




# 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    :






			'''simple docstring'''





			UpperCamelCase__								=42

			# setable values
			UpperCamelCase__								=42
			UpperCamelCase__								=42
			UpperCamelCase__								=None

			@classmethod
			def 							UpperCAmelCase__       ( cls			:   Dict       ,						lowerCamelCase_			:   CommonSchedulerState       ,						lowerCamelCase_			:   jnp.ndarray       ,						lowerCamelCase_			:   jnp.ndarray  )						->   List[str]:
								return cls(common=lowerCamelCase_       ,						init_noise_sigma=lowerCamelCase_       ,						timesteps=lowerCamelCase_  )






@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=42






class    lowerCamelCase    (		_lowerCamelCase				,_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[e.name for e in FlaxKarrasDiffusionSchedulers]

			UpperCamelCase__								=42

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

			@register_to_config
			def __init__( self			:   Tuple       ,						lowerCamelCase_			:   int = 1000       ,						lowerCamelCase_			:   float = 0.0_0_0_1       ,						lowerCamelCase_			:   float = 0.0_2       ,						lowerCamelCase_			:   str = "linear"       ,						lowerCamelCase_			:   Optional[jnp.ndarray] = None       ,						lowerCamelCase_			:   str = "fixed_small"       ,						lowerCamelCase_			:   bool = True       ,						lowerCamelCase_			:   str = "epsilon"       ,						lowerCamelCase_			:   jnp.dtype = jnp.floataa       ,						)						->   List[str]:
								__magic_name__							:	List[str]    							=							dtype

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Optional[CommonSchedulerState] = None  )						->   DDPMSchedulerState:
								if common is None:
													__magic_name__							:	Tuple    							=							CommonSchedulerState.create(self  )

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

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

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

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

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						lowerCamelCase_			:   DDPMSchedulerState       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   Tuple = ()  )						->   DDPMSchedulerState:
								__magic_name__							:	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
								__magic_name__							:	str    							=							(jnp.arange(0       ,						lowerCamelCase_  ) * step_ratio).round()[::-1]

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

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

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

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

								return variance

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

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

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

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

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

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

								__magic_name__							:	Tuple    							=							pred_prev_sample + variance

								if not return_dict:
													return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase_       ,						state=lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   DDPMSchedulerState       ,						lowerCamelCase_			:   jnp.ndarray       ,						lowerCamelCase_			:   jnp.ndarray       ,						lowerCamelCase_			:   jnp.ndarray       ,						)						->   jnp.ndarray:
								return add_noise_common(state.common       ,						lowerCamelCase_       ,						lowerCamelCase_       ,						lowerCamelCase_  )

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


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









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



