




"""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     lowercase :
				__lowerCAmelCase      :      CommonSchedulerState

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

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







				@classmethod
				def    a_					(							cls				:   List[Any] ,       _lowerCamelCase				:   CommonSchedulerState ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   jnp.ndarray    ):



					"""simple docstring"""

					return cls(common=_lowerCamelCase ,       init_noise_sigma=_lowerCamelCase ,       timesteps=_lowerCamelCase ,       sigmas=_lowerCamelCase    )

@dataclass
class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      LMSDiscreteSchedulerState

class     lowercase (      __UpperCAmelCase       ,		__UpperCAmelCase):
				__lowerCAmelCase      :      Union[str, Any]					    =				[e.name for e in FlaxKarrasDiffusionSchedulers]

				__lowerCAmelCase      :      jnp.dtype







				@property
				def    a_					(							self				:   Union[str, Any]    ):



					"""simple docstring"""

					return True







				@register_to_config
				def __init__(							self				:   str ,       _lowerCamelCase				:   int = 10_00 ,       _lowerCamelCase				:   float = 0.0001 ,       _lowerCamelCase				:   float = 0.02 ,       _lowerCamelCase				:   str = "linear" ,       _lowerCamelCase				:   Optional[jnp.ndarray] = None ,       _lowerCamelCase				:   str = "epsilon" ,       _lowerCamelCase				:   jnp.dtype = jnp.floataa ,       ):



					"""simple docstring"""

					A_					:			Optional[int]						=						dtype







				def    a_					(							self				:   Tuple ,       _lowerCamelCase				:   Optional[CommonSchedulerState] = None    ):



					"""simple docstring"""

					if common is None:
						A_					:			Dict						=						CommonSchedulerState.create(self    )

					A_					:			Optional[int]						=						jnp.arange(0 ,       self.config.num_train_timesteps    ).round()[::-1]
					A_					:			List[str]						=						((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

					# standard deviation of the initial noise distribution
					A_					:			Tuple						=						sigmas.max()

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







				def    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   LMSDiscreteSchedulerState ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   int    ):



					"""simple docstring"""

					((A_)				,	)					:			List[str]						=						jnp.where(state.timesteps == timestep ,       size=1    )
					A_					:			str						=						step_index[0]

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







				def    a_					(							self				:   Optional[Any] ,       _lowerCamelCase				:   LMSDiscreteSchedulerState ,       _lowerCamelCase				:   Tuple ,       _lowerCamelCase				:   str ,       _lowerCamelCase				:   Any    ):



					"""simple docstring"""

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

					A_					:			Optional[Any]						=						integrate.quad(_lowerCamelCase ,       state.sigmas[t] ,       state.sigmas[t + 1] ,       epsrel=1E-4    )[0]

					return integrated_coeff







				def    a_					(							self				:   List[str] ,       _lowerCamelCase				:   LMSDiscreteSchedulerState ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   Tuple = ()    ):



					"""simple docstring"""

					A_					:			List[Any]						=						jnp.linspace(self.config.num_train_timesteps - 1 ,       0 ,       _lowerCamelCase ,       dtype=self.dtype    )

					A_					:			Dict						=						jnp.floor(_lowerCamelCase    ).astype(jnp.intaa    )
					A_					:			Optional[Any]						=						jnp.ceil(_lowerCamelCase    ).astype(jnp.intaa    )

					A_					:			Union[str, Any]						=						jnp.mod(_lowerCamelCase ,       1.0    )

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

					A_					:			List[str]						=						timesteps.astype(jnp.intaa    )

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

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







				def    a_					(							self				:   List[Any] ,       _lowerCamelCase				:   LMSDiscreteSchedulerState ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   int ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   int = 4 ,       _lowerCamelCase				:   bool = 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'''    )

					A_					:			Union[str, Any]						=						state.sigmas[timestep]

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

					# 3. Compute linear multistep coefficients
					A_					:			Optional[Any]						=						min(timestep + 1 ,       _lowerCamelCase    )
					A_					:			int						=						[self.get_lms_coefficient(_lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase ,       _lowerCamelCase    ) for curr_order in range(_lowerCamelCase    )]

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

					if not return_dict:
						return (prev_sample, state)

					return FlaxLMSSchedulerOutput(prev_sample=_lowerCamelCase ,       state=_lowerCamelCase    )







				def    a_					(							self				:   List[str] ,       _lowerCamelCase				:   LMSDiscreteSchedulerState ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   jnp.ndarray ,       _lowerCamelCase				:   jnp.ndarray ,       ):



					"""simple docstring"""

					A_					:			Optional[Any]						=						state.sigmas[timesteps].flatten()
					A_					:			List[Any]						=						broadcast_to_shape_from_left(_lowerCamelCase ,       noise.shape    )

					A_					:			List[str]						=						original_samples + noise * sigma

					return noisy_samples







				def __len__(							self				:   int    ):



					"""simple docstring"""

					return self.config.num_train_timesteps











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase					:     List[Any]				       =    logging.get_logger(__name__)

_lowerCamelCase					:     Dict				       =    {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class     lowercase (      __UpperCAmelCase):
				__lowerCAmelCase      :      List[str]					    =				"""plbart"""
				__lowerCAmelCase      :      int					    =				["""past_key_values"""]
				__lowerCAmelCase      :      Union[str, Any]					    =				{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







				def __init__(							self				:   Any ,       _lowerCamelCase				:   Any=5_00_05 ,       _lowerCamelCase				:   List[str]=10_24 ,       _lowerCamelCase				:   Union[str, Any]=6 ,       _lowerCamelCase				:   Any=30_72 ,       _lowerCamelCase				:   List[str]=12 ,       _lowerCamelCase				:   Tuple=6 ,       _lowerCamelCase				:   Dict=30_72 ,       _lowerCamelCase				:   Optional[int]=12 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Optional[int]=0.0 ,       _lowerCamelCase				:   Tuple=True ,       _lowerCamelCase				:   Optional[int]=True ,       _lowerCamelCase				:   str="gelu" ,       _lowerCamelCase				:   int=7_68 ,       _lowerCamelCase				:   Union[str, Any]=0.1 ,       _lowerCamelCase				:   Optional[Any]=0.1 ,       _lowerCamelCase				:   Tuple=0.0 ,       _lowerCamelCase				:   Optional[int]=0.02 ,       _lowerCamelCase				:   Dict=0.0 ,       _lowerCamelCase				:   Dict=True ,       _lowerCamelCase				:   List[str]=1 ,       _lowerCamelCase				:   str=0 ,       _lowerCamelCase				:   int=2 ,       _lowerCamelCase				:   str=2 ,       **_lowerCamelCase				:   Dict ,       ):



					"""simple docstring"""

					A_					:			int						=						vocab_size
					A_					:			Tuple						=						max_position_embeddings
					A_					:			Optional[Any]						=						d_model
					A_					:			Optional[int]						=						encoder_ffn_dim
					A_					:			Optional[int]						=						encoder_layers
					A_					:			List[str]						=						encoder_attention_heads
					A_					:			Dict						=						decoder_ffn_dim
					A_					:			List[str]						=						decoder_layers
					A_					:			str						=						decoder_attention_heads
					A_					:			List[Any]						=						dropout
					A_					:			int						=						attention_dropout
					A_					:			Dict						=						activation_dropout
					A_					:			int						=						activation_function
					A_					:			List[str]						=						init_std
					A_					:			int						=						encoder_layerdrop
					A_					:			int						=						decoder_layerdrop
					A_					:			Optional[int]						=						classifier_dropout
					A_					:			Optional[int]						=						use_cache
					A_					:			Dict						=						encoder_layers
					A_					:			int						=						scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=_lowerCamelCase ,       bos_token_id=_lowerCamelCase ,       eos_token_id=_lowerCamelCase ,       is_encoder_decoder=_lowerCamelCase ,       forced_eos_token_id=_lowerCamelCase ,       **_lowerCamelCase ,       )

class     lowercase (      __UpperCAmelCase):







				@property
				def    a_					(							self				:   Any    ):



					"""simple docstring"""

					return OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
					    ]    )







				@property
				def    a_					(							self				:   Dict    ):



					"""simple docstring"""

					if self.use_past:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''past_keys''', {0: '''batch''', 2: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )
					else:
						return OrderedDict(
						    [
						        ('''last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						        ('''encoder_last_hidden_state''', {0: '''batch''', 1: '''sequence'''}),
						    ]    )





