
"""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      _SCREAMING_SNAKE_CASE   :
			UpperCAmelCase_    :CommonSchedulerState

			# setable values
			UpperCAmelCase_    :jnp.ndarray
			UpperCAmelCase_    :jnp.ndarray
			UpperCAmelCase_    :jnp.ndarray
			UpperCAmelCase_    :Optional[int]												=		None

			# running values
			UpperCAmelCase_    :Optional[jnp.ndarray]												=		None






			@classmethod
			def 					__lowerCAmelCase					(   cls			,  __A			,  __A			,  __A			,  __A			)     ->	Optional[Any]:
						return cls(common=__A			,  init_noise_sigma=__A			,  timesteps=__A			,  sigmas=__A			)




@dataclass
class      _SCREAMING_SNAKE_CASE   (     A__				):
			UpperCAmelCase_    :LMSDiscreteSchedulerState




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

			UpperCAmelCase_    :jnp.dtype






			@property
			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						return True






			@register_to_config
			def __init__(   self			,  __A = 1000			,  __A = 0.0_0_0_1			,  __A = 0.0_2			,  __A = "linear"			,  __A = None			,  __A = "epsilon"			,  __A = jnp.floataa			,  )     ->	Any:
						lowerCAmelCase_       :List[Any]     	=    dtype






			def 					__lowerCAmelCase					(   self			,  __A = None			)     ->	LMSDiscreteSchedulerState:
						if common is None:
									lowerCAmelCase_       :List[str]     	=    CommonSchedulerState.create(self			)

						lowerCAmelCase_       :Dict     	=    jnp.arange(0			,  self.config.num_train_timesteps			).round()[::-1]
						lowerCAmelCase_       :Optional[Any]     	=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						lowerCAmelCase_       :List[str]     	=    sigmas.max()

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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			)     ->	jnp.ndarray:
						((lowerCAmelCase_)	,			)       :int     	=    jnp.where(state.timesteps == timestep			,  size=1			)
						lowerCAmelCase_       :List[str]     	=    step_index[0]

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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			,  __A			)     ->	int:

						def lms_derivative(__A			):
									lowerCAmelCase_       :Optional[Any]     	=    1.0
									for k in range(__A			):
												if current_order == k:
															continue
												prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
									return prod

						lowerCAmelCase_       :str     	=    integrate.quad(__A			,  state.sigmas[t]			,  state.sigmas[t + 1]			,  epsrel=1E-4			)[0]

						return integrated_coeff






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A = ()			)     ->	LMSDiscreteSchedulerState:
						lowerCAmelCase_       :str     	=    jnp.linspace(self.config.num_train_timesteps - 1			,  0			,  __A			,  dtype=self.dtype			)

						lowerCAmelCase_       :Dict     	=    jnp.floor(__A			).astype(jnp.intaa			)
						lowerCAmelCase_       :Optional[Any]     	=    jnp.ceil(__A			).astype(jnp.intaa			)

						lowerCAmelCase_       :Union[str, Any]     	=    jnp.mod(__A			,  1.0			)

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

						lowerCAmelCase_       :Dict     	=    timesteps.astype(jnp.intaa			)

						# initial running values
						lowerCAmelCase_       :List[str]     	=    jnp.zeros((0,) + shape			,  dtype=self.dtype			)

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






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			,  __A			,  __A = 4			,  __A = True			,  )     ->	Union[FlaxLMSSchedulerOutput, 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"""			)

						lowerCAmelCase_       :List[Any]     	=    state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									lowerCAmelCase_       :int     	=    sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
									# * c_out + input * c_skip
									lowerCAmelCase_       :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
						lowerCAmelCase_       :Any     	=    (sample - pred_original_sample) / sigma
						lowerCAmelCase_       :List[str]     	=    state.replace(derivatives=jnp.append(state.derivatives			,  __A			)			)
						if len(state.derivatives			) > order:
									lowerCAmelCase_       :Union[str, Any]     	=    state.replace(derivatives=jnp.delete(state.derivatives			,  0			)			)

						# 3. Compute linear multistep coefficients
						lowerCAmelCase_       :Dict     	=    min(timestep + 1			,  __A			)
						lowerCAmelCase_       :int     	=    [self.get_lms_coefficient(__A			,  __A			,  __A			,  __A			) for curr_order in range(__A			)]

						# 4. Compute previous sample based on the derivatives path
						lowerCAmelCase_       :Tuple     	=    sample + sum(
						    coeff * derivative for coeff, derivative in zip(__A			,  reversed(state.derivatives			)			)			)

						if not return_dict:
									return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=__A			,  state=__A			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A			,  __A			,  )     ->	jnp.ndarray:
						lowerCAmelCase_       :Any     	=    state.sigmas[timesteps].flatten()
						lowerCAmelCase_       :Union[str, Any]     	=    broadcast_to_shape_from_left(__A			,  noise.shape			)

						lowerCAmelCase_       :Dict     	=    original_samples + noise * sigma

						return noisy_samples






			def __len__(   self			)     ->	List[Any]:
						return self.config.num_train_timesteps








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase 					=    {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['MarianTokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarianForCausalLM',
				    'MarianModel',
				    'MarianMTModel',
				    'MarianPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
				from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_marian import MarianTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_marian import (
								    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarianForCausalLM,
								    MarianModel,
								    MarianMTModel,
								    MarianPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
				import sys

				__UpperCAmelCase 					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






