



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
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    :
	lowerCAmelCase	:   CommonSchedulerState

	# setable values
	lowerCAmelCase	:   jnp.ndarray
	lowerCAmelCase	:   jnp.ndarray
	lowerCAmelCase	:   Optional[int]				=	None



	@classmethod
	def 			__A							(       cls			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							return cls(common=UpperCAmelCase__			,	init_noise_sigma=UpperCAmelCase__			,	timesteps=UpperCAmelCase__		)






@dataclass
class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   DDPMSchedulerState






class      UpperCamelCase    (						_UpperCAmelCase       ,    _UpperCAmelCase     ):
	lowerCAmelCase	:   str				=	[e.name for e in FlaxKarrasDiffusionSchedulers]

	lowerCAmelCase	:   jnp.dtype



	@property
	def 			__A							(       self		):
							return True



	@register_to_config
	def __init__(       self			,	UpperCAmelCase__ = 1_000			,	UpperCAmelCase__ = 0.0_001			,	UpperCAmelCase__ = 0.02			,	UpperCAmelCase__ = "linear"			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = "fixed_small"			,	UpperCAmelCase__ = True			,	UpperCAmelCase__ = "epsilon"			,	UpperCAmelCase__ = jnp.floataa			,	):
							A__							=       dtype



	def 			__A							(       self			,	UpperCAmelCase__ = None		):
							if common is None:
													A__							=       CommonSchedulerState.create(self		)

							# standard deviation of the initial noise distribution
							A__							=       jnp.array(1.0			,	dtype=self.dtype		)

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

							return DDPMSchedulerState.create(
							    common=UpperCAmelCase__			,	init_noise_sigma=UpperCAmelCase__			,	timesteps=UpperCAmelCase__			,	)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None		):
							return sample



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = ()		):
							A__							=       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
							A__							=       (jnp.arange(0			,	UpperCAmelCase__		) * step_ratio).round()[::-1]

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



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__=None			,	UpperCAmelCase__=None		):
							A__							=       state.common.alphas_cumprod[t]
							A__							=       jnp.where(t > 0			,	state.common.alphas_cumprod[t - 1]			,	jnp.array(1.0			,	dtype=self.dtype		)		)

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							A__							=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
													A__							=       self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
													A__							=       jnp.clip(UpperCAmelCase__			,	a_min=1e-2_0		)
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
													A__							=       jnp.log(jnp.clip(UpperCAmelCase__			,	a_min=1e-2_0		)		)
							elif variance_type == "fixed_large":
													A__							=       state.common.betas[t]
							elif variance_type == "fixed_large_log":
													# Glide max_log
													A__							=       jnp.log(state.common.betas[t]		)
							elif variance_type == "learned":
													return predicted_variance
							elif variance_type == "learned_range":
													A__							=       variance
													A__							=       state.common.betas[t]
													A__							=       (predicted_variance + 1) / 2
													A__							=       frac * max_log + (1 - frac) * min_log

							return variance



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = True			,	):
							A__							=       timestep

							if key is None:
													A__							=       jax.random.PRNGKey(0		)

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
													A__						,       A__							=       jnp.split(UpperCAmelCase__			,	sample.shape[1]			,	axis=1		)
							else:
													A__							=       None

							# 1. compute alphas, betas
							A__							=       state.common.alphas_cumprod[t]
							A__							=       jnp.where(t > 0			,	state.common.alphas_cumprod[t - 1]			,	jnp.array(1.0			,	dtype=self.dtype		)		)
							A__							=       1 - alpha_prod_t
							A__							=       1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
													A__							=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
													A__							=       model_output
							elif self.config.prediction_type == "v_prediction":
													A__							=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
													raise ValueError(
													    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
													    " for the FlaxDDPMScheduler."		)

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
													A__							=       jnp.clip(UpperCAmelCase__			,	-1			,	1		)

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__							=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							A__							=       state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							A__							=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
													A__							=       jax.random.split(UpperCAmelCase__			,	num=1		)
													A__							=       jax.random.normal(UpperCAmelCase__			,	shape=model_output.shape			,	dtype=self.dtype		)
													return (self._get_variance(UpperCAmelCase__			,	UpperCAmelCase__			,	predicted_variance=UpperCAmelCase__		) ** 0.5) * noise

							A__							=       jnp.where(t > 0			,	random_variance()			,	jnp.zeros(model_output.shape			,	dtype=self.dtype		)		)

							A__							=       pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=UpperCAmelCase__			,	state=UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							return add_noise_common(state.common			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)



	def 			__A							(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							return get_velocity_common(state.common			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)



	def __len__(       self		):
							return self.config.num_train_timesteps










def  UpperCamelCase						(       _A			:				str    )->       str:





						"""simple docstring"""





						A__							=       ""
						for ch in key:
												if ch == " " or ch not in key_no_dups and ch.isalpha():
																		key_no_dups += ch
						return key_no_dups
def  UpperCamelCase						(       _A			:				str    )->       dict[str, str]:





						"""simple docstring"""





						A__							=       [chr(i + 65    ) for i in range(26    )]
						# Remove duplicate characters from key
						A__							=       remove_duplicates(key.upper()    )
						A__							=       len(_A    )
						# First fill cipher with key characters
						A__							=       {alphabet[i]: char for i, char in enumerate(_A    )}
						# Then map remaining characters in alphabet to
						# the alphabet from the beginning
						for i in range(len(_A    )			,							26    ):
												A__							=       alphabet[i - offset]
												# Ensure we are not mapping letters to letters previously mapped
												while char in key:
																		offset -= 1
																		A__							=       alphabet[i - offset]
												A__							=       char
						return cipher_alphabet
def  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, str]    )->       str:





						"""simple docstring"""





						return "".join(cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       _A			:				str			,							_A			:				dict[str, str]    )->       str:





						"""simple docstring"""





						A__							=       {v: k for k, v in cipher_map.items()}
						return "".join(rev_cipher_map.get(_A			,							_A    ) for ch in message.upper()    )
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						A__							=       input("Enter message to encode or decode: "    ).strip()
						A__							=       input("Enter keyword: "    ).strip()
						A__							=       input("Encipher or decipher? E/D:"    ).strip()[0].lower()
						try:
												A__							=       {"e": encipher, "d": decipher}[option]
						except KeyError:
												raise KeyError("invalid input option"    )
						A__							=       create_cipher_map(_A    )
						print(func(_A			,							_A    )    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()





