from collections.abc import Generator
from math import sin
def 							lowerCAmelCase(     __lowerCamelCase						):
					if len(__lowerCamelCase						) != 32:
										raise ValueError('Input must be of length 32'						)

					__a		 =       b''
					for i in [3, 2, 1, 0]:
										little_endian += string_aa[8 * i : 8 * i + 8]
					return little_endian
def 							lowerCAmelCase(     __lowerCamelCase						):
					if i < 0:
										raise ValueError('Input must be non-negative'						)

					__a		 =       format(__lowerCamelCase				,    '08x'						)[-8:]
					__a		 =       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 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       b''
					for char in message:
										bit_string += format(__lowerCamelCase				,    '08b'						).encode('utf-8'						)
					__a		 =       format(len(__lowerCamelCase						)				,    '064b'						).encode('utf-8'						)

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

					return bit_string
def 							lowerCAmelCase(     __lowerCamelCase						):
					if len(__lowerCamelCase						) % 512 != 0:
										raise ValueError('Input must have length that\'s a multiple of 512'						)

					for pos in range(0				,    len(__lowerCamelCase						)				,    512						):
										__a		 =       bit_string[pos : pos + 512]
										__a		 =       []
										for i in range(0				,    512				,    32						):
															block_words.append(int(to_little_endian(block[i : i + 32]						)				,    2						)						)
										yield block_words
def 							lowerCAmelCase(     __lowerCamelCase						):
					if i < 0:
										raise ValueError('Input must be non-negative'						)

					__a		 =       format(__lowerCamelCase				,    '032b'						)
					__a		 =       ''
					for c in i_str:
										new_str += "1" if c == "0" else "0"
					return int(__lowerCamelCase				,    2						)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					return (a + b) % 2**32
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					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 							lowerCAmelCase(     __lowerCamelCase						):
					__a		 =       preprocess(__lowerCamelCase						)

					__a		 =       [int(2**32 * abs(sin(i + 1						)						)						) for i in range(64						)]

					# Starting states
					__a		 =       0X6745_2301
					__a		 =       0XEFCD_AB89
					__a		 =       0X98BA_DCFE
					__a		 =       0X1032_5476

					__a		 =       [
					    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(__lowerCamelCase						):
										__a		 =       aa
										__a		 =       ba
										__a		 =       ca
										__a		 =       da

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

										# Add hashed chunk to running total
										__a		 =       sum_aa(__lowerCamelCase				,    __lowerCamelCase						)
										__a		 =       sum_aa(__lowerCamelCase				,    __lowerCamelCase						)
										__a		 =       sum_aa(__lowerCamelCase				,    __lowerCamelCase						)
										__a		 =       sum_aa(__lowerCamelCase				,    __lowerCamelCase						)

					__a		 =       reformat_hex(__lowerCamelCase						) + reformat_hex(__lowerCamelCase						) + reformat_hex(__lowerCamelCase						) + reformat_hex(__lowerCamelCase						)
					return digest


if __name__ == "__main__":
	import doctest

	doctest.testmod()





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()




