






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__      :
		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							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				List[str]:
				return cls(common=_A							, init_noise_sigma=_A							, timesteps=_A							, sigmas=_A						)





@dataclass
class        a__      (	A_		):
		UpperCAmelCase_							:       LMSDiscreteSchedulerState





class        a__      (	A_	,       A_		):
		UpperCAmelCase_							:       str 					= [e.name for e in FlaxKarrasDiffusionSchedulers]

		UpperCAmelCase_							:       jnp.dtype






		@property
		def 					_lowerCamelCase					(      self						)				->				List[str]:
				return True






		@register_to_config
		def __init__(      self							, lowercase__ = 1000							, lowercase__ = 0.0001							, lowercase__ = 0.02							, lowercase__ = "linear"							, lowercase__ = None							, lowercase__ = "epsilon"							, lowercase__ = jnp.floataa							, )				->				List[Any]:
				__A    =							dtype






		def 					_lowerCamelCase					(      self							, lowercase__ = None						)				->				LMSDiscreteSchedulerState:
				if common is None:
						__A    =							CommonSchedulerState.create(self						)

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

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				jnp.ndarray:
				((__A				) ,   				)    =							jnp.where(state.timesteps == timestep							, size=1						)
				__A    =							step_index[0]

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				int:

				def lms_derivative(lowercase__						):
						__A    =							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

				__A    =							integrate.quad(_A							, state.sigmas[t]							, state.sigmas[t + 1]							, epsrel=1e-4						)[0]

				return integrated_coeff






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__ = ()						)				->				LMSDiscreteSchedulerState:
				__A    =							jnp.linspace(self.config.num_train_timesteps - 1							, 0							, _A							, dtype=self.dtype						)

				__A    =							jnp.floor(_A						).astype(jnp.intaa						)
				__A    =							jnp.ceil(_A						).astype(jnp.intaa						)

				__A    =							jnp.mod(_A							, 1.0						)

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

				__A    =							timesteps.astype(jnp.intaa						)

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

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






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, lowercase__ = 4							, lowercase__ = 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"						)

				__A    =							state.sigmas[timestep]

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

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

				# 4. Compute previous sample based on the derivatives path
				__A    =							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							, lowercase__							, lowercase__							, lowercase__							, lowercase__							, )				->				jnp.ndarray:
				__A    =							state.sigmas[timesteps].flatten()
				__A    =							broadcast_to_shape_from_left(_A							, noise.shape						)

				__A    =							original_samples + noise * sigma

				return noisy_samples






		def __len__(      self						)				->				str:
				return self.config.num_train_timesteps



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


_A       =					logging.get_logger(__name__)

_A       =					{
    "openbmb/cpm-ant-10b": "https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}







class 	lowerCamelCase						(    A_    ):
     UpperCAmelCase__							:				List[Any] 	=						"cpmant"





     def __init__(self :       List[Any]							,       _A :       int = 3_0_7_2_0							,       _A :       int = 4_0_9_6							,       _A :       int = 3_2							,       _A :       int = 1_2_8							,       _A :       int = 1_0_2_4_0							,       _A :       int = 4_8							,       _A :       int = 0.0							,       _A :       int = 5_1_2							,       _A :       int = 2_0_4_8							,       _A :       int = 1E-6							,       _A :       float = 1.0							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       int = 3_2							,       _A :       bool = True							,       **_A :       Dict							,       )		->		Dict:
         super().__init__(**_A  )
         snake_case						=   prompt_types
         snake_case						=   prompt_length
         snake_case						=   segment_types
         snake_case						=   hidden_size
         snake_case						=   num_attention_heads
         snake_case						=   dim_head
         snake_case						=   dim_ff
         snake_case						=   num_hidden_layers
         snake_case						=   position_bias_num_buckets
         snake_case						=   position_bias_max_distance
         snake_case						=   dropout_p
         snake_case						=   eps
         snake_case						=   use_cache
         snake_case						=   vocab_size
         snake_case						=   init_std

