"""simple docstring"""





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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)

@flax.struct.dataclass
class     __a   :

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						42

			# setable values
			SCREAMING_SNAKE_CASE_						     =						42
			SCREAMING_SNAKE_CASE_						     =						42
			SCREAMING_SNAKE_CASE_						     =						42
			SCREAMING_SNAKE_CASE_						     =						None

			# running values
			SCREAMING_SNAKE_CASE_						     =						None






			@classmethod
			def    _lowerCAmelCase			(							cls     :					Dict						,  lowercase_     :					Dict						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					List[str]						,  lowercase_     :					int						):
								return cls(common=lowercase_						,  init_noise_sigma=lowercase_						,  timesteps=lowercase_						,  sigmas=lowercase_						)



@dataclass
class     __a   (      A_    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						42



class     __a   (      A_,    A_    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						[e.name for e in FlaxKarrasDiffusionSchedulers]

			SCREAMING_SNAKE_CASE_						     =						42






			@property
			def    _lowerCAmelCase			(							self     :					Any						):
								return True






			@register_to_config
			def __init__(							self     :					List[str]						,  lowercase_     :					Dict = 1000						,  lowercase_     :					str = 0.0_0_0_1						,  lowercase_     :					Any = 0.0_2						,  lowercase_     :					Optional[Any] = "linear"						,  lowercase_     :					Optional[Any] = None						,  lowercase_     :					Optional[Any] = "epsilon"						,  lowercase_     :					Optional[int] = jnp.floataa						,  ):
								UpperCamelCase__		:    int         =dtype






			def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Optional[Any] = None						):
								if common is None:
													UpperCamelCase__		:    Any         =CommonSchedulerState.create(self						)

								UpperCamelCase__		:    Tuple         =jnp.arange(0						,  self.config.num_train_timesteps						).round()[::-1]
								UpperCamelCase__		:    str         =((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

								# standard deviation of the initial noise distribution
								UpperCamelCase__		:    Any         =sigmas.max()

								return LMSDiscreteSchedulerState.create(
								    common=lowercase_						,  init_noise_sigma=lowercase_						,  timesteps=lowercase_						,  sigmas=lowercase_						,  )






			def    _lowerCAmelCase			(							self     :					Optional[Any]						,  lowercase_     :					Optional[int]						,  lowercase_     :					Optional[int]						,  lowercase_     :					Dict						):
								((UpperCamelCase__							)  ,										)		:    int         =jnp.where(state.timesteps == timestep						,  size=1						)
								UpperCamelCase__		:    Union[str, Any]         =step_index[0]

								UpperCamelCase__		:    Any         =state.sigmas[step_index]
								UpperCamelCase__		:    str         =sample / ((sigma**2 + 1) ** 0.5)
								return sample






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					Any						,  lowercase_     :					List[str]						,  lowercase_     :					str						,  lowercase_     :					List[str]						):

								def lms_derivative(lowercase_     :					Tuple						):
													UpperCamelCase__		:    Union[str, Any]         =1.0
													for k in range(lowercase_						):
																		if current_order == k:
																							continue
																		prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
													return prod

								UpperCamelCase__		:    int         =integrate.quad(lowercase_						,  state.sigmas[t]						,  state.sigmas[t + 1]						,  epsrel=1e-4						)[0]

								return integrated_coeff






			def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					List[str]						,  lowercase_     :					Dict						,  lowercase_     :					Tuple = ()						):
								UpperCamelCase__		:    Any         =jnp.linspace(self.config.num_train_timesteps - 1						,  0						,  lowercase_						,  dtype=self.dtype						)

								UpperCamelCase__		:    Any         =jnp.floor(lowercase_						).astype(jnp.intaa						)
								UpperCamelCase__		:    Union[str, Any]         =jnp.ceil(lowercase_						).astype(jnp.intaa						)

								UpperCamelCase__		:    Optional[Any]         =jnp.mod(lowercase_						,  1.0						)

								UpperCamelCase__		:    Dict         =((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
								UpperCamelCase__		:    str         =(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
								UpperCamelCase__		:    Optional[Any]         =jnp.concatenate([sigmas, jnp.array([0.0]						,  dtype=self.dtype						)]						)

								UpperCamelCase__		:    Optional[int]         =timesteps.astype(jnp.intaa						)

								# initial running values
								UpperCamelCase__		:    Any         =jnp.zeros((0,) + shape						,  dtype=self.dtype						)

								return state.replace(
								    timesteps=lowercase_						,  sigmas=lowercase_						,  num_inference_steps=lowercase_						,  derivatives=lowercase_						,  )






			def    _lowerCAmelCase			(							self     :					Any						,  lowercase_     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					Optional[int]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					Optional[int] = 4						,  lowercase_     :					Tuple = True						,  ):
								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'''						)

								UpperCamelCase__		:    Optional[int]         =state.sigmas[timestep]

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
													UpperCamelCase__		:    Tuple         =sample - sigma * model_output
								elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													UpperCamelCase__		:    Dict         =model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
								else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''						)

								# 2. Convert to an ODE derivative
								UpperCamelCase__		:    str         =(sample - pred_original_sample) / sigma
								UpperCamelCase__		:    List[str]         =state.replace(derivatives=jnp.append(state.derivatives						,  lowercase_						)						)
								if len(state.derivatives						) > order:
													UpperCamelCase__		:    Union[str, Any]         =state.replace(derivatives=jnp.delete(state.derivatives						,  0						)						)

								# 3. Compute linear multistep coefficients
								UpperCamelCase__		:    List[str]         =min(timestep + 1						,  lowercase_						)
								UpperCamelCase__		:    Dict         =[self.get_lms_coefficient(lowercase_						,  lowercase_						,  lowercase_						,  lowercase_						) for curr_order in range(lowercase_						)]

								# 4. Compute previous sample based on the derivatives path
								UpperCamelCase__		:    Union[str, Any]         =sample + sum(
								    coeff * derivative for coeff, derivative in zip(lowercase_						,  reversed(state.derivatives						)						)						)

								if not return_dict:
													return (prev_sample, state)

								return FlaxLMSSchedulerOutput(prev_sample=lowercase_						,  state=lowercase_						)






			def    _lowerCAmelCase			(							self     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						,  lowercase_     :					Tuple						,  lowercase_     :					Dict						,  lowercase_     :					Optional[int]						,  ):
								UpperCamelCase__		:    Any         =state.sigmas[timesteps].flatten()
								UpperCamelCase__		:    Any         =broadcast_to_shape_from_left(lowercase_						,  noise.shape						)

								UpperCamelCase__		:    Optional[Any]         =original_samples + noise * sigma

								return noisy_samples






			def __len__(							self     :					Optional[Any]						):
								return self.config.num_train_timesteps





"""simple docstring"""





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

				_SCREAMING_SNAKE_CASE       :  Dict        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


