




"""simple docstring"""





import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


A   =       logging.get_logger(__name__)

A   =       """T5Config"""

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			jnp.ndarray:


			"""simple docstring"""
			__UpperCAmelCase	: Union[str, Any]		=					jnp.zeros_like(UpperCamelCase							)
			__UpperCAmelCase	: List[str]		=					shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]							)
			__UpperCAmelCase	: List[Any]		=					shifted_input_ids.at[:, 0].set(UpperCamelCase							)

			__UpperCAmelCase	: List[Any]		=					jnp.where(shifted_input_ids == -100				,       UpperCamelCase				,       UpperCamelCase							)
			return shifted_input_ids



class      a__				(	__magic_name__   ):
			lowercase_    =	"mt5"
			lowercase_    =	MTaConfig






class      a__				(	__magic_name__   ):
			lowercase_    =	"mt5"
			lowercase_    =	MTaConfig






class      a__				(	__magic_name__   ):
			lowercase_    =	"mt5"
			lowercase_    =	MTaConfig








"""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      a__				:
			lowercase_    =	42

			# setable values
			lowercase_    =	42
			lowercase_    =	42
			lowercase_    =	None


			@classmethod
			def       a_     (      cls      :    List[str]      ,  UpperCamelCase_      :    CommonSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray):


						"""simple docstring"""




						return cls(common=UpperCamelCase_      ,  init_noise_sigma=UpperCamelCase_      ,  timesteps=UpperCamelCase_)






@dataclass
class      a__				(	__magic_name__   ):
			lowercase_    =	42






class      a__				(	__magic_name__							,    __magic_name__   ):
			lowercase_    =	[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowercase_    =	42


			@property
			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						return True


			@register_to_config
			def __init__(      self      :    str      ,  UpperCamelCase_      :    int = 1000      ,  UpperCamelCase_      :    float = 0.0001      ,  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      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[int]		=					dtype


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    Optional[CommonSchedulerState] = None):


						"""simple docstring"""




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

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

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

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


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[int] = None):


						"""simple docstring"""




						return sample


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    Tuple = ()):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					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	: List[str]		=					(jnp.arange(0      ,  UpperCamelCase_) * step_ratio).round()[::-1]

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


			def       a_     (      self      :    Any      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Union[str, Any]=None      ,  UpperCamelCase_      :    Optional[int]=None):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: Optional[int]		=					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	: Tuple		=					(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

						return variance


			def       a_     (      self      :    Optional[int]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    int      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    Optional[jax.random.KeyArray] = None      ,  UpperCamelCase_      :    bool = True      ,  ):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					timestep

						if key is None:
									__UpperCAmelCase	: List[str]		=					jax.random.PRNGKey(0)

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

						# 1. compute alphas, betas
						__UpperCAmelCase	: str		=					state.common.alphas_cumprod[t]
						__UpperCAmelCase	: str		=					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	: Any		=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif self.config.prediction_type == "sample":
									__UpperCAmelCase	: Optional[int]		=					model_output
						elif self.config.prediction_type == "v_prediction":
									__UpperCAmelCase	: Optional[int]		=					(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	: List[Any]		=					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
						__UpperCAmelCase	: Union[str, Any]		=					(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
						__UpperCAmelCase	: Union[str, 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	: Optional[Any]		=					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

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

						__UpperCAmelCase	: Tuple		=					pred_prev_sample + variance

						if not return_dict:
									return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase_      ,  state=UpperCamelCase_)


			def       a_     (      self      :    Optional[Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




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


			def       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    DDPMSchedulerState      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  UpperCamelCase_      :    jnp.ndarray      ,  ):


						"""simple docstring"""




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


			def __len__(      self      :    int):


						"""simple docstring"""




						return self.config.num_train_timesteps


