






# 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		:
			__snake_case    :				Optional[Any]									=							42

			# setable values
			__snake_case    :				List[str]									=							42
			__snake_case    :				Union[str, Any]									=							42
			__snake_case    :				str									=							None







			@classmethod
			def 							UpperCamelCase				(     cls:  Tuple				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  Dict					) ->    Any:







									'''simple docstring'''




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



@dataclass
class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Tuple									=							42



class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase   ):
			__snake_case    :				Optional[int]									=							[e.name for e in FlaxKarrasDiffusionSchedulers]

			__snake_case    :				Any									=							42







			@property
			def 							UpperCamelCase				(     self:  int					) ->    List[str]:







									'''simple docstring'''




									return True







			@register_to_config
			def __init__(     self:  int				,     UpperCAmelCase_:  int = 1_000				,     UpperCAmelCase_:  List[Any] = 0.00_01				,     UpperCAmelCase_:  str = 0.02				,     UpperCAmelCase_:  Tuple = "linear"				,     UpperCAmelCase_:  List[str] = None				,     UpperCAmelCase_:  str = "fixed_small"				,     UpperCAmelCase_:  Optional[Any] = True				,     UpperCAmelCase_:  Dict = "epsilon"				,     UpperCAmelCase_:  Dict = jnp.floataa				,     ) ->    str:







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  dtype







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  int = None					) ->    DDPMSchedulerState:







									'''simple docstring'''




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

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

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

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







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  Optional[Any]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  Any = None					) ->    jnp.ndarray:







									'''simple docstring'''




									return sample







			def 							UpperCamelCase				(     self:  Dict				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Union[str, Any] = ()					) ->    DDPMSchedulerState:







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  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
									_SCREAMING_SNAKE_CASE     				=  (jnp.arange(0				,     _snake_case					) * step_ratio).round()[::-1]

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







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  str				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  Tuple=None				,     UpperCAmelCase_:  List[str]=None					) ->    str:







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  state.common.alphas_cumprod[t]
									_SCREAMING_SNAKE_CASE     				=  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
									_SCREAMING_SNAKE_CASE     				=  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

									return variance







			def 							UpperCamelCase				(     self:  Union[str, Any]				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  Tuple				,     UpperCAmelCase_:  List[Any] = None				,     UpperCAmelCase_:  Optional[Any] = True				,     ) ->    Union[FlaxDDPMSchedulerOutput, Tuple]:







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  timestep

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

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

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

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

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

									_SCREAMING_SNAKE_CASE     				=  pred_prev_sample + variance

									if not return_dict:
															return (pred_prev_sample, state)

									return FlaxDDPMSchedulerOutput(prev_sample=_snake_case				,     state=_snake_case					)







			def 							UpperCamelCase				(     self:  List[str]				,     UpperCAmelCase_:  Dict				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[Any]				,     UpperCAmelCase_:  List[str]				,     ) ->    jnp.ndarray:







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  Optional[int]				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[str]				,     UpperCAmelCase_:  Union[str, Any]				,     UpperCAmelCase_:  Union[str, Any]				,     ) ->    jnp.ndarray:







									'''simple docstring'''




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







			def __len__(     self:  Optional[Any]					) ->    Dict:







									'''simple docstring'''




									return self.config.num_train_timesteps









from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase			    =     {'''configuration_mmbt''': ['''MMBTConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase			    =     ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']


if TYPE_CHECKING:
		from .configuration_mmbt import MMBTConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings

else:
		import sys

		UpperCamelCase			    =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

