
from collections.abc import Generator
from math import sin


def 							__A(lowerCAmelCase )		->				bytes:

		"""simple docstring"""






		if len(lowerCAmelCase ) != 3_2:
				raise ValueError("""Input must be of length 32""" )

		_UpperCamelCase						    =	b""""""
		for i in [3, 2, 1, 0]:
				little_endian += string_aa[8 * i : 8 * i + 8]
		return little_endian


def 							__A(lowerCAmelCase )		->				bytes:

		"""simple docstring"""






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

		_UpperCamelCase						    =	format(lowerCAmelCase						,  """08x""" )[-8:]
		_UpperCamelCase						    =	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(lowerCAmelCase )		->				bytes:

		"""simple docstring"""






		_UpperCamelCase						    =	b""""""
		for char in message:
				bit_string += format(lowerCAmelCase						,  """08b""" ).encode("""utf-8""" )
		_UpperCamelCase						    =	format(len(lowerCAmelCase )						,  """064b""" ).encode("""utf-8""" )

		# Pad bit_string to a multiple of 512 chars
		bit_string += b"1"
		while len(lowerCAmelCase ) % 5_1_2 != 4_4_8:
				bit_string += b"0"
		bit_string += to_little_endian(start_len[3_2:] ) + to_little_endian(start_len[:3_2] )

		return bit_string


def 							__A(lowerCAmelCase )		->				Generator[list[int], None, None]:

		"""simple docstring"""






		if len(lowerCAmelCase ) % 5_1_2 != 0:
				raise ValueError("""Input must have length that's a multiple of 512""" )

		for pos in range(0						,  len(lowerCAmelCase )						,  5_1_2 ):
				_UpperCamelCase						    =	bit_string[pos : pos + 5_1_2]
				_UpperCamelCase						    =	[]
				for i in range(0						,  5_1_2						,  3_2 ):
						block_words.append(int(to_little_endian(block[i : i + 3_2] )						,  2 ) )
				yield block_words


def 							__A(lowerCAmelCase )		->				int:

		"""simple docstring"""






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

		_UpperCamelCase						    =	format(lowerCAmelCase						,  """032b""" )
		_UpperCamelCase						    =	""""""
		for c in i_str:
				new_str += "1" if c == "0" else "0"
		return int(lowerCAmelCase						,  2 )


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				int:

		"""simple docstring"""






		return (a + b) % 2**3_2


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				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 >> (3_2 - shift))) % 2**3_2


def 							__A(lowerCAmelCase )		->				bytes:

		"""simple docstring"""






		_UpperCamelCase						    =	preprocess(lowerCAmelCase )

		_UpperCamelCase						    =	[int(2**3_2 * abs(sin(i + 1 ) ) ) for i in range(6_4 )]

		# Starting states
		_UpperCamelCase						    =	0x67452301
		_UpperCamelCase						    =	0xefcdab89
		_UpperCamelCase						    =	0x98badcfe
		_UpperCamelCase						    =	0x10325476

		_UpperCamelCase						    =	[
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		]

		# Process bit string in chunks, each with 16 32-char words
		for block_words in get_block_words(lowerCAmelCase ):
				_UpperCamelCase						    =	aa
				_UpperCamelCase						    =	ba
				_UpperCamelCase						    =	ca
				_UpperCamelCase						    =	da

				# Hash current chunk
				for i in range(6_4 ):
						if i <= 1_5:
								# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
								_UpperCamelCase						    =	d ^ (b & (c ^ d))
								_UpperCamelCase						    =	i
						elif i <= 3_1:
								# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
								_UpperCamelCase						    =	c ^ (d & (b ^ c))
								_UpperCamelCase						    =	(5 * i + 1) % 1_6
						elif i <= 4_7:
								_UpperCamelCase						    =	b ^ c ^ d
								_UpperCamelCase						    =	(3 * i + 5) % 1_6
						else:
								_UpperCamelCase						    =	c ^ (b | not_aa(lowerCAmelCase ))
								_UpperCamelCase						    =	(7 * i) % 1_6
						_UpperCamelCase						    =	(f + a + added_consts[i] + block_words[g]) % 2**3_2
						_UpperCamelCase						    =	d
						_UpperCamelCase						    =	c
						_UpperCamelCase						    =	b
						_UpperCamelCase						    =	sum_aa(lowerCAmelCase						,  left_rotate_aa(lowerCAmelCase						,  shift_amounts[i] ) )

				# Add hashed chunk to running total
				_UpperCamelCase						    =	sum_aa(lowerCAmelCase						,  lowerCAmelCase )
				_UpperCamelCase						    =	sum_aa(lowerCAmelCase						,  lowerCAmelCase )
				_UpperCamelCase						    =	sum_aa(lowerCAmelCase						,  lowerCAmelCase )
				_UpperCamelCase						    =	sum_aa(lowerCAmelCase						,  lowerCAmelCase )

		_UpperCamelCase						    =	reformat_hex(lowerCAmelCase ) + reformat_hex(lowerCAmelCase ) + reformat_hex(lowerCAmelCase ) + reformat_hex(lowerCAmelCase )
		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


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







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

								if audio_length_in_s is None:
										_UpperCamelCase						    =	self.unet.config.sample_size / self.unet.config.sample_rate

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	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}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (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."""				)
								_UpperCamelCase						    =	int(a				)

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

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

								# set step values
								self.scheduler.set_timesteps(a       ,							device=audio.device				)
								_UpperCamelCase						    =	self.scheduler.timesteps.to(a				)

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

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

								_UpperCamelCase						    =	audio.clamp(-1       ,							1				).float().cpu().numpy()

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



