



# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 						UpperCAmelCase__     :




			'''simple docstring'''






			UpperCamelCase     		=			42
			UpperCamelCase     		=			42

			# setable values
			UpperCamelCase     		=			42
			UpperCamelCase     		=			42
			UpperCamelCase     		=			None




			@classmethod
			def 					snake_case__	(  cls				:					List[str]			,			a_				:					CommonSchedulerState			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			):
					'''simple docstring'''





					return cls(
					    common=a_			,			final_alpha_cumprod=a_			,			init_noise_sigma=a_			,			timesteps=a_			,			)

@dataclass
class 						UpperCAmelCase__     (     __UpperCamelCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			42

class 						UpperCAmelCase__     (     __UpperCamelCase		,__UpperCamelCase  ):




			'''simple docstring'''






			UpperCamelCase     		=			[e.name for e in FlaxKarrasDiffusionSchedulers]

			UpperCamelCase     		=			42




			@property
			def 					snake_case__	(  self				:					List[str]					):
					'''simple docstring'''





					return True




			@register_to_config
			def __init__(  self				:					Any			,			a_				:					int = 10_00			,			a_				:					float = 0.0_0_0_1			,			a_				:					float = 0.0_2			,			a_				:					str = "linear"			,			a_				:					Optional[jnp.ndarray] = None			,			a_				:					bool = True			,			a_				:					int = 0			,			a_				:					str = "epsilon"			,			a_				:					jnp.dtype = jnp.floataa			,			):
					'''simple docstring'''





					__UpperCAmelCase       :	List[str]						=      dtype




			def 					snake_case__	(  self				:					int			,			a_				:					Optional[CommonSchedulerState] = None					):
					'''simple docstring'''





					if common is None:
							__UpperCAmelCase       :	Dict						=      CommonSchedulerState.create(self					)

					# At every step in ddim, we are looking into the previous alphas_cumprod
					# For the final step, there is no previous alphas_cumprod because we are already at 0
					# `set_alpha_to_one` decides whether we set this parameter simply to one or
					# whether we use the final alpha of the "non-previous" one.
					__UpperCAmelCase       :	List[str]						=      (
					    jnp.array(1.0			,			dtype=self.dtype					) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
					)

					# standard deviation of the initial noise distribution
					__UpperCAmelCase       :	List[str]						=      jnp.array(1.0			,			dtype=self.dtype					)

					__UpperCAmelCase       :	List[Any]						=      jnp.arange(0			,			self.config.num_train_timesteps					).round()[::-1]

					return DDIMSchedulerState.create(
					    common=a_			,			final_alpha_cumprod=a_			,			init_noise_sigma=a_			,			timesteps=a_			,			)




			def 					snake_case__	(  self				:					str			,			a_				:					DDIMSchedulerState			,			a_				:					jnp.ndarray			,			a_				:					Optional[int] = None					):
					'''simple docstring'''





					return sample




			def 					snake_case__	(  self				:					List[Any]			,			a_				:					DDIMSchedulerState			,			a_				:					int			,			a_				:					Tuple = ()					):
					'''simple docstring'''





					__UpperCAmelCase       :	int						=      self.config.num_train_timesteps // num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# rounding to avoid issues when num_inference_step is power of 3
					__UpperCAmelCase       :	Any						=      (jnp.arange(0			,			a_					) * step_ratio).round()[::-1] + self.config.steps_offset

					return state.replace(
					    num_inference_steps=a_			,			timesteps=a_			,			)




			def 					snake_case__	(  self				:					Dict			,			a_				:					DDIMSchedulerState			,			a_				:					List[Any]			,			a_				:					Optional[Any]					):
					'''simple docstring'''





					__UpperCAmelCase       :	List[Any]						=      state.common.alphas_cumprod[timestep]
					__UpperCAmelCase       :	Any						=      jnp.where(
					    prev_timestep >= 0			,			state.common.alphas_cumprod[prev_timestep]			,			state.final_alpha_cumprod					)
					__UpperCAmelCase       :	List[Any]						=      1 - alpha_prod_t
					__UpperCAmelCase       :	Optional[Any]						=      1 - alpha_prod_t_prev

					__UpperCAmelCase       :	Optional[int]						=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

					return variance




			def 					snake_case__	(  self				:					int			,			a_				:					DDIMSchedulerState			,			a_				:					jnp.ndarray			,			a_				:					int			,			a_				:					jnp.ndarray			,			a_				:					float = 0.0			,			a_				:					bool = True			,			):
					'''simple docstring'''





					if state.num_inference_steps is None:
							raise ValueError(
							    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''					)

					# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
					# Ideally, read DDIM paper in-detail understanding

					# Notation (<variable name> -> <name in paper>
					# - pred_noise_t -> e_theta(x_t, t)
					# - pred_original_sample -> f_theta(x_t, t) or x_0
					# - std_dev_t -> sigma_t
					# - eta -> η
					# - pred_sample_direction -> "direction pointing to x_t"
					# - pred_prev_sample -> "x_t-1"

					# 1. get previous step value (=t-1)
					__UpperCAmelCase       :	Optional[int]						=      timestep - self.config.num_train_timesteps // state.num_inference_steps

					__UpperCAmelCase       :	Tuple						=      state.common.alphas_cumprod
					__UpperCAmelCase       :	List[Any]						=      state.final_alpha_cumprod

					# 2. compute alphas, betas
					__UpperCAmelCase       :	List[str]						=      alphas_cumprod[timestep]
					__UpperCAmelCase       :	List[Any]						=      jnp.where(prev_timestep >= 0			,			alphas_cumprod[prev_timestep]			,			a_					)

					__UpperCAmelCase       :	int						=      1 - alpha_prod_t

					# 3. compute predicted original sample from predicted noise also called
					# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					if self.config.prediction_type == "epsilon":
							__UpperCAmelCase       :	Any						=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							__UpperCAmelCase       :	List[str]						=      model_output
					elif self.config.prediction_type == "sample":
							__UpperCAmelCase       :	List[str]						=      model_output
							__UpperCAmelCase       :	Union[str, Any]						=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
							__UpperCAmelCase       :	List[Any]						=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							__UpperCAmelCase       :	Optional[int]						=      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
					else:
							raise ValueError(
							    F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
							    ''' `v_prediction`'''					)

					# 4. compute variance: "sigma_t(η)" -> see formula (16)
					# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
					__UpperCAmelCase       :	Dict						=      self._get_variance(a_			,			a_			,			a_					)
					__UpperCAmelCase       :	str						=      eta * variance ** 0.5

					# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					__UpperCAmelCase       :	Any						=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

					# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					__UpperCAmelCase       :	Optional[Any]						=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if not return_dict:
							return (prev_sample, state)

					return FlaxDDIMSchedulerOutput(prev_sample=a_			,			state=a_					)




			def 					snake_case__	(  self				:					Union[str, Any]			,			a_				:					DDIMSchedulerState			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			):
					'''simple docstring'''





					return add_noise_common(state.common			,			a_			,			a_			,			a_					)




			def 					snake_case__	(  self				:					Optional[int]			,			a_				:					DDIMSchedulerState			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			a_				:					jnp.ndarray			,			):
					'''simple docstring'''





					return get_velocity_common(state.common			,			a_			,			a_			,			a_					)




			def __len__(  self				:					Tuple					):
					'''simple docstring'''





					return self.config.num_train_timesteps










import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





		if size_par + len(_UpperCAmelCase		) <= 2**size_par - (len(_UpperCAmelCase		) - 1):
				raise ValueError('''size of parity don\'t match with size of data'''		)

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
						else:
								data_out_gab.append('''D'''		)
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		return data_out

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]		)
				else:
						parity_received.append(data[cont_data]		)
				cont_data += 1

		# -----------calculates the parity with the data
		__UpperCAmelCase       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Dict						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2		)		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





