
import argparse

import torch

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


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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			WavLMForSequenceClassification.from_pretrained(__lowerCAmelCase      ,						config=__lowerCAmelCase							)
    snake_case__  =			downstream_dict["projector.weight"]
    snake_case__  =			downstream_dict["projector.bias"]
    snake_case__  =			downstream_dict["model.post_net.linear.weight"]
    snake_case__  =			downstream_dict["model.post_net.linear.bias"]
    return model





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			WavLMForAudioFrameClassification.from_pretrained(__lowerCAmelCase      ,						config=__lowerCAmelCase							)
    snake_case__  =			downstream_dict["model.linear.weight"]
    snake_case__  =			downstream_dict["model.linear.bias"]
    return model





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

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





@torch.no_grad()
def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			torch.load(__lowerCAmelCase      ,						map_location="cpu"							)

    snake_case__  =			checkpoint["Downstream"]

    snake_case__  =			WavLMConfig.from_pretrained(__lowerCAmelCase							)
    snake_case__  =			WavaVecaFeatureExtractor.from_pretrained(
        __lowerCAmelCase      ,						return_attention_mask=__lowerCAmelCase      ,						do_normalize=__lowerCAmelCase							)

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

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

    hf_feature_extractor.save_pretrained(__lowerCAmelCase							)
    hf_model.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__           =					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.''')
     __magic_name__           =					parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class    _SCREAMING_SNAKE_CASE					( unittest.TestCase    ,   __UpperCamelCase	):
      def A_     (     self	):
          snake_case__  =			load_tool("image-captioning"	)
          self.tool.setup()
          snake_case__  =			load_tool("image-captioning"						,		remote=lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)
      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



      def A_     (     self	):
          snake_case__  =			Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"	)	) / "000000039769.png"	)
          snake_case__  =			self.remote_tool(image=lowerCamelCase	)
          self.assertEqual(lowerCamelCase						,		"two cats sleeping on a couch"	)



