lowerCamelCase		)
        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						lowerCamelCase		)


 def 		a__ (				self		):
        __a     				=  get_model_to_test_mapping(lowerCamelCase		)
        __a     				=  get_model_to_test_mapping(lowerCamelCase		)

        __a     				=  {
            "BertForMaskedLM": ["BertModelTest"],
            "BertForMultipleChoice": ["BertModelTest"],
            "BertForNextSentencePrediction": ["BertModelTest"],
            "BertForPreTraining": ["BertModelTest"],
            "BertForQuestionAnswering": ["BertModelTest"],
            "BertForSequenceClassification": ["BertModelTest"],
            "BertForTokenClassification": ["BertModelTest"],
            "BertLMHeadModel": ["BertModelTest"],
            "BertModel": ["BertModelTest"],
        }

        __a     				=  {
            "BlipForConditionalGeneration": ["BlipTextImageModelTest"],
            "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
            "BlipForQuestionAnswering": ["BlipVQAModelTest"],
            "BlipModel": ["BlipModelTest"],
            "BlipTextModel": ["BlipTextModelTest"],
            "BlipVisionModel": ["BlipVisionModelTest"],
        }

        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						lowerCamelCase		)
        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						lowerCamelCase		)






 def 		a__ (				self		):
        __a     				=  get_model_to_tester_mapping(lowerCamelCase		)
        __a     				=  get_model_to_tester_mapping(lowerCamelCase		)

        __a     				=  {
            "BertForMaskedLM": ["BertModelTester"],
            "BertForMultipleChoice": ["BertModelTester"],
            "BertForNextSentencePrediction": ["BertModelTester"],
            "BertForPreTraining": ["BertModelTester"],
            "BertForQuestionAnswering": ["BertModelTester"],
            "BertForSequenceClassification": ["BertModelTester"],
            "BertForTokenClassification": ["BertModelTester"],
            "BertLMHeadModel": ["BertModelTester"],
            "BertModel": ["BertModelTester"],
        }

        __a     				=  {
            "BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
            "BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
            "BlipForQuestionAnswering": ["BlipVQAModelTester"],
            "BlipModel": ["BlipModelTester"],
            "BlipTextModel": ["BlipTextModelTester"],
            "BlipVisionModel": ["BlipVisionModelTester"],
        }

        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						lowerCamelCase		)
        self.assertEqual(get_test_info.to_json(lowerCamelCase		)  ,						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)
