
'''simple docstring'''





import numpy as np
import requests
def 	snake_case_							(				):
      snake_case_    							=				requests.get(
          "https://raw.githubusercontent.com/yashLadha/The_Math_of_Intelligence/"
          "master/Week1/ADRvsRating.csv"      )
      snake_case_    							=				response.text.splitlines()
      snake_case_    							=				[]
      for item in lines:
            snake_case_    							=				item.split(","      )
            data.append(_a      )
      data.pop(0      )  # This is for removing the labels from the list
      snake_case_    							=				np.matrix(_a      )
      return dataset
def 	snake_case_							(				_a  :				Tuple			,       _a  :				int			,       _a  :				int			,       _a  :				Union[str, Any]			,       _a  :				List[str]      ):
      snake_case_    							=				len_data

      snake_case_    							=				np.dot(_a			,       data_x.transpose()      )
      prod -= data_y.transpose()
      snake_case_    							=				np.dot(_a			,       _a      )
      snake_case_    							=				theta - (alpha / n) * sum_grad
      return theta
def 	snake_case_							(				_a  :				Dict			,       _a  :				Dict			,       _a  :				Any			,       _a  :				List[str]      ):
      snake_case_    							=				np.dot(_a			,       data_x.transpose()      )
      prod -= data_y.transpose()
      snake_case_    							=				np.sum(np.square(_a      )      )
      snake_case_    							=				sum_elem / (2 * len_data)
      return error
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				Union[str, Any]      ):
      snake_case_    							=				100_000
      snake_case_    							=				0.0_0_0_1_5_5_0

      snake_case_    							=				data_x.shape[1]
      snake_case_    							=				data_x.shape[0] - 1

      snake_case_    							=				np.zeros((1, no_features)      )

      for i in range(0			,       _a      ):
            snake_case_    							=				run_steep_gradient_descent(_a			,       _a			,       _a			,       _a			,       _a      )
            snake_case_    							=				sum_of_square_error(_a			,       _a			,       _a			,       _a      )
            print(F'''At Iteration {i + 1} - Error is {error:.5f}'''      )

      return theta
def 	snake_case_							(				_a  :				List[Any]			,       _a  :				Any      ):
      snake_case_    							=				sum(abs(y - predicted_y[i]      ) for i, y in enumerate(_a      )      )
      return total / len(_a      )
def 	snake_case_							(				):
      snake_case_    							=				collect_dataset()

      snake_case_    							=				data.shape[0]
      snake_case_    							=				np.c_[np.ones(_a      ), data[:, :-1]].astype(_a      )
      snake_case_    							=				data[:, -1].astype(_a      )

      snake_case_    							=				run_linear_regression(_a			,       _a      )
      snake_case_    							=				theta.shape[1]
      print("Resultant Feature vector : "      )
      for i in range(0			,       _a      ):
            print(F'''{theta[0, i]:.5f}'''      )


if __name__ == "__main__":
     main()



'''simple docstring'''





import argparse

import torch

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


logging.set_verbosity_info()
A							     = logging.get_logger(__name__)
def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple			,       _a  :				int      ):
      snake_case_    							=				WavLMForSequenceClassification.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				List[str]			,       _a  :				Optional[Any]			,       _a  :				List[str]      ):
      snake_case_    							=				WavLMForAudioFrameClassification.from_pretrained(_a			,       config=_a      )
      snake_case_    							=				downstream_dict["model.linear.weight"]
      snake_case_    							=				downstream_dict["model.linear.bias"]
      return model
def 	snake_case_							(				_a  :				Dict			,       _a  :				Tuple			,       _a  :				Any      ):
      snake_case_    							=				WavLMForXVector.from_pretrained(_a			,       config=_a      )
      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 	snake_case_							(				_a  :				Union[str, Any]			,       _a  :				int			,       _a  :				str			,       _a  :				List[Any]      ):
      snake_case_    							=				torch.load(_a			,       map_location="cpu"      )

      snake_case_    							=				checkpoint["Downstream"]

      snake_case_    							=				WavLMConfig.from_pretrained(_a      )
      snake_case_    							=				WavaVecaFeatureExtractor.from_pretrained(
          _a			,       return_attention_mask=_a			,       do_normalize=_a      )

      snake_case_    							=				hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"      ):
            snake_case_    							=				convert_classification(_a			,       _a			,       _a      )
      elif arch.endswith("ForAudioFrameClassification"      ):
            snake_case_    							=				convert_diarization(_a			,       _a			,       _a      )
      elif arch.endswith("ForXVector"      ):
            snake_case_    							=				convert_xvector(_a			,       _a			,       _a      )
      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(_a      )
      hf_model.save_pretrained(_a      )


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

