


"""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 			lowerCamelCase__						:
						lowerCAmelCase       =     42

						# setable values
						lowerCAmelCase       =     42
						lowerCAmelCase       =     42
						lowerCAmelCase       =     None







						@classmethod
						def __a						(							cls   :    Any			,			_lowercase   :    CommonSchedulerState			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    jnp.ndarray		):
								return cls(common=_lowercase			,			init_noise_sigma=_lowercase			,			timesteps=_lowercase		)



@dataclass
class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     42



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     [e.name for e in FlaxKarrasDiffusionSchedulers]

						lowerCAmelCase       =     42







						@property
						def __a						(							self   :    Dict		):
								return True







						@register_to_config
						def __init__(							self   :    Any			,			_lowercase   :    int = 1_000			,			_lowercase   :    float = 0.0_0_0_1			,			_lowercase   :    float = 0.0_2			,			_lowercase   :    str = "linear"			,			_lowercase   :    Optional[jnp.ndarray] = None			,			_lowercase   :    str = "fixed_small"			,			_lowercase   :    bool = True			,			_lowercase   :    str = "epsilon"			,			_lowercase   :    jnp.dtype = jnp.floataa			,			):
								A					       = dtype







						def __a						(							self   :    Union[str, Any]			,			_lowercase   :    Optional[CommonSchedulerState] = None		):
								if common is None:
										A					       = CommonSchedulerState.create(self		)

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

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

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







						def __a						(							self   :    int			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    Optional[int] = None		):
								return sample







						def __a						(							self   :    Any			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    int			,			_lowercase   :    Tuple = ()		):
								A					       = 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
								A					       = (jnp.arange(0			,			_lowercase		) * step_ratio).round()[::-1]

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







						def __a						(							self   :    str			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    List[Any]			,			_lowercase   :    Union[str, Any]=None			,			_lowercase   :    int=None		):
								A					       = state.common.alphas_cumprod[t]
								A					       = 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
								A					       = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

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

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

								return variance







						def __a						(							self   :    str			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    int			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    Optional[jax.random.KeyArray] = None			,			_lowercase   :    bool = True			,			):
								A					       = timestep

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

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

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

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

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

								A					       = pred_prev_sample + variance

								if not return_dict:
										return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=_lowercase			,			state=_lowercase		)







						def __a						(							self   :    List[Any]			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    jnp.ndarray			,			):
								return add_noise_common(state.common			,			_lowercase			,			_lowercase			,			_lowercase		)







						def __a						(							self   :    List[Any]			,			_lowercase   :    DDPMSchedulerState			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    jnp.ndarray			,			_lowercase   :    jnp.ndarray			,			):
								return get_velocity_common(state.common			,			_lowercase			,			_lowercase			,			_lowercase		)







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









"""simple docstring"""


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


UpperCamelCase						:				Tuple             =      logging.get_logger(__name__)

UpperCamelCase						:				Union[str, Any]             =      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """lilt"""







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Dict=30_522			,			_lowercase   :    Any=768			,			_lowercase   :    Union[str, Any]=12			,			_lowercase   :    List[str]=12			,			_lowercase   :    str=3_072			,			_lowercase   :    int="gelu"			,			_lowercase   :    Union[str, Any]=0.1			,			_lowercase   :    Dict=0.1			,			_lowercase   :    Optional[Any]=512			,			_lowercase   :    Optional[Any]=2			,			_lowercase   :    Tuple=0.0_2			,			_lowercase   :    int=1e-12			,			_lowercase   :    Any=0			,			_lowercase   :    List[str]="absolute"			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=4			,			_lowercase   :    Optional[int]=1_024			,			**_lowercase   :    Union[str, Any]			,			):
								super().__init__(pad_token_id=_lowercase			,			**_lowercase		)

								A					       = vocab_size
								A					       = hidden_size
								A					       = num_hidden_layers
								A					       = num_attention_heads
								A					       = hidden_act
								A					       = intermediate_size
								A					       = hidden_dropout_prob
								A					       = attention_probs_dropout_prob
								A					       = max_position_embeddings
								A					       = type_vocab_size
								A					       = initializer_range
								A					       = layer_norm_eps
								A					       = position_embedding_type
								A					       = classifier_dropout
								A					       = channel_shrink_ratio
								A					       = max_ad_position_embeddings





