


# 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							:
	_a  :  CommonSchedulerState

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




	@classmethod
	def 							UpperCAmelCase__( cls						:		Dict		, _SCREAMING_SNAKE_CASE						:		CommonSchedulerState		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		)->	Optional[Any]:
			return cls(common=_SCREAMING_SNAKE_CASE		, init_noise_sigma=_SCREAMING_SNAKE_CASE		, timesteps=_SCREAMING_SNAKE_CASE		)





@dataclass
class  _a							(    _lowercase):
	_a  :  DDPMSchedulerState





class  _a							(    _lowercase					,						_lowercase):
	_a  :  Any				   =							[e.name for e in FlaxKarrasDiffusionSchedulers]

	_a  :  jnp.dtype




	@property
	def 							UpperCAmelCase__( self						:		Tuple		)->	Any:
			return True




	@register_to_config
	def __init__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		int = 1000		, _SCREAMING_SNAKE_CASE						:		float = 0.0001		, _SCREAMING_SNAKE_CASE						:		float = 0.02		, _SCREAMING_SNAKE_CASE						:		str = "linear"		, _SCREAMING_SNAKE_CASE						:		Optional[jnp.ndarray] = None		, _SCREAMING_SNAKE_CASE						:		str = "fixed_small"		, _SCREAMING_SNAKE_CASE						:		bool = True		, _SCREAMING_SNAKE_CASE						:		str = "epsilon"		, _SCREAMING_SNAKE_CASE						:		jnp.dtype = jnp.floataa		, )->	Optional[int]:
			lowerCAmelCase__       :      List[Any]   					= dtype




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Optional[CommonSchedulerState] = None		)->	DDPMSchedulerState:
			if common is None:
					lowerCAmelCase__       :      Optional[Any]   					= CommonSchedulerState.create(self		)

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

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

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




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		Optional[int] = None		)->	jnp.ndarray:
			return sample




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		Tuple = ()		)->	DDPMSchedulerState:
			lowerCAmelCase__       :      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
			lowerCAmelCase__       :      List[Any]   					= (jnp.arange(0		, _SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

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




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		, _SCREAMING_SNAKE_CASE						:		Dict=None		)->	Any:
			lowerCAmelCase__       :      int   					= state.common.alphas_cumprod[t]
			lowerCAmelCase__       :      List[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
			lowerCAmelCase__       :      List[Any]   					= (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

			return variance




	def 							UpperCAmelCase__( self						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		Optional[jax.random.KeyArray] = None		, _SCREAMING_SNAKE_CASE						:		bool = True		, )->	Union[FlaxDDPMSchedulerOutput, Tuple]:
			lowerCAmelCase__       :      Optional[Any]   					= timestep

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

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

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

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

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

			lowerCAmelCase__       :      Union[str, Any]   					= pred_prev_sample + variance

			if not return_dict:
					return (pred_prev_sample, state)

			return FlaxDDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		, state=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, )->	jnp.ndarray:
			return add_noise_common(state.common		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		DDPMSchedulerState		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, _SCREAMING_SNAKE_CASE						:		jnp.ndarray		, )->	jnp.ndarray:
			return get_velocity_common(state.common		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		)




	def __len__( self						:		Optional[int]		)->	str:
			return self.config.num_train_timesteps








from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}





class  _a							(    _lowercase):
	_a  :  List[Any]				   =							'''dpr'''




	def __init__( self						:		List[str]		, _SCREAMING_SNAKE_CASE						:		List[str]=3_0522		, _SCREAMING_SNAKE_CASE						:		Optional[int]=768		, _SCREAMING_SNAKE_CASE						:		List[str]=12		, _SCREAMING_SNAKE_CASE						:		Tuple=12		, _SCREAMING_SNAKE_CASE						:		str=3072		, _SCREAMING_SNAKE_CASE						:		Optional[Any]="gelu"		, _SCREAMING_SNAKE_CASE						:		Dict=0.1		, _SCREAMING_SNAKE_CASE						:		Optional[int]=0.1		, _SCREAMING_SNAKE_CASE						:		List[str]=512		, _SCREAMING_SNAKE_CASE						:		int=2		, _SCREAMING_SNAKE_CASE						:		Optional[Any]=0.02		, _SCREAMING_SNAKE_CASE						:		Tuple=1E-12		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=0		, _SCREAMING_SNAKE_CASE						:		List[str]="absolute"		, _SCREAMING_SNAKE_CASE						:		int = 0		, **_SCREAMING_SNAKE_CASE						:		Optional[Any]		, )->	Optional[int]:
			super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      Union[str, Any]   					= vocab_size
			lowerCAmelCase__       :      Tuple   					= hidden_size
			lowerCAmelCase__       :      Union[str, Any]   					= num_hidden_layers
			lowerCAmelCase__       :      List[Any]   					= num_attention_heads
			lowerCAmelCase__       :      Optional[int]   					= hidden_act
			lowerCAmelCase__       :      Optional[Any]   					= intermediate_size
			lowerCAmelCase__       :      Optional[Any]   					= hidden_dropout_prob
			lowerCAmelCase__       :      int   					= attention_probs_dropout_prob
			lowerCAmelCase__       :      str   					= max_position_embeddings
			lowerCAmelCase__       :      List[Any]   					= type_vocab_size
			lowerCAmelCase__       :      Optional[int]   					= initializer_range
			lowerCAmelCase__       :      List[str]   					= layer_norm_eps
			lowerCAmelCase__       :      Dict   					= projection_dim
			lowerCAmelCase__       :      int   					= position_embedding_type




