



'''simple docstring'''

# 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       __magic_name__		:
							lowerCAmelCase			:       CommonSchedulerState

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







							@classmethod
							def 				__lowercase     (				cls   :			Optional[Any]      ,_UpperCAmelCase   :			CommonSchedulerState      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			jnp.ndarray						):
									return cls(common=_UpperCAmelCase      ,init_noise_sigma=_UpperCAmelCase      ,timesteps=_UpperCAmelCase						)

@dataclass
class       __magic_name__		(       _UpperCamelCase  ):
							lowerCAmelCase			:       DDPMSchedulerState

class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase  ):
							lowerCAmelCase			:       Any  							=   [e.name for e in FlaxKarrasDiffusionSchedulers]

							lowerCAmelCase			:       jnp.dtype







							@property
							def 				__lowercase     (				self   :			Any						):
									return True







							@register_to_config
							def __init__(				self   :			Optional[Any]      ,_UpperCAmelCase   :			int = 1000      ,_UpperCAmelCase   :			float = 0.00_01      ,_UpperCAmelCase   :			float = 0.02      ,_UpperCAmelCase   :			str = "linear"      ,_UpperCAmelCase   :			Optional[jnp.ndarray] = None      ,_UpperCAmelCase   :			str = "fixed_small"      ,_UpperCAmelCase   :			bool = True      ,_UpperCAmelCase   :			str = "epsilon"      ,_UpperCAmelCase   :			jnp.dtype = jnp.floataa      ,):
									_a	: List[Any]								=				dtype







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Optional[CommonSchedulerState] = None						):
									if common is None:
											_a	: str								=				CommonSchedulerState.create(self						)

									# standard deviation of the initial noise distribution
									_a	: Optional[int]								=				jnp.array(1.0      ,dtype=self.dtype						)

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

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







							def 				__lowercase     (				self   :			Optional[int]      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			Optional[int] = None						):
									return sample







							def 				__lowercase     (				self   :			Tuple      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			Tuple = ()						):
									_a	: 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
									_a	: Optional[int]								=				(jnp.arange(0      ,_UpperCAmelCase						) * step_ratio).round()[::-1]

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







							def 				__lowercase     (				self   :			Union[str, Any]      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			int=None      ,_UpperCAmelCase   :			List[Any]=None						):
									_a	: Optional[Any]								=				state.common.alphas_cumprod[t]
									_a	: str								=				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	: Dict								=				(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

									if variance_type is None:
											_a	: Tuple								=				self.config.variance_type

									# hacks - were probably added for training stability
									if variance_type == "fixed_small":
											_a	: Dict								=				jnp.clip(_UpperCAmelCase      ,a_min=1E-20						)
									# for rl-diffuser https://arxiv.org/abs/2205.09991
									elif variance_type == "fixed_small_log":
											_a	: List[Any]								=				jnp.log(jnp.clip(_UpperCAmelCase      ,a_min=1E-20						)						)
									elif variance_type == "fixed_large":
											_a	: Optional[Any]								=				state.common.betas[t]
									elif variance_type == "fixed_large_log":
											# Glide max_log
											_a	: Optional[Any]								=				jnp.log(state.common.betas[t]						)
									elif variance_type == "learned":
											return predicted_variance
									elif variance_type == "learned_range":
											_a	: Union[str, Any]								=				variance
											_a	: Optional[Any]								=				state.common.betas[t]
											_a	: Dict								=				(predicted_variance + 1) / 2
											_a	: List[Any]								=				frac * max_log + (1 - frac) * min_log

									return variance







							def 				__lowercase     (				self   :			Any      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			int      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			Optional[jax.random.KeyArray] = None      ,_UpperCAmelCase   :			bool = True      ,):
									_a	: int								=				timestep

									if key is None:
											_a	: Optional[Any]								=				jax.random.PRNGKey(0						)

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

									# 1. compute alphas, betas
									_a	: str								=				state.common.alphas_cumprod[t]
									_a	: List[str]								=				jnp.where(t > 0      ,state.common.alphas_cumprod[t - 1]      ,jnp.array(1.0      ,dtype=self.dtype						)						)
									_a	: Dict								=				1 - alpha_prod_t
									_a	: List[str]								=				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	: Optional[int]								=				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									elif self.config.prediction_type == "sample":
											_a	: Optional[int]								=				model_output
									elif self.config.prediction_type == "v_prediction":
											_a	: List[Any]								=				(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	: str								=				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	: Any								=				(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
									_a	: Any								=				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	: Optional[Any]								=				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

									# 6. Add noise
									def random_variance():
											_a	: List[str]								=				jax.random.split(_UpperCAmelCase      ,num=1						)
											_a	: Dict								=				jax.random.normal(_UpperCAmelCase      ,shape=model_output.shape      ,dtype=self.dtype						)
											return (self._get_variance(_UpperCAmelCase      ,_UpperCAmelCase      ,predicted_variance=_UpperCAmelCase						) ** 0.5) * noise

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

									_a	: Any								=				pred_prev_sample + variance

									if not return_dict:
											return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase      ,state=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[Any]      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			jnp.ndarray      ,):
									return add_noise_common(state.common      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			List[str]      ,_UpperCAmelCase   :			DDPMSchedulerState      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			jnp.ndarray      ,_UpperCAmelCase   :			jnp.ndarray      ,):
									return get_velocity_common(state.common      ,_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)







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









'''simple docstring'''

import re

from filelock import FileLock


try:
							import nltk

							__lowerCAmelCase		     =  True
except (ImportError, ModuleNotFoundError):
							__lowerCAmelCase		     =  False

if NLTK_AVAILABLE:
							with FileLock('''.lock''') as lock:
														nltk.download('''punkt''', quiet=True)



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> str:
		re.sub('<n>'		,     ''		,     lowerCAmelCase_    )  # remove pegasus newline char
		assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
		return "\n".join(nltk.sent_tokenize(lowerCAmelCase_    )    )




