
"""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__      :





			'''simple docstring'''
			lowerCamelCase						      =	42
			lowerCamelCase						      =	42

			# setable values
			lowerCamelCase						      =	42
			lowerCamelCase						      =	42
			lowerCamelCase						      =	None



			@classmethod
			def       _lowerCAmelCase	(							cls  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  )				->  List[Any]:
				return cls(
				    common=__UpperCAmelCase  ,  final_alpha_cumprod=__UpperCAmelCase  ,  init_noise_sigma=__UpperCAmelCase  ,  timesteps=__UpperCAmelCase  ,  )







@dataclass
class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	42







class 	lowerCamelCase__      (      __magic_name__				,			__magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase						      =	42



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



			@register_to_config
			def __init__(							self  ,  __UpperCAmelCase = 10_00  ,  __UpperCAmelCase = 0.0_0_0_1  ,  __UpperCAmelCase = 0.0_2  ,  __UpperCAmelCase = "linear"  ,  __UpperCAmelCase = None  ,  __UpperCAmelCase = True  ,  __UpperCAmelCase = 0  ,  __UpperCAmelCase = "epsilon"  ,  __UpperCAmelCase = jnp.floataa  ,  )				->  Dict:
				_lowerCAmelCase			=dtype



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase = None						)				->  DDIMSchedulerState:
				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=__UpperCAmelCase  ,  final_alpha_cumprod=__UpperCAmelCase  ,  init_noise_sigma=__UpperCAmelCase  ,  timesteps=__UpperCAmelCase  ,  )



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = None						)				->  jnp.ndarray:
				return sample



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = ()						)				->  DDIMSchedulerState:
				_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  ,  __UpperCAmelCase						) * step_ratio).round()[::-1] + self.config.steps_offset

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



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)				->  int:
				_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       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase = 0.0  ,  __UpperCAmelCase = True  ,  )				->  Union[FlaxDDIMSchedulerOutput, Tuple]:
				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]  ,  __UpperCAmelCase						)

				_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(__UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)
				_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=__UpperCAmelCase  ,  state=__UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  )				->  jnp.ndarray:
				return add_noise_common(state.common  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  )				->  jnp.ndarray:
				return get_velocity_common(state.common  ,  __UpperCAmelCase  ,  __UpperCAmelCase  ,  __UpperCAmelCase						)



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





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


__A 		= logging.get_logger(__name__)

__A 		= {
    'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
    'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
    'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
    'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
    'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
    'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
}







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''
			lowerCamelCase						      =	'''ernie'''



			def __init__(							self  ,  __UpperCAmelCase=3_05_22  ,  __UpperCAmelCase=7_68  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=12  ,  __UpperCAmelCase=30_72  ,  __UpperCAmelCase="gelu"  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=0.1  ,  __UpperCAmelCase=5_12  ,  __UpperCAmelCase=2  ,  __UpperCAmelCase=3  ,  __UpperCAmelCase=False  ,  __UpperCAmelCase=0.0_2  ,  __UpperCAmelCase=1e-12  ,  __UpperCAmelCase=0  ,  __UpperCAmelCase="absolute"  ,  __UpperCAmelCase=True  ,  __UpperCAmelCase=None  ,  **__UpperCAmelCase  ,  )				->  str:
				super().__init__(pad_token_id=__UpperCAmelCase  ,  **__UpperCAmelCase						)

				_lowerCAmelCase			=vocab_size
				_lowerCAmelCase			=hidden_size
				_lowerCAmelCase			=num_hidden_layers
				_lowerCAmelCase			=num_attention_heads
				_lowerCAmelCase			=hidden_act
				_lowerCAmelCase			=intermediate_size
				_lowerCAmelCase			=hidden_dropout_prob
				_lowerCAmelCase			=attention_probs_dropout_prob
				_lowerCAmelCase			=max_position_embeddings
				_lowerCAmelCase			=type_vocab_size
				_lowerCAmelCase			=task_type_vocab_size
				_lowerCAmelCase			=use_task_id
				_lowerCAmelCase			=initializer_range
				_lowerCAmelCase			=layer_norm_eps
				_lowerCAmelCase			=position_embedding_type
				_lowerCAmelCase			=use_cache
				_lowerCAmelCase			=classifier_dropout







class 	lowerCamelCase__      (      __magic_name__       ):





			'''simple docstring'''



			@property
			def       _lowerCAmelCase	(							self						)				->  Mapping[str, Mapping[int, str]]:
				if self.task == "multiple-choice":
					_lowerCAmelCase			={0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
					_lowerCAmelCase			={0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				        ("""token_type_ids""", dynamic_axis),
				        ("""task_type_ids""", dynamic_axis),
				    ]						)



