


import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
A   :					Dict	=      logging.get_logger(__name__)

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					Any					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       List[Any]					=						UniSpeechSatForSequenceClassification.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["projector.weight"]
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["projector.bias"]
      lowerCamelCase__	:       int					=						downstream_dict["model.post_net.linear.weight"]
      lowerCamelCase__	:       Optional[Any]					=						downstream_dict["model.post_net.linear.bias"]
      return model

def    lowercase_	(	_A    :					str					,      _A    :					List[Any]					,      _A    :					List[str]		):

      """simple docstring"""




      lowerCamelCase__	:       Tuple					=						UniSpeechSatForAudioFrameClassification.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       List[str]					=						downstream_dict["model.linear.weight"]
      lowerCamelCase__	:       Optional[int]					=						downstream_dict["model.linear.bias"]
      return model

def    lowercase_	(	_A    :					Optional[int]					,      _A    :					str					,      _A    :					Optional[Any]		):

      """simple docstring"""




      lowerCamelCase__	:       int					=						UniSpeechSatForXVector.from_pretrained(_A					,      config=_A		)
      lowerCamelCase__	:       List[Any]					=						downstream_dict["connector.weight"]
      lowerCamelCase__	:       int					=						downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
            lowerCamelCase__	:       Optional[Any]					=						downstream_dict[
                F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
            ]
            lowerCamelCase__	:       Dict					=						downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

      lowerCamelCase__	:       str					=						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      lowerCamelCase__	:       List[Any]					=						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      lowerCamelCase__	:       Dict					=						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      lowerCamelCase__	:       Optional[Any]					=						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      lowerCamelCase__	:       Dict					=						downstream_dict["objective.W"]
      return model

@torch.no_grad()
def    lowercase_	(	_A    :					str					,      _A    :					Tuple					,      _A    :					str					,      _A    :					Tuple		):

      """simple docstring"""




      lowerCamelCase__	:       List[str]					=						torch.load(_A					,      map_location="cpu"		)

      lowerCamelCase__	:       Dict					=						checkpoint["Downstream"]

      lowerCamelCase__	:       List[Any]					=						UniSpeechSatConfig.from_pretrained(_A		)
      lowerCamelCase__	:       Dict					=						WavaVecaFeatureExtractor.from_pretrained(
          _A					,      return_attention_mask=_A					,      do_normalize=_A		)

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

      if hf_config.use_weighted_layer_sum:
            lowerCamelCase__	:       Optional[Any]					=						checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(_A		)
      hf_model.save_pretrained(_A		)


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






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


