




"""simple docstring"""

# 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      lowerCamelCase__      :
			a__	: str        =       42
			a__	: Optional[Any]        =       42

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


			@classmethod
			def 							lowerCamelCase_			(       cls			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					return cls(
					    common=__UpperCamelCase			,	final_alpha_cumprod=__UpperCamelCase			,	init_noise_sigma=__UpperCamelCase			,	timesteps=__UpperCamelCase			,	)







@dataclass
class      lowerCamelCase__      (					_lowerCamelCase		):
			a__	: List[str]        =       42







class      lowerCamelCase__      (					_lowerCamelCase			,					_lowerCamelCase		):
			a__	: str        =       [e.name for e in FlaxKarrasDiffusionSchedulers]

			a__	: int        =       42


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return True


			@register_to_config
			def __init__(       self			,	SCREAMING_SNAKE_CASE = 1_000			,	SCREAMING_SNAKE_CASE = 0.00_01			,	SCREAMING_SNAKE_CASE = 0.02			,	SCREAMING_SNAKE_CASE = "linear"			,	SCREAMING_SNAKE_CASE = None			,	SCREAMING_SNAKE_CASE = True			,	SCREAMING_SNAKE_CASE = 0			,	SCREAMING_SNAKE_CASE = "epsilon"			,	SCREAMING_SNAKE_CASE = jnp.floataa			,	):



					"""simple docstring"""







					snake_case						:  int        =			dtype


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if common is None:
							snake_case						:  Any        =			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.
					snake_case						:  List[Any]        =			(
					    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
					snake_case						:  Union[str, Any]        =			jnp.array(1.0			,	dtype=self.dtype							)

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

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					return sample


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = ()							):



					"""simple docstring"""







					snake_case						:  Dict        =			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
					snake_case						:  Optional[int]        =			(jnp.arange(0			,	__UpperCamelCase							) * step_ratio).round()[::-1] + self.config.steps_offset

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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Tuple        =			state.common.alphas_cumprod[timestep]
					snake_case						:  Tuple        =			jnp.where(
					    prev_timestep >= 0			,	state.common.alphas_cumprod[prev_timestep]			,	state.final_alpha_cumprod							)
					snake_case						:  List[str]        =			1 - alpha_prod_t
					snake_case						:  int        =			1 - alpha_prod_t_prev

					snake_case						:  List[str]        =			(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

					return variance


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE = 0.0			,	SCREAMING_SNAKE_CASE = 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)
					snake_case						:  Dict        =			timestep - self.config.num_train_timesteps // state.num_inference_steps

					snake_case						:  Optional[Any]        =			state.common.alphas_cumprod
					snake_case						:  List[Any]        =			state.final_alpha_cumprod

					# 2. compute alphas, betas
					snake_case						:  Union[str, Any]        =			alphas_cumprod[timestep]
					snake_case						:  List[str]        =			jnp.where(prev_timestep >= 0			,	alphas_cumprod[prev_timestep]			,	__UpperCamelCase							)

					snake_case						:  List[str]        =			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":
							snake_case						:  Any        =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							snake_case						:  Dict        =			model_output
					elif self.config.prediction_type == "sample":
							snake_case						:  Dict        =			model_output
							snake_case						:  str        =			(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
							snake_case						:  int        =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							snake_case						:  List[str]        =			(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)
					snake_case						:  Any        =			self._get_variance(__UpperCamelCase			,	__UpperCamelCase			,	__UpperCamelCase							)
					snake_case						:  Tuple        =			eta * variance ** 0.5

					# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					snake_case						:  List[Any]        =			(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
					snake_case						:  Dict        =			alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if not return_dict:
							return (prev_sample, state)

					return FlaxDDIMSchedulerOutput(prev_sample=__UpperCamelCase			,	state=__UpperCamelCase							)


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







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



			def __len__(       self							):



					"""simple docstring"""







					return self.config.num_train_timesteps







"""simple docstring"""

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


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

