





from collections.abc import Generator
from math import sin
def      _A       (		SCREAMING_SNAKE_CASE     :   bytes						):





  """simple docstring"""



  if len(SCREAMING_SNAKE_CASE						) != 32:
    raise ValueError("Input must be of length 32"						)

  a__	:					Optional[Any]										=b""
  for i in [3, 2, 1, 0]:
    little_endian += string_aa[8 * i : 8 * i + 8]
  return little_endian
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  if i < 0:
    raise ValueError("Input must be non-negative"						)

  a__	:					Optional[Any]										=format(SCREAMING_SNAKE_CASE       ,       "08x"						)[-8:]
  a__	:					Tuple										=b""
  for i in [3, 2, 1, 0]:
    little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"						)
  return little_endian_hex
def      _A       (		SCREAMING_SNAKE_CASE     :   bytes						):





  """simple docstring"""



  a__	:					Any										=b""
  for char in message:
    bit_string += format(SCREAMING_SNAKE_CASE       ,       "08b"						).encode("utf-8"						)
  a__	:					Optional[Any]										=format(len(SCREAMING_SNAKE_CASE						)       ,       "064b"						).encode("utf-8"						)

  # Pad bit_string to a multiple of 512 chars
  bit_string += b"1"
  while len(SCREAMING_SNAKE_CASE						) % 512 != 448:
    bit_string += b"0"
  bit_string += to_little_endian(start_len[32:]						) + to_little_endian(start_len[:32]						)

  return bit_string
def      _A       (		SCREAMING_SNAKE_CASE     :   bytes						):





  """simple docstring"""



  if len(SCREAMING_SNAKE_CASE						) % 512 != 0:
    raise ValueError("Input must have length that's a multiple of 512"						)

  for pos in range(0       ,       len(SCREAMING_SNAKE_CASE						)       ,       512						):
    a__	:					List[str]										=bit_string[pos : pos + 512]
    a__	:					Union[str, Any]										=[]
    for i in range(0       ,       512       ,       32						):
      block_words.append(int(to_little_endian(block[i : i + 32]						)       ,       2						)						)
    yield block_words
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  if i < 0:
    raise ValueError("Input must be non-negative"						)

  a__	:					Optional[int]										=format(SCREAMING_SNAKE_CASE       ,       "032b"						)
  a__	:					Optional[int]										=""
  for c in i_str:
    new_str += "1" if c == "0" else "0"
  return int(SCREAMING_SNAKE_CASE       ,       2						)
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return (a + b) % 2**32
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  if i < 0:
    raise ValueError("Input must be non-negative"						)
  if shift < 0:
    raise ValueError("Shift must be non-negative"						)
  return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def      _A       (		SCREAMING_SNAKE_CASE     :   bytes						):





  """simple docstring"""



  a__	:					str										=preprocess(SCREAMING_SNAKE_CASE						)

  a__	:					List[str]										=[int(2**32 * abs(sin(i + 1						)						)						) for i in range(64						)]

  # Starting states
  a__	:					List[Any]										=0x67_452_301
  a__	:					Optional[Any]										=0xEF_CDA_B89
  a__	:					List[Any]										=0x98_BAD_CFE
  a__	:					Any										=0x10_325_476

  a__	:					str										=[
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      7,
      12,
      17,
      22,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      5,
      9,
      14,
      20,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      4,
      11,
      16,
      23,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
      6,
      10,
      15,
      21,
  ]

  # Process bit string in chunks, each with 16 32-char words
  for block_words in get_block_words(SCREAMING_SNAKE_CASE						):
    a__	:					List[Any]										=aa
    a__	:					List[Any]										=ba
    a__	:					List[Any]										=ca
    a__	:					List[str]										=da

    # Hash current chunk
    for i in range(64						):
      if i <= 15:
        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
        a__	:					Tuple										=d ^ (b & (c ^ d))
        a__	:					str										=i
      elif i <= 31:
        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
        a__	:					Any										=c ^ (d & (b ^ c))
        a__	:					str										=(5 * i + 1) % 16
      elif i <= 47:
        a__	:					Dict										=b ^ c ^ d
        a__	:					Optional[Any]										=(3 * i + 5) % 16
      else:
        a__	:					Optional[Any]										=c ^ (b | not_aa(SCREAMING_SNAKE_CASE						))
        a__	:					str										=(7 * i) % 16
      a__	:					Optional[Any]										=(f + a + added_consts[i] + block_words[g]) % 2**32
      a__	:					List[Any]										=d
      a__	:					List[str]										=c
      a__	:					int										=b
      a__	:					Dict										=sum_aa(SCREAMING_SNAKE_CASE       ,       left_rotate_aa(SCREAMING_SNAKE_CASE       ,       shift_amounts[i]						)						)

    # Add hashed chunk to running total
    a__	:					Union[str, Any]										=sum_aa(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    a__	:					List[Any]										=sum_aa(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    a__	:					int										=sum_aa(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
    a__	:					List[str]										=sum_aa(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)

  a__	:					Tuple										=reformat_hex(SCREAMING_SNAKE_CASE						) + reformat_hex(SCREAMING_SNAKE_CASE						) + reformat_hex(SCREAMING_SNAKE_CASE						) + reformat_hex(SCREAMING_SNAKE_CASE						)
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()













from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


lowercase      :       Any									=  logging.get_logger(__name__)  # pylint: disable=invalid-name





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

          super().__init__()
          self.register_modules(unet=lowercase					,  scheduler=lowercase)

   @torch.no_grad()
   def __call__(					self					,  lowercase = 1					,  lowercase = 100					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  )			->						Union[AudioPipelineOutput, Tuple]:







          '''simple docstring'''

          if audio_length_in_s is None:
                 a__						:	Optional[int] =				self.unet.config.sample_size / self.unet.config.sample_rate

          a__						:	int =				audio_length_in_s * self.unet.config.sample_rate

          a__						:	Union[str, Any] =				2 ** len(self.unet.up_blocks)
          if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
                     F' {3 * down_scale_factor / self.unet.config.sample_rate}.')

          a__						:	str =				int(lowercase)
          if sample_size % down_scale_factor != 0:
                 a__						:	List[str] =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
                     F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
                     ' process.')
          a__						:	List[Any] =				int(lowercase)

          a__						:	int =				next(iter(self.unet.parameters())).dtype
          a__						:	Tuple =				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(lowercase					,  lowercase) and len(lowercase) != batch_size:
                 raise ValueError(
                     F'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
                     F' size of {batch_size}. Make sure the batch size matches the length of the generators.')

          a__						:	Optional[Any] =				randn_tensor(lowercase					,  generator=lowercase					,  device=self.device					,  dtype=lowercase)

          # set step values
          self.scheduler.set_timesteps(lowercase					,  device=audio.device)
          a__						:	Union[str, Any] =				self.scheduler.timesteps.to(lowercase)

          for t in self.progress_bar(self.scheduler.timesteps):
                 # 1. predict noise model_output
                 a__						:	Dict =				self.unet(lowercase					,  lowercase).sample

                 # 2. compute previous image: x_t -> t_t-1
                 a__						:	Any =				self.scheduler.step(lowercase					,  lowercase					,  lowercase).prev_sample

          a__						:	str =				audio.clamp(-1					,  1).float().cpu().numpy()

          a__						:	List[Any] =				audio[:, :, :original_sample_size]

          if not return_dict:
                 return (audio,)

          return AudioPipelineOutput(audios=lowercase)


