

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  __UpperCAmelCase    				=     argparse.ArgumentParser()

  parser.add_argument(
      '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
  )
  parser.add_argument(
      '--original_config_file',
      type=str,
      required=True,
      help='The YAML config file corresponding to the original architecture.',
  )
  parser.add_argument(
      '--num_in_channels',
      default=None,
      type=int,
      help='The number of input channels. If `None` number of input channels will be automatically inferred.',
  )
  parser.add_argument(
      '--image_size',
      default=5_1_2,
      type=int,
      help=(
          'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
          ' Base. Use 768 for Stable Diffusion v2.'
      ),
  )
  parser.add_argument(
      '--extract_ema',
      action='store_true',
      help=(
          'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
          ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
          ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
      ),
  )
  parser.add_argument(
      '--upcast_attention',
      action='store_true',
      help=(
          'Whether the attention computation should always be upcasted. This is necessary when running stable'
          ' diffusion 2.1.'
      ),
  )
  parser.add_argument(
      '--from_safetensors',
      action='store_true',
      help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
  )
  parser.add_argument(
      '--to_safetensors',
      action='store_true',
      help='Whether to store pipeline in safetensors format or not.',
  )
  parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
  parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')







  def 	_snake_case		(    SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

       """simple docstring"""







       if string == "True":
            return True
       elif string == "False":
            return False
       else:
            raise ValueError(f'''could not parse string as bool {string}'''				)

  parser.add_argument(
      '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
  )

  parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

  __UpperCAmelCase    				=     parser.parse_args()

  __UpperCAmelCase    				=     download_controlnet_from_original_ckpt(
      checkpoint_path=args.checkpoint_path,
      original_config_file=args.original_config_file,
      image_size=args.image_size,
      extract_ema=args.extract_ema,
      num_in_channels=args.num_in_channels,
      upcast_attention=args.upcast_attention,
      from_safetensors=args.from_safetensors,
      device=args.device,
      use_linear_projection=args.use_linear_projection,
      cross_attention_dim=args.cross_attention_dim,
  )



  controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)









from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class A__    ( A ):



 """simple docstring"""







 def    __magic_name__       (		self :	List[Any]     ,						A_ :	float       ):






      '''simple docstring'''




      return 0.0







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      tuple[int | float, int | float]:

     """simple docstring"""







     _lowerCAmelCase   :					Union[str, Any]        =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
     _lowerCAmelCase   :					Optional[Any]        =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
     return lowest, highest







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Any        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Optional[Any]        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Dict        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Dict        =    np.abs(np.fft.fft(SCREAMING_SNAKE_CASE				)				)
     _lowerCAmelCase   :					str        =    20 * np.logaa(SCREAMING_SNAKE_CASE				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     # Display within reasonable bounds
     _lowerCAmelCase   :					Tuple        =    get_bounds(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     plt.ylim(max([-80, bounds[0]]				)		,							min([80, bounds[1]]				)				)
     plt.ylabel("Gain (dB)"				)

     plt.plot(SCREAMING_SNAKE_CASE				)
     plt.show()







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      None:

     """simple docstring"""







     _lowerCAmelCase   :					Tuple        =    512
     _lowerCAmelCase   :					Tuple        =    [1] + [0] * (size - 1)
     _lowerCAmelCase   :					Dict        =    [filter_type.process(SCREAMING_SNAKE_CASE				) for item in inputs]

     _lowerCAmelCase   :					Tuple        =    [0] * (samplerate - size)  # zero-padding
     outputs += filler
     _lowerCAmelCase   :					Tuple        =    np.angle(np.fft.fft(SCREAMING_SNAKE_CASE				)				)

     # Frequencies on log scale from 24 to nyquist frequency
     plt.xlim(24		,							samplerate / 2 - 1				)
     plt.xlabel("Frequency (Hz)"				)
     plt.xscale("log"				)

     plt.ylim(-2 * pi		,							2 * pi				)
     plt.ylabel("Phase shift (Radians)"				)
     plt.plot(np.unwrap(SCREAMING_SNAKE_CASE		,							-2 * pi				)				)
     plt.show()






