'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left




@flax.struct.dataclass
class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			__magic_name__   :							Optional[jnp.ndarray]             =					None
			__magic_name__   :							Optional[jnp.ndarray]             =					None
			__magic_name__   :							Optional[jnp.ndarray]             =					None



			@classmethod
			def      lowerCamelCase   (       cls				):
									return cls()







@dataclass
class 		SCREAMING_SNAKE_CASE_				(UpperCAmelCase__			):



			"""simple docstring"""






			__magic_name__   :							ScoreSdeVeSchedulerState
			__magic_name__   :							jnp.ndarray
			__magic_name__   :							Optional[jnp.ndarray]             =					None







class 		SCREAMING_SNAKE_CASE_				(UpperCAmelCase__       ,							UpperCAmelCase__			):



			"""simple docstring"""






			@property
			def      lowerCamelCase   (       self				):
									return True



			@register_to_config
			def __init__(       self							, snake_case_ = 2_000							, snake_case_ = 0.15							, snake_case_ = 0.01							, snake_case_ = 1_348.0							, snake_case_ = 1e-5							, snake_case_ = 1							, ):
									pass



			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						ScoreSdeVeSchedulerState.create()
									return self.set_sigmas(
									    lowerCamelCase__							, self.config.num_train_timesteps							, self.config.sigma_min							, self.config.sigma_max							, self.config.sampling_eps							, )



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_ = ()							, snake_case_ = None				):
									snake_case   :					str							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

									snake_case   :					List[str]							=						jnp.linspace(1							, lowerCamelCase__							, lowerCamelCase__				)
									return state.replace(timesteps=lowerCamelCase__				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_ = None							, snake_case_ = None							, snake_case_ = None							, ):
									snake_case   :					Dict							=						sigma_min if sigma_min is not None else self.config.sigma_min
									snake_case   :					Dict							=						sigma_max if sigma_max is not None else self.config.sigma_max
									snake_case   :					int							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
									if state.timesteps is None:
															snake_case   :					Dict							=						self.set_timesteps(lowerCamelCase__							, lowerCamelCase__							, lowerCamelCase__				)

									snake_case   :					Optional[int]							=						jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__				)							, jnp.log(lowerCamelCase__				)							, lowerCamelCase__				)				)
									snake_case   :					Union[str, Any]							=						jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]				)

									return state.replace(discrete_sigmas=lowerCamelCase__							, sigmas=lowerCamelCase__				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_				):
									return jnp.where(timesteps == 0							, jnp.zeros_like(lowerCamelCase__				)							, state.discrete_sigmas[timesteps - 1]				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = True							, ):
									if state.timesteps is None:
															raise ValueError(
															    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

									snake_case   :					Optional[int]							=						timestep * jnp.ones(
									    sample.shape[0]							, )
									snake_case   :					Dict							=						(timestep * (len(state.timesteps				) - 1)).long()

									snake_case   :					Optional[int]							=						state.discrete_sigmas[timesteps]
									snake_case   :					int							=						self.get_adjacent_sigma(lowerCamelCase__							, lowerCamelCase__							, lowerCamelCase__				)
									snake_case   :					Optional[Any]							=						jnp.zeros_like(lowerCamelCase__				)
									snake_case   :					int							=						(sigma**2 - adjacent_sigma**2) ** 0.5

									# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
									# also equation 47 shows the analog from SDE models to ancestral sampling methods
									snake_case   :					Any							=						diffusion.flatten()
									snake_case   :					Union[str, Any]							=						broadcast_to_shape_from_left(lowerCamelCase__							, sample.shape				)
									snake_case   :					Union[str, Any]							=						drift - diffusion**2 * model_output

									#  equation 6: sample noise for the diffusion term of
									snake_case   :					Optional[Any]							=						random.split(lowerCamelCase__							, num=1				)
									snake_case   :					Union[str, Any]							=						random.normal(key=lowerCamelCase__							, shape=sample.shape				)
									snake_case   :					Dict							=						sample - drift  # subtract because `dt` is a small negative timestep
									# TODO is the variable diffusion the correct scaling term for the noise?
									snake_case   :					Any							=						prev_sample_mean + diffusion * noise  # add impact of diffusion field g

									if not return_dict:
															return (prev_sample, prev_sample_mean, state)

									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__							, prev_sample_mean=lowerCamelCase__							, state=lowerCamelCase__				)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = True							, ):
									if state.timesteps is None:
															raise ValueError(
															    "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"				)

									# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
									# sample noise for correction
									snake_case   :					Tuple							=						random.split(lowerCamelCase__							, num=1				)
									snake_case   :					Any							=						random.normal(key=lowerCamelCase__							, shape=sample.shape				)

									# compute step size from the model_output, the noise, and the snr
									snake_case   :					Tuple							=						jnp.linalg.norm(lowerCamelCase__				)
									snake_case   :					Any							=						jnp.linalg.norm(lowerCamelCase__				)
									snake_case   :					Dict							=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
									snake_case   :					Dict							=						step_size * jnp.ones(sample.shape[0]				)

									# compute corrected sample: model_output term and noise term
									snake_case   :					List[Any]							=						step_size.flatten()
									snake_case   :					str							=						broadcast_to_shape_from_left(lowerCamelCase__							, sample.shape				)
									snake_case   :					str							=						sample + step_size * model_output
									snake_case   :					str							=						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

									if not return_dict:
															return (prev_sample, state)

									return FlaxSdeVeOutput(prev_sample=lowerCamelCase__							, state=lowerCamelCase__				)






			def __len__(       self				):
									return self.config.num_train_timesteps






'''simple docstring'''

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


A_        =   {
    "configuration_utils": ["GenerationConfig"],
    "streamers": ["TextIteratorStreamer", "TextStreamer"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "Constraint",
						    "ConstraintListState",
						    "DisjunctiveConstraint",
						    "PhrasalConstraint",
						]
						A_        =   [
						    "BeamHypotheses",
						    "BeamScorer",
						    "BeamSearchScorer",
						    "ConstrainedBeamSearchScorer",
						]
						A_        =   [
						    "EpsilonLogitsWarper",
						    "EtaLogitsWarper",
						    "ForcedBOSTokenLogitsProcessor",
						    "ForcedEOSTokenLogitsProcessor",
						    "HammingDiversityLogitsProcessor",
						    "InfNanRemoveLogitsProcessor",
						    "LogitsProcessor",
						    "LogitsProcessorList",
						    "LogitsWarper",
						    "MinLengthLogitsProcessor",
						    "MinNewTokensLengthLogitsProcessor",
						    "NoBadWordsLogitsProcessor",
						    "NoRepeatNGramLogitsProcessor",
						    "PrefixConstrainedLogitsProcessor",
						    "RepetitionPenaltyLogitsProcessor",
						    "SequenceBiasLogitsProcessor",
						    "EncoderRepetitionPenaltyLogitsProcessor",
						    "TemperatureLogitsWarper",
						    "TopKLogitsWarper",
						    "TopPLogitsWarper",
						    "TypicalLogitsWarper",
						    "EncoderNoRepeatNGramLogitsProcessor",
						    "ExponentialDecayLengthPenalty",
						    "LogitNormalization",
						]
						A_        =   [
						    "MaxNewTokensCriteria",
						    "MaxLengthCriteria",
						    "MaxTimeCriteria",
						    "StoppingCriteria",
						    "StoppingCriteriaList",
						    "validate_stopping_criteria",
						]
						A_        =   [
						    "GenerationMixin",
						    "top_k_top_p_filtering",
						    "GreedySearchEncoderDecoderOutput",
						    "GreedySearchDecoderOnlyOutput",
						    "SampleEncoderDecoderOutput",
						    "SampleDecoderOnlyOutput",
						    "BeamSearchEncoderDecoderOutput",
						    "BeamSearchDecoderOnlyOutput",
						    "BeamSampleEncoderDecoderOutput",
						    "BeamSampleDecoderOnlyOutput",
						    "ContrastiveSearchEncoderDecoderOutput",
						    "ContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "TFForcedBOSTokenLogitsProcessor",
						    "TFForcedEOSTokenLogitsProcessor",
						    "TFLogitsProcessor",
						    "TFLogitsProcessorList",
						    "TFLogitsWarper",
						    "TFMinLengthLogitsProcessor",
						    "TFNoBadWordsLogitsProcessor",
						    "TFNoRepeatNGramLogitsProcessor",
						    "TFRepetitionPenaltyLogitsProcessor",
						    "TFTemperatureLogitsWarper",
						    "TFTopKLogitsWarper",
						    "TFTopPLogitsWarper",
						    "TFForceTokensLogitsProcessor",
						    "TFSuppressTokensAtBeginLogitsProcessor",
						    "TFSuppressTokensLogitsProcessor",
						]
						A_        =   [
						    "TFGenerationMixin",
						    "tf_top_k_top_p_filtering",
						    "TFGreedySearchDecoderOnlyOutput",
						    "TFGreedySearchEncoderDecoderOutput",
						    "TFSampleEncoderDecoderOutput",
						    "TFSampleDecoderOnlyOutput",
						    "TFBeamSearchEncoderDecoderOutput",
						    "TFBeamSearchDecoderOnlyOutput",
						    "TFBeamSampleEncoderDecoderOutput",
						    "TFBeamSampleDecoderOnlyOutput",
						    "TFContrastiveSearchEncoderDecoderOutput",
						    "TFContrastiveSearchDecoderOnlyOutput",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_        =   [
						    "FlaxForcedBOSTokenLogitsProcessor",
						    "FlaxForcedEOSTokenLogitsProcessor",
						    "FlaxLogitsProcessor",
						    "FlaxLogitsProcessorList",
						    "FlaxLogitsWarper",
						    "FlaxMinLengthLogitsProcessor",
						    "FlaxTemperatureLogitsWarper",
						    "FlaxTopKLogitsWarper",
						    "FlaxTopPLogitsWarper",
						]
						A_        =   [
						    "FlaxGenerationMixin",
						    "FlaxGreedySearchOutput",
						    "FlaxSampleOutput",
						    "FlaxBeamSearchOutput",
						]

if TYPE_CHECKING:
						from .configuration_utils import GenerationConfig
						from .streamers import TextIteratorStreamer, TextStreamer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
												from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
												from .logits_process import (
												    EncoderNoRepeatNGramLogitsProcessor,
												    EncoderRepetitionPenaltyLogitsProcessor,
												    EpsilonLogitsWarper,
												    EtaLogitsWarper,
												    ExponentialDecayLengthPenalty,
												    ForcedBOSTokenLogitsProcessor,
												    ForcedEOSTokenLogitsProcessor,
												    HammingDiversityLogitsProcessor,
												    InfNanRemoveLogitsProcessor,
												    LogitNormalization,
												    LogitsProcessor,
												    LogitsProcessorList,
												    LogitsWarper,
												    MinLengthLogitsProcessor,
												    MinNewTokensLengthLogitsProcessor,
												    NoBadWordsLogitsProcessor,
												    NoRepeatNGramLogitsProcessor,
												    PrefixConstrainedLogitsProcessor,
												    RepetitionPenaltyLogitsProcessor,
												    SequenceBiasLogitsProcessor,
												    TemperatureLogitsWarper,
												    TopKLogitsWarper,
												    TopPLogitsWarper,
												    TypicalLogitsWarper,
												)
												from .stopping_criteria import (
												    MaxLengthCriteria,
												    MaxNewTokensCriteria,
												    MaxTimeCriteria,
												    StoppingCriteria,
												    StoppingCriteriaList,
												    validate_stopping_criteria,
												)
												from .utils import (
												    BeamSampleDecoderOnlyOutput,
												    BeamSampleEncoderDecoderOutput,
												    BeamSearchDecoderOnlyOutput,
												    BeamSearchEncoderDecoderOutput,
												    ContrastiveSearchDecoderOnlyOutput,
												    ContrastiveSearchEncoderDecoderOutput,
												    GenerationMixin,
												    GreedySearchDecoderOnlyOutput,
												    GreedySearchEncoderDecoderOutput,
												    SampleDecoderOnlyOutput,
												    SampleEncoderDecoderOutput,
												    top_k_top_p_filtering,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tf_logits_process import (
												    TFForcedBOSTokenLogitsProcessor,
												    TFForcedEOSTokenLogitsProcessor,
												    TFForceTokensLogitsProcessor,
												    TFLogitsProcessor,
												    TFLogitsProcessorList,
												    TFLogitsWarper,
												    TFMinLengthLogitsProcessor,
												    TFNoBadWordsLogitsProcessor,
												    TFNoRepeatNGramLogitsProcessor,
												    TFRepetitionPenaltyLogitsProcessor,
												    TFSuppressTokensAtBeginLogitsProcessor,
												    TFSuppressTokensLogitsProcessor,
												    TFTemperatureLogitsWarper,
												    TFTopKLogitsWarper,
												    TFTopPLogitsWarper,
												)
												from .tf_utils import (
												    TFBeamSampleDecoderOnlyOutput,
												    TFBeamSampleEncoderDecoderOutput,
												    TFBeamSearchDecoderOnlyOutput,
												    TFBeamSearchEncoderDecoderOutput,
												    TFContrastiveSearchDecoderOnlyOutput,
												    TFContrastiveSearchEncoderDecoderOutput,
												    TFGenerationMixin,
												    TFGreedySearchDecoderOnlyOutput,
												    TFGreedySearchEncoderDecoderOutput,
												    TFSampleDecoderOnlyOutput,
												    TFSampleEncoderDecoderOutput,
												    tf_top_k_top_p_filtering,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .flax_logits_process import (
												    FlaxForcedBOSTokenLogitsProcessor,
												    FlaxForcedEOSTokenLogitsProcessor,
												    FlaxLogitsProcessor,
												    FlaxLogitsProcessorList,
												    FlaxLogitsWarper,
												    FlaxMinLengthLogitsProcessor,
												    FlaxTemperatureLogitsWarper,
												    FlaxTopKLogitsWarper,
												    FlaxTopPLogitsWarper,
												)
												from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
						import sys

						A_        =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





