






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
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      __SCREAMING_SNAKE_CASE       :
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       42
						_SCREAMING_SNAKE_CASE						:							Tuple				=       42

						# setable values
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       42
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       42
						_SCREAMING_SNAKE_CASE						:							Union[str, Any]				=       None






						@classmethod
						def    UpperCamelCase__   (						cls  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    ):






												"""simple docstring"""
												return cls(
												    common=SCREAMING_SNAKE_CASE_  ,    final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,    init_noise_sigma=SCREAMING_SNAKE_CASE_  ,    timesteps=SCREAMING_SNAKE_CASE_  ,    )




@dataclass
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       42




class      __SCREAMING_SNAKE_CASE       ( __lowercase		,       __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       [e.name for e in FlaxKarrasDiffusionSchedulers]

						_SCREAMING_SNAKE_CASE						:							Any				=       42






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return True






						@register_to_config
						def __init__(						self  ,    _UpperCamelCase = 10_00  ,    _UpperCamelCase = 0.00_01  ,    _UpperCamelCase = 0.02  ,    _UpperCamelCase = "linear"  ,    _UpperCamelCase = None  ,    _UpperCamelCase = True  ,    _UpperCamelCase = 0  ,    _UpperCamelCase = "epsilon"  ,    _UpperCamelCase = jnp.floataa  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    dtype






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase = None ):






												"""simple docstring"""
												if common is None:
																		lowerCAmelCase__         =    CommonSchedulerState.create(self )

												# At every step in ddim, we are looking into the previous alphas_cumprod
												# For the final step, there is no previous alphas_cumprod because we are already at 0
												# `set_alpha_to_one` decides whether we set this parameter simply to one or
												# whether we use the final alpha of the "non-previous" one.
												lowerCAmelCase__         =    (
												    jnp.array(1.0  ,    dtype=self.dtype ) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
												)

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

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

												return DDIMSchedulerState.create(
												    common=SCREAMING_SNAKE_CASE_  ,    final_alpha_cumprod=SCREAMING_SNAKE_CASE_  ,    init_noise_sigma=SCREAMING_SNAKE_CASE_  ,    timesteps=SCREAMING_SNAKE_CASE_  ,    )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = None ):






												"""simple docstring"""
												return sample






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = () ):






												"""simple docstring"""
												lowerCAmelCase__         =    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__         =    (jnp.arange(0  ,    SCREAMING_SNAKE_CASE_ ) * step_ratio).round()[::-1] + self.config.steps_offset

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






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    state.common.alphas_cumprod[timestep]
												lowerCAmelCase__         =    jnp.where(
												    prev_timestep >= 0  ,    state.common.alphas_cumprod[prev_timestep]  ,    state.final_alpha_cumprod )
												lowerCAmelCase__         =    1 - alpha_prod_t
												lowerCAmelCase__         =    1 - alpha_prod_t_prev

												lowerCAmelCase__         =    (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase = 0.0  ,    _UpperCamelCase = True  ,    ):






												"""simple docstring"""
												if state.num_inference_steps is None:
																		raise ValueError(
																		    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler' )

												# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
												# Ideally, read DDIM paper in-detail understanding

												# Notation (<variable name> -> <name in paper>
												# - pred_noise_t -> e_theta(x_t, t)
												# - pred_original_sample -> f_theta(x_t, t) or x_0
												# - std_dev_t -> sigma_t
												# - eta -> η
												# - pred_sample_direction -> "direction pointing to x_t"
												# - pred_prev_sample -> "x_t-1"

												# 1. get previous step value (=t-1)
												lowerCAmelCase__         =    timestep - self.config.num_train_timesteps // state.num_inference_steps

												lowerCAmelCase__         =    state.common.alphas_cumprod
												lowerCAmelCase__         =    state.final_alpha_cumprod

												# 2. compute alphas, betas
												lowerCAmelCase__         =    alphas_cumprod[timestep]
												lowerCAmelCase__         =    jnp.where(prev_timestep >= 0  ,    alphas_cumprod[prev_timestep]  ,    SCREAMING_SNAKE_CASE_ )

												lowerCAmelCase__         =    1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																		lowerCAmelCase__         =    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		lowerCAmelCase__         =    model_output
												elif self.config.prediction_type == "sample":
																		lowerCAmelCase__         =    model_output
																		lowerCAmelCase__         =    (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																		lowerCAmelCase__         =    (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		lowerCAmelCase__         =    (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																		raise ValueError(
																		    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																		    ' `v_prediction`' )

												# 4. compute variance: "sigma_t(η)" -> see formula (16)
												# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
												lowerCAmelCase__         =    self._get_variance(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )
												lowerCAmelCase__         =    eta * variance ** 0.5

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowerCAmelCase__         =    (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowerCAmelCase__         =    alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																		return (prev_sample, state)

												return FlaxDDIMSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_  ,    state=SCREAMING_SNAKE_CASE_ )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    ):






												"""simple docstring"""
												return add_noise_common(state.common  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    ):






												"""simple docstring"""
												return get_velocity_common(state.common  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_ )







						def __len__(						self ):






												"""simple docstring"""
												return self.config.num_train_timesteps







from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_				:							Any       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""deberta"""

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=50_265							,       SCREAMING_SNAKE_CASE_=768							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=12							,       SCREAMING_SNAKE_CASE_=3_072							,       SCREAMING_SNAKE_CASE_="gelu"							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=0.0_2							,       SCREAMING_SNAKE_CASE_=1E-7							,       SCREAMING_SNAKE_CASE_=False							,       SCREAMING_SNAKE_CASE_=-1							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_="gelu"							,       **SCREAMING_SNAKE_CASE_							,       ):
            super().__init__(**SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   hidden_size
            __snake_case						       =   num_hidden_layers
            __snake_case						       =   num_attention_heads
            __snake_case						       =   intermediate_size
            __snake_case						       =   hidden_act
            __snake_case						       =   hidden_dropout_prob
            __snake_case						       =   attention_probs_dropout_prob
            __snake_case						       =   max_position_embeddings
            __snake_case						       =   type_vocab_size
            __snake_case						       =   initializer_range
            __snake_case						       =   relative_attention
            __snake_case						       =   max_relative_positions
            __snake_case						       =   pad_token_id
            __snake_case						       =   position_biased_input

            # Backwards compatibility
            if type(SCREAMING_SNAKE_CASE_    ) == str:
                   __snake_case						       =   [x.strip() for x in pos_att_type.lower().split('|'    )]

            __snake_case						       =   pos_att_type
            __snake_case						       =   vocab_size
            __snake_case						       =   layer_norm_eps

            __snake_case						       =   kwargs.get('pooler_hidden_size'							,       SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   pooler_dropout
            __snake_case						       =   pooler_hidden_act







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            if self.task == "multiple-choice":
                   __snake_case						       =   {0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'sequence'}
            if self._config.type_vocab_size > 0:
                   return OrderedDict(
                       [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]    )
            else:
                   return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]    )

     @property
     def 						__lowerCamelCase				(  self    ):
            return 12

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = -1							,       SCREAMING_SNAKE_CASE_ = False							,       SCREAMING_SNAKE_CASE_ = None							,       SCREAMING_SNAKE_CASE_ = 3							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = 40							,       SCREAMING_SNAKE_CASE_ = None							,       ):
            __snake_case						       =   super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_							,       framework=SCREAMING_SNAKE_CASE_    )
            if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
            return dummy_inputs
