


# 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        lowerCamelCase      :
	_lowerCAmelCase    :		CommonSchedulerState

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


	@classmethod
	def    A(						cls							,    lowercase__							,    lowercase__							,    lowercase__):
							return cls(common=lowercase__							,    init_noise_sigma=lowercase__							,    timesteps=lowercase__)






@dataclass
class        lowerCamelCase      (     _UpperCamelCase   ):
	_lowerCAmelCase    :		DDPMSchedulerState






class        lowerCamelCase      (     _UpperCamelCase       ,	_UpperCamelCase   ):
	_lowerCAmelCase    :		Dict        =	[e.name for e in FlaxKarrasDiffusionSchedulers]

	_lowerCAmelCase    :		jnp.dtype


	@property
	def    A(						self):
							return True


	@register_to_config
	def __init__(						self							,    lowercase__ = 1_0_0_0							,    lowercase__ = 0.0_0_0_1							,    lowercase__ = 0.0_2							,    lowercase__ = "linear"							,    lowercase__ = None							,    lowercase__ = "fixed_small"							,    lowercase__ = True							,    lowercase__ = "epsilon"							,    lowercase__ = jnp.floataa							,    ):
							__UpperCAmelCase :       Tuple  						=  dtype


	def    A(						self							,    lowercase__ = None):
							if common is None:
													__UpperCAmelCase :       Any  						=  CommonSchedulerState.create(self)

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

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

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = None):
							return sample


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__ = ()):
							__UpperCAmelCase :       Tuple  						=  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 :       Tuple  						=  (jnp.arange(0							,    lowercase__) * step_ratio).round()[::-1]

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


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__=None							,    lowercase__=None):
							__UpperCAmelCase :       List[str]  						=  state.common.alphas_cumprod[t]
							__UpperCAmelCase :       Optional[Any]  						=  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
							__UpperCAmelCase :       Union[str, Any]  						=  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
													__UpperCAmelCase :       List[Any]  						=  self.config.variance_type

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

							return variance


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       int  						=  timestep

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

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

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

							# 6. Add noise
							def random_variance():
													__UpperCAmelCase :       Optional[Any]  						=  jax.random.split(lowercase__							,    num=1)
													__UpperCAmelCase :       str  						=  jax.random.normal(lowercase__							,    shape=model_output.shape							,    dtype=self.dtype)
													return (self._get_variance(lowercase__							,    lowercase__							,    predicted_variance=lowercase__) ** 0.5) * noise

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

							__UpperCAmelCase :       int  						=  pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=lowercase__							,    state=lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							return add_noise_common(state.common							,    lowercase__							,    lowercase__							,    lowercase__)


	def    A(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    ):
							return get_velocity_common(state.common							,    lowercase__							,    lowercase__							,    lowercase__)


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





from typing import TYPE_CHECKING

from ....utils import _LazyModule


lowerCAmelCase							=						{"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
				from .tokenization_tapex import TapexTokenizer


else:
				import sys

				lowerCAmelCase							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)

