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_		:
   __lowerCAmelCase											=							42

   # setable values
   __lowerCAmelCase											=							42
   __lowerCAmelCase											=							42
   __lowerCAmelCase											=							42
   __lowerCAmelCase											=							None

   # running values
   __lowerCAmelCase											=							None







   @classmethod
   def 	lowerCamelCase_  (    cls	:	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 SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							42





class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    __lowerCAmelCase		):
   __lowerCAmelCase											=							[e.name for e in FlaxKarrasDiffusionSchedulers]

   __lowerCAmelCase											=							42







   @property
   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    return True







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





    """simple docstring"""


    UpperCamelCase      =			dtype







   def 	lowerCamelCase_  (    self	:	List[Any]      ,      lowerCamelCase_	:	Optional[CommonSchedulerState] = None							):





    """simple docstring"""


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

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

    # standard deviation of the initial noise distribution
    UpperCamelCase      =			sigmas.max()

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







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	LMSDiscreteSchedulerState      ,      lowerCamelCase_	:	jnp.ndarray      ,      lowerCamelCase_	:	int							):





    """simple docstring"""


    ((UpperCamelCase)     ,    )      =			jnp.where(state.timesteps == timestep      ,      size=1							)
    UpperCamelCase      =			step_index[0]

    UpperCamelCase      =			state.sigmas[step_index]
    UpperCamelCase      =			sample / ((sigma**2 + 1) ** 0.5)
    return sample







   def 	lowerCamelCase_  (    self	:	Tuple      ,      lowerCamelCase_	:	LMSDiscreteSchedulerState      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	Any							):





    """simple docstring"""


    def lms_derivative(lowerCamelCase_	:	Optional[int]							):
     UpperCamelCase      =			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

    UpperCamelCase      =			integrate.quad(lowerCamelCase_      ,      state.sigmas[t]      ,      state.sigmas[t + 1]      ,      epsrel=1E-4							)[0]

    return integrated_coeff







   def 	lowerCamelCase_  (    self	:	Tuple      ,      lowerCamelCase_	:	LMSDiscreteSchedulerState      ,      lowerCamelCase_	:	int      ,      lowerCamelCase_	:	Tuple = ()							):





    """simple docstring"""


    UpperCamelCase      =			jnp.linspace(self.config.num_train_timesteps - 1      ,      0      ,      lowerCamelCase_      ,      dtype=self.dtype							)

    UpperCamelCase      =			jnp.floor(lowerCamelCase_							).astype(jnp.intaa							)
    UpperCamelCase      =			jnp.ceil(lowerCamelCase_							).astype(jnp.intaa							)

    UpperCamelCase      =			jnp.mod(lowerCamelCase_      ,      1.0							)

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

    UpperCamelCase      =			timesteps.astype(jnp.intaa							)

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

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







   def 	lowerCamelCase_  (    self	:	Union[str, 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"""							)

    UpperCamelCase      =			state.sigmas[timestep]

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

    # 3. Compute linear multistep coefficients
    UpperCamelCase      =			min(timestep + 1      ,      lowerCamelCase_							)
    UpperCamelCase      =			[self.get_lms_coefficient(lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_      ,      lowerCamelCase_							) for curr_order in range(lowerCamelCase_							)]

    # 4. Compute previous sample based on the derivatives path
    UpperCamelCase      =			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 	lowerCamelCase_  (    self	:	Union[str, Any]      ,      lowerCamelCase_	:	LMSDiscreteSchedulerState      ,      lowerCamelCase_	:	jnp.ndarray      ,      lowerCamelCase_	:	jnp.ndarray      ,      lowerCamelCase_	:	jnp.ndarray      ,      ):





    """simple docstring"""


    UpperCamelCase      =			state.sigmas[timesteps].flatten()
    UpperCamelCase      =			broadcast_to_shape_from_left(lowerCamelCase_      ,      noise.shape							)

    UpperCamelCase      =			original_samples + noise * sigma

    return noisy_samples







   def __len__(    self	:	List[Any]							):





    """simple docstring"""


    return self.config.num_train_timesteps

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   		=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   		=			{
    """tau/splinter-base""": """https://huggingface.co/tau/splinter-base/resolve/main/config.json""",
    """tau/splinter-base-qass""": """https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json""",
    """tau/splinter-large""": """https://huggingface.co/tau/splinter-large/resolve/main/config.json""",
    """tau/splinter-large-qass""": """https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json""",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase		):
   __lowerCAmelCase											=							"""splinter"""







   def __init__(    self	:	List[Any]      ,      lowerCamelCase_	:	int=3_0522      ,      lowerCamelCase_	:	Any=768      ,      lowerCamelCase_	:	List[Any]=12      ,      lowerCamelCase_	:	Tuple=12      ,      lowerCamelCase_	:	Tuple=3072      ,      lowerCamelCase_	:	Any="gelu"      ,      lowerCamelCase_	:	Union[str, Any]=0.1      ,      lowerCamelCase_	:	str=0.1      ,      lowerCamelCase_	:	int=512      ,      lowerCamelCase_	:	List[Any]=2      ,      lowerCamelCase_	:	List[Any]=0.0_2      ,      lowerCamelCase_	:	Dict=1E-12      ,      lowerCamelCase_	:	Tuple=True      ,      lowerCamelCase_	:	str=0      ,      lowerCamelCase_	:	Any=104      ,      **lowerCamelCase_	:	Dict      ,      ):





    """simple docstring"""


    super().__init__(pad_token_id=lowerCamelCase_      ,      **lowerCamelCase_							)

    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			max_position_embeddings
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			hidden_act
    UpperCamelCase      =			hidden_dropout_prob
    UpperCamelCase      =			attention_probs_dropout_prob
    UpperCamelCase      =			initializer_range
    UpperCamelCase      =			type_vocab_size
    UpperCamelCase      =			layer_norm_eps
    UpperCamelCase      =			use_cache
    UpperCamelCase      =			question_token_id
