






"""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    snake_case						:


   '''simple docstring'''

   A_		:      Union[str, Any]							=					42
   A_		:      Union[str, Any]							=					42

   # setable values
   A_		:      Optional[Any]							=					42
   A_		:      Optional[int]							=					42
   A_		:      List[Any]							=					None


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Union[str, Any],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	str,			_lowerCamelCase					:	int,			):

     '''simple docstring'''
     return cls(
         common=__snake_case,			final_alpha_cumprod=__snake_case,			init_noise_sigma=__snake_case,			timesteps=__snake_case,			)





@dataclass
class    snake_case						(				_lowercase		):


   '''simple docstring'''

   A_		:      Optional[Any]							=					42





class    snake_case						(				_lowercase  ,      _lowercase		):


   '''simple docstring'''

   A_		:      Optional[int]							=					[e.name for e in FlaxKarrasDiffusionSchedulers]

   A_		:      List[str]							=					42


   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     return True


   @register_to_config
   def __init__(      self					:	List[str],			_lowerCamelCase					:	int = 10_00,			_lowerCamelCase					:	int = 0.00_01,			_lowerCamelCase					:	int = 0.02,			_lowerCamelCase					:	int = "linear",			_lowerCamelCase					:	Tuple = None,			_lowerCamelCase					:	Optional[int] = True,			_lowerCamelCase					:	List[Any] = 0,			_lowerCamelCase					:	Any = "epsilon",			_lowerCamelCase					:	Any = jnp.floataa,			):

     '''simple docstring'''
     __A		     =					dtype


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	List[Any] = None    ):

     '''simple docstring'''
     if common is None:
       __A		     =					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.
     __A		     =					(
         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
     __A		     =					jnp.array(1.0,			dtype=self.dtype    )

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	int,			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	str = None    ):

     '''simple docstring'''
     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str = ()    ):

     '''simple docstring'''
     __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,			__snake_case    ) * step_ratio).round()[::-1] + self.config.steps_offset

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	str,			_lowerCamelCase					:	Optional[Any],			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[str]    ):

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

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

     return variance


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	str,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	List[Any],			_lowerCamelCase					:	Optional[Any] = 0.0,			_lowerCamelCase					:	Optional[Any] = 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)
     __A		     =					timestep - self.config.num_train_timesteps // state.num_inference_steps

     __A		     =					state.common.alphas_cumprod
     __A		     =					state.final_alpha_cumprod

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

     __A		     =					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":
       __A		     =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       __A		     =					model_output
     elif self.config.prediction_type == "sample":
       __A		     =					model_output
       __A		     =					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
     elif self.config.prediction_type == "v_prediction":
       __A		     =					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       __A		     =					(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)
     __A		     =					self._get_variance(__snake_case,			__snake_case,			__snake_case    )
     __A		     =					eta * variance ** 0.5

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

     if not return_dict:
       return (prev_sample, state)

     return FlaxDDIMSchedulerOutput(prev_sample=__snake_case,			state=__snake_case    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Union[str, Any],			_lowerCamelCase					:	str,			):

     '''simple docstring'''
     return add_noise_common(state.common,			__snake_case,			__snake_case,			__snake_case    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int,			_lowerCamelCase					:	Tuple,			_lowerCamelCase					:	Optional[int],			_lowerCamelCase					:	List[str],			_lowerCamelCase					:	Optional[int],			):

     '''simple docstring'''
     return get_velocity_common(state.common,			__snake_case,			__snake_case,			__snake_case    )






   def __len__(      self					:	Optional[int]    ):

     '''simple docstring'''
     return self.config.num_train_timesteps







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase__             =		{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)




