
'''simple docstring'''





import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Any						,			lowerCAmelCase_	:			Union[str, Any]						,			lowerCAmelCase_	:			int     ) ->							Any:
        snake_case_    							=				0
        # max greyscale value for #FFFFFF
        snake_case_    							=				int(self.get_greyscale(2_55						,			2_55						,			2_55     )     )

        if not self.min_threshold < threshold < self.max_threshold:
              snake_case_    							=				f'''Factor value should be from 0 to {self.max_threshold}'''
              raise ValueError(lowerCAmelCase_     )

        snake_case_    							=				input_img
        snake_case_    							=				threshold
        snake_case_							,						snake_case_    							=				self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        snake_case_    							=				[
            [0 for _ in range(self.height + 4     )] for __ in range(self.width + 1     )
        ]
        snake_case_    							=				np.ones((self.width, self.height, 3)						,			np.uinta     ) * 2_55

  @classmethod
  def 		_lowerCamelCase						(cls	:			Tuple						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int     ) ->							float:
        return 0.114 * blue + 0.587 * green + 0.2_126 * red


  def 		_lowerCamelCase						(self	:			Union[str, Any]     ) ->							None:
        for y in range(self.height     ):
              for x in range(self.width     ):
                    snake_case_    							=				int(self.get_greyscale(*self.input_img[y][x]     )     )
                    if self.threshold > greyscale + self.error_table[y][x]:
                          snake_case_    							=				(0, 0, 0)
                          snake_case_    							=				greyscale + self.error_table[x][y]
                    else:
                          snake_case_    							=				(2_55, 2_55, 2_55)
                          snake_case_    							=				greyscale + self.error_table[x][y] - 2_55
                    self.error_table[y][x + 1] += int(8 / 32 * current_error     )
                    self.error_table[y][x + 2] += int(4 / 32 * current_error     )
                    self.error_table[y + 1][x] += int(8 / 32 * current_error     )
                    self.error_table[y + 1][x + 1] += int(4 / 32 * current_error     )
                    self.error_table[y + 1][x + 2] += int(2 / 32 * current_error     )
                    self.error_table[y + 1][x - 1] += int(4 / 32 * current_error     )
                    self.error_table[y + 1][x - 2] += int(2 / 32 * current_error     )


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     A							     = [
         Burkes(imread("image_data/lena.jpg", 1), threshold)
         for threshold in (1, 1_2_6, 1_3_0, 1_4_0)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              F"""Original image with dithering threshold: {burkes.threshold}""",
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()



'''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)

