
import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:Any   =    logging.get_logger(__name__)

lowercase__		:Union[str, Any]   =    {
    "facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json",
    "facebook/encodec_48khz": "https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json",
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[Any]       					='''encodec'''




							def __init__(				self   ,A__=[1.5, 3.0, 6.0, 12.0, 24.0]   ,A__=2_4_0_0_0   ,A__=1   ,A__=False   ,A__=None   ,A__=None   ,A__=1_2_8   ,A__=3_2   ,A__=1   ,A__=[8, 5, 4, 2]   ,A__="weight_norm"   ,A__=7   ,A__=7   ,A__=3   ,A__=2   ,A__=True   ,A__="reflect"   ,A__=2   ,A__=2   ,A__=1.0   ,A__=1_0_2_4   ,A__=None   ,A__=True   ,**A__   ,):
												lowercase							=						target_bandwidths
												lowercase							=						sampling_rate
												lowercase							=						audio_channels
												lowercase							=						normalize
												lowercase							=						chunk_length_s
												lowercase							=						overlap
												lowercase							=						hidden_size
												lowercase							=						num_filters
												lowercase							=						num_residual_layers
												lowercase							=						upsampling_ratios
												lowercase							=						norm_type
												lowercase							=						kernel_size
												lowercase							=						last_kernel_size
												lowercase							=						residual_kernel_size
												lowercase							=						dilation_growth_rate
												lowercase							=						use_causal_conv
												lowercase							=						pad_mode
												lowercase							=						compress
												lowercase							=						num_lstm_layers
												lowercase							=						trim_right_ratio
												lowercase							=						codebook_size
												lowercase							=						codebook_dim if codebook_dim is not None else hidden_size
												lowercase							=						use_conv_shortcut

												if self.norm_type not in ["weight_norm", "time_group_norm"]:
																	raise ValueError(
																	    f'self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}')

												super().__init__(**A__)




							@property
							def  A__      (				self):
												if self.chunk_length_s is None:
																	return None
												else:
																	return int(self.chunk_length_s * self.sampling_rate)




							@property
							def  A__      (				self):
												if self.chunk_length_s is None or self.overlap is None:
																	return None
												else:
																	return max(1   ,int((1.0 - self.overlap) * self.chunk_length))




							@property
							def  A__      (				self):
												lowercase							=						np.prod(self.upsampling_ratios)
												return math.ceil(self.sampling_rate / hop_length)





							@property
							def  A__      (				self):
												return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0))








from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


